commit d4919a8c6ad9b9ffe4d2abcc5fe89798a6d043c1 Author: Joanna-HE Date: Thu Jan 18 08:54:06 2024 -0500 point-lio with grid map diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100755 index 0000000..3055c75 --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,86 @@ +cmake_minimum_required(VERSION 2.8.3) +project(point_lio) + +SET(CMAKE_BUILD_TYPE "Debug") + +ADD_COMPILE_OPTIONS(-std=c++14 ) +ADD_COMPILE_OPTIONS(-std=c++14 ) +set( CMAKE_CXX_FLAGS "-std=c++14 -O3" ) + +add_definitions(-DROOT_DIR=\"${CMAKE_CURRENT_SOURCE_DIR}/\") + +set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fexceptions" ) +set(CMAKE_CXX_STANDARD 14) +set(CMAKE_CXX_STANDARD_REQUIRED ON) +set(CMAKE_CXX_EXTENSIONS OFF) +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -pthread -std=c++0x -std=c++14 -fexceptions") + +message("Current CPU archtecture: ${CMAKE_SYSTEM_PROCESSOR}") +if(CMAKE_SYSTEM_PROCESSOR MATCHES "(x86)|(X86)|(amd64)|(AMD64)" ) + include(ProcessorCount) + ProcessorCount(N) + message("Processer number: ${N}") + if(N GREATER 5) + add_definitions(-DMP_EN) + add_definitions(-DMP_PROC_NUM=4) + message("core for MP: 3") + elseif(N GREATER 3) + math(EXPR PROC_NUM "${N} - 2") + add_definitions(-DMP_EN) + add_definitions(-DMP_PROC_NUM="${PROC_NUM}") + message("core for MP: ${PROC_NUM}") + else() + add_definitions(-DMP_PROC_NUM=1) + endif() +else() + add_definitions(-DMP_PROC_NUM=1) +endif() + +find_package(OpenMP QUIET) +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") +set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") + +find_package(PythonLibs REQUIRED) +find_path(MATPLOTLIB_CPP_INCLUDE_DIRS "matplotlibcpp.h") + +find_package(catkin REQUIRED COMPONENTS + geometry_msgs + nav_msgs + sensor_msgs + roscpp + rospy + std_msgs + pcl_ros + tf + livox_ros_driver + message_generation + eigen_conversions +) + +find_package(Eigen3 REQUIRED) +find_package(PCL 1.8 REQUIRED) + + +message(Eigen: ${EIGEN3_INCLUDE_DIR}) + +include_directories( + ${catkin_INCLUDE_DIRS} + ${EIGEN3_INCLUDE_DIR} + ${PCL_INCLUDE_DIRS} + ${PYTHON_INCLUDE_DIRS} + include) + +link_directories( + include + ${PCL_LIBRARY_DIRS} +) + +add_executable(pointlio_mapping src/laserMapping.cpp + src/li_initialization.cpp src/parameters.cpp src/preprocess.cpp src/Estimator.cpp + src/IMU_Processing.cpp) +target_link_libraries(pointlio_mapping ${catkin_LIBRARIES} ${PCL_LIBRARIES} ${PYTHON_LIBRARIES}) + +target_include_directories(pointlio_mapping PRIVATE ${PYTHON_INCLUDE_DIRS}) + + + diff --git a/LICENSE b/LICENSE new file mode 100755 index 0000000..399a50f --- /dev/null +++ b/LICENSE @@ -0,0 +1,34 @@ +This is an advanced implementation of the algorithm described in the following paper: + J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time. + Robotics: Science and Systems Conference (RSS). Berkeley, CA, July 2014. + +Modifier: livox dev@livoxtech.com + +Copyright 2013, Ji Zhang, Carnegie Mellon University +Further contributions copyright (c) 2016, Southwest Research Institute +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. +3. Neither the name of the copyright holder nor the names of its contributors + may be used to endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,OR +TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF +THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Log/IMU_before_filter.txt b/Log/IMU_before_filter.txt new file mode 100644 index 0000000..e69de29 diff --git a/Log/IMU_meas.txt b/Log/IMU_meas.txt new file mode 100644 index 0000000..e69de29 diff --git a/Log/LiDAR_meas.txt b/Log/LiDAR_meas.txt new file mode 100644 index 0000000..e69de29 diff --git a/Log/Lidar_before_filter.txt b/Log/Lidar_before_filter.txt new file mode 100644 index 0000000..e69de29 diff --git a/Log/Lidar_omg_after_rot.txt b/Log/Lidar_omg_after_rot.txt new file mode 100644 index 0000000..e69de29 diff --git a/Log/acc_cost.txt b/Log/acc_cost.txt new file mode 100644 index 0000000..e69de29 diff --git a/Log/guide.md b/Log/guide.md new file mode 100755 index 0000000..883b3d4 --- /dev/null +++ b/Log/guide.md @@ -0,0 +1 @@ +Here saved the debug records which can be drew by the ../log/plot.py. The record function can be found frm the MACRO: DEBUG_FILE_DIR(name) in common_lib.h. diff --git a/Log/imu.txt b/Log/imu.txt new file mode 100755 index 0000000..e69de29 diff --git a/Log/imu_pbp.txt b/Log/imu_pbp.txt new file mode 100644 index 0000000..e69de29 diff --git a/Log/mat_out.txt b/Log/mat_out.txt new file mode 100644 index 0000000..e69de29 diff --git a/Log/plot.py b/Log/plot.py new file mode 100755 index 0000000..b5d1c1a --- /dev/null +++ b/Log/plot.py @@ -0,0 +1,46 @@ +# import matplotlib +# matplotlib.use('Agg') +import numpy as np +import matplotlib.pyplot as plt + +a_out=np.loadtxt('mat_out.txt') +#######for normal####### +fig, axs = plt.subplots(3,2) +lab_out = ['', 'out-x', 'out-y', 'out-z'] +plot_ind = range(7,10) +time=a_out[:,0] +axs[0,0].set_title('Attitude') +axs[1,0].set_title('Translation') +axs[2,0].set_title('Velocity') +axs[0,1].set_title('bg') +axs[1,1].set_title('ba') +axs[2,1].set_title('Gravity') +for i in range(1,4): + for j in range(6): + axs[j%3, j//3].plot(time, a_out[:,i+j*3],'.-', label=lab_out[i]) + for j in range(6): + axs[j%3, j//3].grid() + axs[j%3, j//3].legend() +plt.grid() + #######for normal####### + + +#### Draw IMU data +#fig, axs = plt.subplots(2) +#imu=np.loadtxt('imu_pbp.txt') +#time=imu[:,0] +#axs[0].set_title('Gyroscope') +#axs[1].set_title('Accelerameter') +#lab_1 = ['gyr-x', 'gyr-y', 'gyr-z'] +#lab_2 = ['acc-x', 'acc-y', 'acc-z'] +#for i in range(3): + #if i==1: +# axs[0].plot(time, imu[:,i+1],'.-', label=lab_1[i]) +# axs[1].plot(time, imu[:,i+4],'.-', label=lab_2[i]) +#for i in range(2): + #axs[i].set_xlim(386,389) +# axs[i].grid() +# axs[i].legend() +#plt.grid() + +plt.show() diff --git a/Log/plot_imu.py b/Log/plot_imu.py new file mode 100755 index 0000000..0177273 --- /dev/null +++ b/Log/plot_imu.py @@ -0,0 +1,125 @@ +# import matplotlib +# matplotlib.use('Agg') +import numpy as np +import matplotlib.pyplot as plt + +#### Draw IMU data +fig, axs = plt.subplots(2) +imu=np.loadtxt('imu_pbp.txt') +time=imu[:,0] +axs[0].set_title('Gyroscope') +axs[1].set_title('Accelerameter') +lab_1 = ['gyr-x', 'gyr-y', 'gyr-z'] +lab_2 = ['acc-x', 'acc-y', 'acc-z'] +for i in range(3): + #if i==1: + axs[0].plot(time, imu[:,i+1],'.-', label=lab_1[i]) + axs[1].plot(time, imu[:,i+4],'.-', label=lab_2[i]) +for i in range(2): + #axs[i].set_xlim(386,389) + axs[i].grid() + axs[i].legend() +plt.grid() + +#fig, axs = plt.subplots(5) +#axs[0].set_title('miss') +#axs[1].set_title('miss') +#axs[2].set_title('miss') +#axs[3].set_title('miss') +#axs[4].set_title('miss') +#len_time1 = np.arange(0,1977) +#len_time2 = np.arange(1977, 3954) +#len_time3 = np.arange(3954,5931) +#len_time4 = np.arange(5931,7908) +#len_time5 = np.arange(7908,9885) + #if i==1: +#axs[0].plot(len_time1, time[0:1977],'.-', label='check') +#axs[1].plot(len_time2, time[1977:3954],'.-', label='check') +#axs[2].plot(len_time3, time[3954:5931],'.-', label='check') +#axs[3].plot(len_time4, time[5931:7908],'.-', label='check') +#axs[4].plot(len_time5, time[7908:9885],'.-', label='check') + #axs[i].set_xlim(386,389) +#axs[0].grid() +#axs[0].legend() +#axs[1].grid() +#axs[1].legend() +#axs[2].grid() +#axs[2].legend() +#axs[3].grid() +#axs[3].legend() +#axs[4].grid() +#axs[4].legend() +#plt.grid() + +#fig, axs = plt.subplots(5) +#axs[0].set_title('miss') +#axs[1].set_title('miss') +#axs[2].set_title('miss') +#axs[3].set_title('miss') +#axs[4].set_title('miss') +#len_time1 = np.arange(9885,9885+1977) +#len_time2 = np.arange(9885+1977,9885+3954) +#len_time3 = np.arange(9885+3954,9885+5931) +#len_time4 = np.arange(9885+5931,9885+7908) +#len_time5 = np.arange(9885+7908,9885+9885) + #if i==1: +#axs[0].plot(len_time1, time[9885+0:9885+1977],'.-', label='check') +#axs[1].plot(len_time2, time[9885+1977:9885+3954],'.-', label='check') +#axs[2].plot(len_time3, time[9885+3954:9885+5931],'.-', label='check') +#axs[3].plot(len_time4, time[9885+5931:9885+7908],'.-', label='check') +#axs[4].plot(len_time5, time[9885+7908:9885+9885],'.-', label='check') + #axs[i].set_xlim(386,389) +#axs[0].grid() +#axs[0].legend() +#axs[1].grid() +#axs[1].legend() +#axs[2].grid() +#axs[2].legend() +#axs[3].grid() +#axs[3].legend() +#axs[4].grid() +#axs[4].legend() +#plt.grid() + +# #### Draw time calculation +# plt.figure(3) +# fig = plt.figure() +# font1 = {'family' : 'Times New Roman', +# 'weight' : 'normal', +# 'size' : 12, +# } +# c="red" +# a_out1=np.loadtxt('Log/mat_out_time_indoor1.txt') +# a_out2=np.loadtxt('Log/mat_out_time_indoor2.txt') +# a_out3=np.loadtxt('Log/mat_out_time_outdoor.txt') +# # n = a_out[:,1].size +# # time_mean = a_out[:,1].mean() +# # time_se = a_out[:,1].std() / np.sqrt(n) +# # time_err = a_out[:,1] - time_mean +# # feat_mean = a_out[:,2].mean() +# # feat_err = a_out[:,2] - feat_mean +# # feat_se = a_out[:,2].std() / np.sqrt(n) +# ax1 = fig.add_subplot(111) +# ax1.set_ylabel('Effective Feature Numbers',font1) +# ax1.boxplot(a_out1[:,2], showfliers=False, positions=[0.9]) +# ax1.boxplot(a_out2[:,2], showfliers=False, positions=[1.9]) +# ax1.boxplot(a_out3[:,2], showfliers=False, positions=[2.9]) +# ax1.set_ylim([0, 3000]) + +# ax2 = ax1.twinx() +# ax2.spines['right'].set_color('red') +# ax2.set_ylabel('Compute Time (ms)',font1) +# ax2.yaxis.label.set_color('red') +# ax2.tick_params(axis='y', colors='red') +# ax2.boxplot(a_out1[:,1]*1000, showfliers=False, positions=[1.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c)) +# ax2.boxplot(a_out2[:,1]*1000, showfliers=False, positions=[2.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c)) +# ax2.boxplot(a_out3[:,1]*1000, showfliers=False, positions=[3.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c)) +# ax2.set_xlim([0.5, 3.5]) +# ax2.set_ylim([0, 100]) + +# plt.xticks([1,2,3], ('Outdoor Scene', 'Indoor Scene 1', 'Indoor Scene 2')) +# # # print(time_se) +# # # print(a_out3[:,2]) +# plt.grid() +# plt.savefig("time.pdf", dpi=1200) +plt.show() diff --git a/Log/plot_out.py b/Log/plot_out.py new file mode 100755 index 0000000..8dd80cb --- /dev/null +++ b/Log/plot_out.py @@ -0,0 +1,178 @@ +# import matplotlib +# matplotlib.use('Agg') +import numpy as np +import matplotlib.pyplot as plt + +# a_pre=np.loadtxt('mat_pre.txt') +a_out=np.loadtxt('mat_out.txt') +if((a_out.shape[1] != 19) & (a_out.shape[1] != 20)): + ######for ikfom + fig, axs = plt.subplots(4,2) + #lab_pre = ['', 'pre-x', 'pre-y', 'pre-z'] + lab_out = ['', 'out-x', 'out-y', 'out-z'] + plot_ind = range(7,10) + time=a_out[:,0] + axs[0,0].set_title('Attitude') + axs[1,0].set_title('Translation') + axs[2,0].set_title('Velocity') + axs[3,0].set_title('Angular velocity') + axs[0,1].set_title('Acceleration') + axs[1,1].set_title('Gravity') + axs[2,1].set_title('bg') + axs[3,1].set_title('ba') + for i in range(1,4): + for j in range(8): + #axs[j%4, j//4].plot(time, a_pre[:,i+j*3],'.-', label=lab_pre[i]) + axs[j%4, j//4].plot(time, a_out[:,i+j*3],'.-', label=lab_out[i]) + for j in range(8): + # axs[j].set_xlim(386,389) + axs[j%4, j//4].grid() + axs[j%4, j//4].legend() + plt.grid() + ######for ikfom####### +else: + #######for normal####### + fig, axs = plt.subplots(3,2) + lab_pre = ['', 'pre-x', 'pre-y', 'pre-z'] + lab_out = ['', 'out-x', 'out-y', 'out-z'] + plot_ind = range(7,10) + time=a_out[:,0] + time1 = a_pre[:,0] + axs[0,0].set_title('Attitude') + axs[1,0].set_title('Translation') + axs[2,0].set_title('Velocity') + axs[0,1].set_title('bg') + axs[1,1].set_title('ba') + axs[2,1].set_title('Gravity') + for i in range(1,4): + for j in range(6): + axs[j%3, j/3].plot(time1, a_pre[:,i+j*3],'.-', label=lab_pre[i]) + axs[j%3, j/3].plot(time, a_out[:,i+j*3],'.-', label=lab_out[i]) + for j in range(6): + # axs[j].set_xlim(386,389) + axs[j%3, j//3].grid() + axs[j%3, j//3].legend() + plt.grid() + #######for normal####### + + +#### Draw IMU data +fig, axs = plt.subplots(2) +imu=np.loadtxt('imu_pbp.txt') +time=imu[:,0] +axs[0].set_title('Gyroscope') +axs[1].set_title('Accelerameter') +lab_1 = ['gyr-x', 'gyr-y', 'gyr-z'] +lab_2 = ['acc-x', 'acc-y', 'acc-z'] +for i in range(3): + #if i==1: + axs[0].plot(time, imu[:,i+1],'.-', label=lab_1[i]) + axs[1].plot(time, imu[:,i+4],'.-', label=lab_2[i]) +for i in range(2): + #axs[i].set_xlim(386,389) + axs[i].grid() + axs[i].legend() +plt.grid() + +#fig, axs = plt.subplots(5) +#axs[0].set_title('miss') +#axs[1].set_title('miss') +#axs[2].set_title('miss') +#axs[3].set_title('miss') +#axs[4].set_title('miss') +#len_time1 = np.arange(0,1977) +#len_time2 = np.arange(1977, 3954) +#len_time3 = np.arange(3954,5931) +#len_time4 = np.arange(5931,7908) +#len_time5 = np.arange(7908,9885) + #if i==1: +#axs[0].plot(len_time1, time[0:1977],'.-', label='check') +#axs[1].plot(len_time2, time[1977:3954],'.-', label='check') +#axs[2].plot(len_time3, time[3954:5931],'.-', label='check') +#axs[3].plot(len_time4, time[5931:7908],'.-', label='check') +#axs[4].plot(len_time5, time[7908:9885],'.-', label='check') + #axs[i].set_xlim(386,389) +#axs[0].grid() +#axs[0].legend() +#axs[1].grid() +#axs[1].legend() +#axs[2].grid() +#axs[2].legend() +#axs[3].grid() +#axs[3].legend() +#axs[4].grid() +#axs[4].legend() +#plt.grid() + +#fig, axs = plt.subplots(5) +#axs[0].set_title('miss') +#axs[1].set_title('miss') +#axs[2].set_title('miss') +#axs[3].set_title('miss') +#axs[4].set_title('miss') +#len_time1 = np.arange(9885,9885+1977) +#len_time2 = np.arange(9885+1977,9885+3954) +#len_time3 = np.arange(9885+3954,9885+5931) +#len_time4 = np.arange(9885+5931,9885+7908) +#len_time5 = np.arange(9885+7908,9885+9885) + #if i==1: +#axs[0].plot(len_time1, time[9885+0:9885+1977],'.-', label='check') +#axs[1].plot(len_time2, time[9885+1977:9885+3954],'.-', label='check') +#axs[2].plot(len_time3, time[9885+3954:9885+5931],'.-', label='check') +#axs[3].plot(len_time4, time[9885+5931:9885+7908],'.-', label='check') +#axs[4].plot(len_time5, time[9885+7908:9885+9885],'.-', label='check') + #axs[i].set_xlim(386,389) +#axs[0].grid() +#axs[0].legend() +#axs[1].grid() +#axs[1].legend() +#axs[2].grid() +#axs[2].legend() +#axs[3].grid() +#axs[3].legend() +#axs[4].grid() +#axs[4].legend() +#plt.grid() + +# #### Draw time calculation +# plt.figure(3) +# fig = plt.figure() +# font1 = {'family' : 'Times New Roman', +# 'weight' : 'normal', +# 'size' : 12, +# } +# c="red" +# a_out1=np.loadtxt('Log/mat_out_time_indoor1.txt') +# a_out2=np.loadtxt('Log/mat_out_time_indoor2.txt') +# a_out3=np.loadtxt('Log/mat_out_time_outdoor.txt') +# # n = a_out[:,1].size +# # time_mean = a_out[:,1].mean() +# # time_se = a_out[:,1].std() / np.sqrt(n) +# # time_err = a_out[:,1] - time_mean +# # feat_mean = a_out[:,2].mean() +# # feat_err = a_out[:,2] - feat_mean +# # feat_se = a_out[:,2].std() / np.sqrt(n) +# ax1 = fig.add_subplot(111) +# ax1.set_ylabel('Effective Feature Numbers',font1) +# ax1.boxplot(a_out1[:,2], showfliers=False, positions=[0.9]) +# ax1.boxplot(a_out2[:,2], showfliers=False, positions=[1.9]) +# ax1.boxplot(a_out3[:,2], showfliers=False, positions=[2.9]) +# ax1.set_ylim([0, 3000]) + +# ax2 = ax1.twinx() +# ax2.spines['right'].set_color('red') +# ax2.set_ylabel('Compute Time (ms)',font1) +# ax2.yaxis.label.set_color('red') +# ax2.tick_params(axis='y', colors='red') +# ax2.boxplot(a_out1[:,1]*1000, showfliers=False, positions=[1.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c)) +# ax2.boxplot(a_out2[:,1]*1000, showfliers=False, positions=[2.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c)) +# ax2.boxplot(a_out3[:,1]*1000, showfliers=False, positions=[3.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c)) +# ax2.set_xlim([0.5, 3.5]) +# ax2.set_ylim([0, 100]) + +# plt.xticks([1,2,3], ('Outdoor Scene', 'Indoor Scene 1', 'Indoor Scene 2')) +# # # print(time_se) +# # # print(a_out3[:,2]) +# plt.grid() +# plt.savefig("time.pdf", dpi=1200) +plt.show() diff --git a/Log/plot_rtk.py b/Log/plot_rtk.py new file mode 100755 index 0000000..a08e122 --- /dev/null +++ b/Log/plot_rtk.py @@ -0,0 +1,38 @@ +import numpy as np +import matplotlib.pyplot as plt + +a_grdt=np.loadtxt('pos_rtk.txt') +a_out=np.loadtxt('mat_out.txt') +#######for normal####### +fig, axs = plt.subplots(3,1) +lab_grdt = ['', 'gt-x', 'gt-y', 'gt-z'] +lab_out = ['', 'out-x', 'out-y', 'out-z'] +plot_ind = range(7,10) +time=a_out[:,0] +time1 = a_grdt[:,0] +axs[0].set_title('East [m]') +axs[1].set_title('North [m]') +axs[2].set_title('Up [m]') +for i in range(1,4): + axs[i-1].plot(time1, a_grdt[:,i],'.-', label=lab_grdt[i]) + axs[i-1].plot(time, a_out[:,i+3],'.-', label=lab_out[i]) +for j in range(1,4): + axs[j-1].grid() + axs[j-1].legend() +plt.grid() +#######for normal####### + +fig, axs = plt.subplots(2,1) +axs[0].set_title('clock drift') +lab_1 = ['dt-g', 'dt-r', 'dt-e', 'dt-c'] +for i in range(3): + #if i==1: + axs[0].plot(time, a_out[:,i+13],'.-', label=lab_1[i]) +axs[0].plot(time, a_out[:,19],'.-', label=lab_1[3]) +for i in range(1): + #axs[i].set_xlim(386,389) + axs[i].grid() + axs[i].legend() +plt.grid() + +plt.show() diff --git a/Log/pos_log.txt b/Log/pos_log.txt new file mode 100644 index 0000000..e69de29 diff --git a/Log/pos_rtk.txt b/Log/pos_rtk.txt new file mode 100644 index 0000000..74e0dbd --- /dev/null +++ b/Log/pos_rtk.txt @@ -0,0 +1,13978 @@ + 0 0 0 0 + 0.0999999 0.0102959 -0.0221475 -0.048 + 0.2 0.0308878 -0.0332213 -0.064 + 0.3 0.0308878 -0.0221475 -0.079 + 0.4 0.0617756 -0.0332213 -0.134 + 0.5 0.0617756 -0.0664426 -0.1 + 0.6 0.0720715 -0.0775163 -0.11 + 0.7 0.0617756 -0.0775163 -0.079 + 0.8 0.0823674 -0.0996639 -0.076 + 0.9 0.0823674 -0.0996639 -0.139 + 1 0.0926633 -0.0996639 -0.136 + 1.1 0.0926633 -0.0996639 -0.135 + 1.2 0.0823674 -0.0996639 -0.136 + 1.3 0.0823674 -0.110738 -0.155 + 1.4 0.0720715 -0.121811 -0.118 + 1.5 0.0617756 -0.121811 -0.109 + 1.6 0.0720715 -0.132885 -0.112 + 1.7 0.0720715 -0.166106 -0.065 + 1.8 0.0411837 -0.166106 -0.023 + 1.9 0.0617756 -0.166106 -0.054 + 2 0.0720715 -0.188254 -0.047 + 2.1 0.0720715 -0.188254 -0.062 + 2.2 0.0823674 -0.199328 -0.07 + 2.3 0.0720715 -0.17718 -0.107 + 2.4 0.0617756 -0.17718 -0.104 + 2.5 0.0926633 -0.188254 -0.146 + 2.6 0.133847 -0.17718 -0.24 + 2.7 0.144143 -0.166106 -0.303 + 2.8 0.144143 -0.166106 -0.262 + 2.9 0.154439 -0.166106 -0.32 + 3 0.185327 -0.199328 -0.301 + 3.1 0.175031 -0.188254 -0.321 + 3.2 0.205919 -0.221475 -0.328 + 3.3 0.195623 -0.221475 -0.302 + 3.4 0.175031 -0.232549 -0.248 + 3.5 0.236806 -0.221475 -0.425 + 3.6 0.22651 -0.232549 -0.373 + 3.7 0.247102 -0.254697 -0.364 + 3.8 0.257398 -0.243623 -0.404 + 3.9 0.267694 -0.26577 -0.397 + 4 0.267694 -0.254697 -0.473 + 4.1 0.308878 -0.276844 -0.482 + 4.2 0.308878 -0.26577 -0.523 + 4.3 0.350061 -0.26577 -0.604 + 4.4 0.360357 -0.276844 -0.634 + 4.5 0.370653 -0.254697 -0.691 + 4.6 0.463317 -0.254696 -0.866 + 4.7 0.5045 -0.232549 -0.957 + 4.8 0.5045 -0.210401 -1.049 + 4.9 0.535388 -0.221475 -1.112 + 5 0.576572 -0.210401 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17.9 5.7657 3.4993 -21.194 + 18 5.67304 3.42178 -20.93 + 18.1 5.61126 3.33319 -20.751 + 18.2 5.54949 3.26675 -20.555 + 18.3 5.44653 3.21138 -20.347 + 18.4 5.38475 3.14494 -20.189 + 18.5 5.31268 3.05635 -19.932 + 18.6 5.23031 2.95669 -19.671 + 18.7 5.48771 4.09728 -22.768 + 18.8 5.58037 4.1305 -22.978 + 18.9 5.67303 4.19694 -23.241 + 19 5.7657 4.28553 -23.535 + 19.1 5.86865 4.38519 -23.897 + 19.2 5.94073 4.45164 -24.189 + 19.3 6.03339 4.51808 -24.453 + 19.4 6.12605 4.58452 -24.728 + 19.5 6.19812 4.65096 -24.98 + 19.6 6.30108 4.72848 -25.297 + 19.7 6.38345 4.78385 -25.522 + 19.8 6.45552 4.82814 -25.709 + 19.9 6.53789 4.90566 -26.004 + 20 6.62025 4.96103 -26.24 + 20.1 6.71291 5.01639 -26.475 + 20.2 6.78499 5.08284 -26.697 + 20.3 6.87765 5.13821 -26.976 + 20.4 6.93942 5.19357 -27.165 + 20.5 7.03209 5.24894 -27.437 + 20.6 7.12475 5.30431 -27.691 + 20.7 7.18652 5.35968 -27.908 + 20.8 7.26889 5.4372 -28.143 + 20.9 7.34096 5.50364 -28.403 + 21 7.40274 5.50364 -28.483 + 21.1 7.4851 5.6033 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7.33079 -35.904 + 24.5 9.91492 7.38616 -36.165 + 24.6 9.97669 7.43046 -36.355 + 24.7 10.0488 7.4969 -36.612 + 24.8 10.4812 7.75159 -38.024 + 24.9 10.8827 8.05058 -39.481 + 25 11.1813 8.30527 -40.612 + 25.1 11.3563 8.41601 -41.209 + 25.2 11.5108 8.5046 -41.66 + 25.3 11.6343 8.61533 -42.088 + 25.4 11.7785 8.70392 -42.495 + 25.5 11.9329 8.80359 -43.022 + 25.6 12.0873 8.91432 -43.573 + 25.7 12.3138 9.04721 -44.276 + 25.8 12.4889 9.16902 -44.856 + 25.9 12.6536 9.29083 -45.42 + 26 12.8183 9.39049 -45.933 + 26.1 12.9522 9.45693 -46.307 + 26.2 12.9934 9.49015 -46.415 + 26.3 13.0037 9.49015 -46.39 + 26.4 13.0551 9.49015 -46.466 + 26.5 13.086 9.47908 -46.446 + 26.6 13.1272 9.47908 -46.466 + 26.7 13.1272 9.44586 -46.369 + 26.8 13.0242 9.31297 -45.77 + 26.9 12.8595 9.12472 -44.984 + 27 12.6845 8.94754 -44.165 + 27.1 12.4374 8.73714 -43.11 + 27.2 12.1182 8.44923 -41.792 + 27.3 11.8093 8.12809 -40.336 + 27.4 11.5005 7.82911 -38.96 + 27.5 11.274 7.58549 -37.915 + 27.6 11.0475 7.36401 -36.908 + 27.7 10.8724 7.19791 -36.124 + 27.8 10.7283 7.0318 -35.401 + 27.9 10.5842 6.88785 -34.738 + 28 10.4709 6.69959 -34.012 + 28.1 10.3473 6.54456 -33.381 + 28.2 10.2238 6.40061 -32.717 + 28.3 10.1311 6.31202 -32.34 + 28.4 10.0488 6.13484 -31.729 + 28.5 9.9664 6.03517 -31.281 + 28.6 9.89433 5.99088 -30.994 + 28.7 9.7399 5.82477 -30.296 + 28.8 9.61635 5.75833 -29.811 + 28.9 9.46191 5.6033 -29.123 + 29 9.28688 5.44827 -28.336 + 29.1 9.12215 5.31539 -27.629 + 29.2 8.94712 5.10499 -26.772 + 29.3 8.80298 4.94995 -26.107 + 29.4 8.65884 4.83922 -25.487 + 29.5 8.47351 4.70633 -24.769 + 29.6 8.30878 4.5513 -24.033 + 29.7 8.14405 4.41842 -23.39 + 29.8 8.03079 4.24124 -22.724 + 29.9 7.91754 4.14157 -22.201 + 30 7.82488 4.06406 -21.818 + 30.1 7.7734 4.01976 -21.577 + 30.2 7.70133 3.98654 -21.243 + 30.3 7.55718 3.85366 -20.661 + 30.4 7.42334 3.754 -20.124 + 30.5 7.23801 3.62111 -19.411 + 30.6 7.11446 3.4993 -18.838 + 30.7 6.98062 3.39964 -18.331 + 30.8 6.82618 3.26675 -17.657 + 30.9 6.68204 3.11172 -16.912 + 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Robust High-Bandwidth Lidar-Inertial Odometry + +## 1. Introduction + +
+
+ +
+ The framework and key points of the Point-LIO. +
+ +**Point-LIO** is a robust and high-bandwidth LiDAR-inertial odometry with the capability to estimate extremely aggressive robotic motions. Point-LIO has two key novelties that enable a high-bandwidth LiDAR-inertial odometry (LIO). The first one is a point-by-point LIO framework, where the state is updated at each LiDAR point measurement without accumulating them into a frame. This point-by-point update allows an extremely high-frequency odometry output, significantly increases the odometry bandwidth, and also fundamentally removes the artificial in-frame motion distortion in aggressive motions. The second main novelty is a stochastic process-augmented kinematic model which models the IMU measurements as an output, instead of input as in existing filter-based odometry or SLAM systems, of the model. This new modeling method enables accurate localization and reliable mapping for aggressive motions even when IMU measurements are saturated. In experiments, Point-LIO is able to provide accurate, high-frequency odometry (4-8 kHz) and reliable mapping under severe vibrations and aggressive motions with high angular velocity (75 rad s^{-1}) beyond the IMU measuring ranges. And Point-LIO is computationally efficient, robust, versatile on public datasets with general motions. As an odometry, Point-LIO could be used in various autonomous tasks, such as trajectory planning, control, and perception, especially in cases involving very fast ego-motions (e.g., in the presence of severe vibration and high angular or linear velocity) or requiring high-rate odometry output and mapping (e.g., for high-rate feedback control and perception). + +## **1.1. Developers:** +The codes of this repo are contributed by: +[Dongjiao He (贺东娇)](https://github.com/Joanna-HE) and [Wei Xu (徐威)](https://github.com/XW-HKU) + + +## **1.2. Related paper** +Our paper is accepted to Advanced Intelligent Systems(AIS), and is in the process of production. The preprint version is attached here, [Point-LIO_preprint.pdf](https://github.com/hku-mars/Point-LIO/files/10989136/Point-LIO_preprint.pdf) + + +## **1.3. Related video** +Our accompany video is available on **YouTube**. +
+ +
+ +## 2. What can Point-LIO do? +### 2.1 Simultaneous LiDAR localization and mapping (SLAM) without motion distortion + +### 2.2 Produce high odometry output frequence and high bandwidth + +### 2.3 SLAM with aggressive motions even the IMU is saturated + +# **3. Prerequisites** + +## **3.1 Ubuntu and [ROS](https://www.ros.org/)** +We tested our code on Ubuntu18.04 with ros melodic and Ubuntu20.04 with noetic. Additional ROS package is required: +``` +sudo apt-get install ros-xxx-pcl-conversions +``` + +## **3.2 Eigen** +Following the official [Eigen installation](eigen.tuxfamily.org/index.php?title=Main_Page), or directly install Eigen by: +``` +sudo apt-get install libeigen3-dev +``` + +## **3.3 livox_ros_driver** +Follow [livox_ros_driver Installation](https://github.com/Livox-SDK/livox_ros_driver). + +*Remarks:* +- Since the Point-LIO supports Livox serials LiDAR, so the **livox_ros_driver** must be installed and **sourced** before run any Point-LIO luanch file. +- How to source? The easiest way is add the line ``` source $Licox_ros_driver_dir$/devel/setup.bash ``` to the end of file ``` ~/.bashrc ```, where ``` $Licox_ros_driver_dir$ ``` is the directory of the livox ros driver workspace (should be the ``` ws_livox ``` directory if you completely followed the livox official document). + +## 4. Build +Clone the repository and catkin_make: + +``` + cd ~/$A_ROS_DIR$/src + git clone https://github.com/hku-mars/Point-LIO.git + cd Point-LIO + git submodule update --init + cd ../.. + catkin_make + source devel/setup.bash +``` +- Remember to source the livox_ros_driver before build (follow 3.3 **livox_ros_driver**) +- If you want to use a custom build of PCL, add the following line to ~/.bashrc +```export PCL_ROOT={CUSTOM_PCL_PATH}``` + +## 5. Directly run +Important notes: + +A. Please make sure the IMU and LiDAR are **Synchronized**, that's important. + +B. Please obtain the saturation values of your used IMU (i.e., accelerator and gyroscope), and the units of the accelerator of your used IMU, then modify the .yaml file according to those settings. That's improtant. + +C. The warning message "Failed to find match for field 'time'." means the timestamps of each LiDAR points are missed in the rosbag file. That is important because Point-LIO processes at the sampling time of each LiDAR point. + +D. We recommend to set the **extrinsic_est_en** to false if the extrinsic is give. As for the extrinsic initiallization, please refer to our recent work: [**Robust and Online LiDAR-inertial Initialization**](https://github.com/hku-mars/LiDAR_IMU_Init). + +E. If a high odometry output frequency without downsample is required, set ``` publish_odometry_without_downsample ``` as true. Then the warning message of tf "TF_REPEATED_DATA" will pop up in the terminal window, because the time interval between two publish odometery is too small. The following command could be used to suppress this warning to a smaller frequency: + +in your catkin_ws/src, + +git clone --branch throttle-tf-repeated-data-error git@github.com:BadgerTechnologies/geometry2.git + +Then rebuild, source setup.bash, run and then it should be reduced down to once every 10 seconds. If 10 seconds is still too much log output then change the ros::Duration(10.0) to 10000 seconds or whatever you like. + +### 5.1 For Avia +Connect to your PC to Livox Avia LiDAR by following [Livox-ros-driver installation](https://github.com/Livox-SDK/livox_ros_driver), then +``` + cd ~/$Point_LIO_ROS_DIR$ + source devel/setup.bash + roslaunch point_lio mapping_avia.launch + roslaunch livox_ros_driver livox_lidar_msg.launch +``` +- For livox serials, Point-LIO only support the data collected by the ``` livox_lidar_msg.launch ``` since only its ``` livox_ros_driver/CustomMsg ``` data structure produces the timestamp of each LiDAR point which is very important for Point-LIO. ``` livox_lidar.launch ``` can not produce it right now. +- If you want to change the frame rate, please modify the **publish_freq** parameter in the [livox_lidar_msg.launch](https://github.com/Livox-SDK/livox_ros_driver/blob/master/livox_ros_driver/launch/livox_lidar_msg.launch) of [Livox-ros-driver](https://github.com/Livox-SDK/livox_ros_driver) before make the livox_ros_driver pakage. + +### 5.2 For Livox serials with external IMU + +mapping_avia.launch theratically supports mid-70, mid-40 or other livox serial LiDAR, but need to setup some parameters befor run: + +Edit ``` config/avia.yaml ``` to set the below parameters: + +1. LiDAR point cloud topic name: ``` lid_topic ``` +2. IMU topic name: ``` imu_topic ``` +3. Translational extrinsic: ``` extrinsic_T ``` +4. Rotational extrinsic: ``` extrinsic_R ``` (only support rotation matrix) +- The extrinsic parameters in Point-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. the IMU is the base frame). They can be found in the official manual. +5. Saturation value of IMU's accelerator and gyroscope: ```satu_acc```, ```satu_gyro``` +6. The norm of IMU's acceleration according to unit of acceleration messages: ``` acc_norm ``` + +### 5.3 For Velodyne or Ouster (Velodyne as an example) + +Step A: Setup before run + +Edit ``` config/velodyne.yaml ``` to set the below parameters: + +1. LiDAR point cloud topic name: ``` lid_topic ``` +2. IMU topic name: ``` imu_topic ``` (both internal and external, 6-aixes or 9-axies are fine) +3. Set the parameter ```timestamp_unit``` based on the unit of **time** (Velodyne) or **t** (Ouster) field in PoindCloud2 rostopic +4. Line number (we tested 16, 32 and 64 line, but not tested 128 or above): ``` scan_line ``` +5. Translational extrinsic: ``` extrinsic_T ``` +6. Rotational extrinsic: ``` extrinsic_R ``` (only support rotation matrix) +- The extrinsic parameters in Point-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. the IMU is the base frame). +7. Saturation value of IMU's accelerator and gyroscope: ```satu_acc```, ```satu_gyro``` +8. The norm of IMU's acceleration according to unit of acceleration messages: ``` acc_norm ``` + +Step B: Run below +``` + cd ~/$Point_LIO_ROS_DIR$ + source devel/setup.bash + roslaunch point_lio mapping_velody16.launch +``` + +Step C: Run LiDAR's ros driver or play rosbag. + +### 5.4 PCD file save + +Set ``` pcd_save_enable ``` in launchfile to ``` 1 ```. All the scans (in global frame) will be accumulated and saved to the file ``` Point-LIO/PCD/scans.pcd ``` after the Point-LIO is terminated. ```pcl_viewer scans.pcd``` can visualize the point clouds. + +*Tips for pcl_viewer:* +- change what to visualize/color by pressing keyboard 1,2,3,4,5 when pcl_viewer is running. +``` + 1 is all random + 2 is X values + 3 is Y values + 4 is Z values + 5 is intensity +``` + +# **6. Examples** + +## **6.1. Example-1: SLAM on datasets with aggressive motions where IMU is saturated** +
+ + +
+ +## **6.2. Example-2: Application on FPV and PULSAR** +
+ + +
+ +PULSAR is a self-rotating actuated by only one motor, [PULSAR](https://github.com/hku-mars/PULSAR) + +## 7. Contact us +If you have any questions about this work, please feel free to contact me and Dr. Fu Zhang via email. diff --git a/config/avia.yaml b/config/avia.yaml new file mode 100755 index 0000000..1aa6642 --- /dev/null +++ b/config/avia.yaml @@ -0,0 +1,54 @@ +common: + lid_topic: "/livox/lidar" + imu_topic: "/livox/imu" + con_frame: false # true: if you need to combine several LiDAR frames into one + con_frame_num: 1 # the number of frames combined + cut_frame: false # true: if you need to cut one LiDAR frame into several subframes + cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency + time_diff_lidar_to_imu: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme) + +preprocess: + lidar_type: 1 # 4 + scan_line: 6 # 32 + timestamp_unit: 1 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond. + blind: 0.50 + +mapping: + imu_en: true + extrinsic_est_en: false # for aggressive motion, set this variable false + imu_time_inte: 0.005 # = 1 / frequency of IMU + lidar_time_inte: 0.1 + satu_acc: 3.0 # the saturation value of IMU's acceleration. not related to the units + satu_gyro: 35 # the saturation value of IMU's angular velocity. not related to the units + acc_norm: 1.0 # 9.810 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration + lidar_meas_cov: 0.01 # 0.001; 0.01 + acc_cov_output: 500 + gyr_cov_output: 1000 + b_acc_cov: 0.0001 + b_gyr_cov: 0.0001 + imu_meas_acc_cov: 0.1 #0.1 # 0.1 + imu_meas_omg_cov: 0.1 #0.01 # 0.1 + gyr_cov_input: 0.01 # for IMU as input model + acc_cov_input: 0.1 # for IMU as input model + plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane + match_s: 81 + ivox_grid_resolution: 2.0 + gravity: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # # [0.0, 0.0, -9.787561] # gvins # + gravity_init: [0.0, 0.0, -9.810] # preknown gravity for unstationary start + extrinsic_T: [ 0.04165, 0.02326, -0.0284 ] + extrinsic_R: [ 1, 0, 0, + 0, 1, 0, + 0, 0, 1 ] + +odometry: + publish_odometry_without_downsample: false + +publish: + path_en: true # false: close the path output + scan_publish_en: true # false: close all the point cloud output + scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame + +pcd_save: + pcd_save_en: false + interval: -1 # how many LiDAR frames saved in each pcd file; + # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames. \ No newline at end of file diff --git a/config/horizon.yaml b/config/horizon.yaml new file mode 100755 index 0000000..75041bd --- /dev/null +++ b/config/horizon.yaml @@ -0,0 +1,53 @@ +common: + lid_topic: "/livox/lidar" + imu_topic: "/livox/imu" + con_frame: false # true: if you need to combine several LiDAR frames into one + con_frame_num: 1 # the number of frames combined + cut_frame: false # true: if you need to cut one LiDAR frame into several subframes + cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency + time_diff_lidar_to_imu: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme) + +preprocess: + lidar_type: 1 + scan_line: 6 + timestamp_unit: 1 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond. + blind: 4.0 + +mapping: + imu_en: true + extrinsic_est_en: false # for aggressive motion, set this variable false + imu_time_inte: 0.005 # = 1 / frequency of IMU + lidar_time_inte: 0.1 + satu_acc: 3.0 # the saturation value of IMU's acceleration. not related to the units + satu_gyro: 35 # the saturation value of IMU's angular velocity. not related to the units + acc_norm: 1.0 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration + lidar_meas_cov: 0.01 # 0.001 + acc_cov_output: 500 + gyr_cov_output: 1000 + b_acc_cov: 0.0001 + b_gyr_cov: 0.0001 + imu_meas_acc_cov: 0.01 #0.1 # 2 + imu_meas_omg_cov: 0.01 #0.1 # 2 + gyr_cov_input: 0.01 # for IMU as input model + acc_cov_input: 0.1 # for IMU as input model + plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane + match_s: 81 + ivox_grid_resolution: 2.0 + gravity: [0.0, 0.0, -9.810] # preknown gravity, use when imu_en is false or start from a non-stationary state + extrinsic_T: [ 0.05512, 0.02226, -0.0297 ] + extrinsic_R: [ 1, 0, 0, + 0, 1, 0, + 0, 0, 1 ] + +odometry: + publish_odometry_without_downsample: false + +publish: + path_en: true # false: close the path output + scan_publish_en: true # false: close all the point cloud output + scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame + +pcd_save: + pcd_save_en: false + interval: -1 # how many LiDAR frames saved in each pcd file; + # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames. \ No newline at end of file diff --git a/config/ouster64.yaml b/config/ouster64.yaml new file mode 100755 index 0000000..08192d2 --- /dev/null +++ b/config/ouster64.yaml @@ -0,0 +1,53 @@ +common: + lid_topic: "/os_cloud_node/points" + imu_topic: "/os_cloud_node/imu" + con_frame: false # true: if you need to combine several LiDAR frames into one + con_frame_num: 1 # the number of frames combined + cut_frame: false # true: if you need to cut one LiDAR frame into several subframes + cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency + time_diff_lidar_to_imu: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme) + +preprocess: + lidar_type: 3 # 2 #velodyne # 1 Livox Avia LiDAR + scan_line: 64 # 32 #velodyne 6 avia + timestamp_unit: 3 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond. + blind: 4.0 + +mapping: + imu_en: true + extrinsic_est_en: false # for aggressive motion, set this variable false + imu_time_inte: 0.01 # = 1 / frequency of IMU + lidar_time_inte: 0.1 + satu_acc: 30.0 # the saturation value of IMU's acceleration. not related to the units + satu_gyro: 35 # the saturation value of IMU's angular velocity. not related to the units + acc_norm: 9.81 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration + lidar_meas_cov: 0.01 # 0.001 + acc_cov_output: 500 + gyr_cov_output: 1000 + b_acc_cov: 0.0001 + b_gyr_cov: 0.0001 + imu_meas_acc_cov: 0.1 #0.1 # 2 + imu_meas_omg_cov: 0.1 #0.1 # 2 + gyr_cov_input: 0.01 # for IMU as input model + acc_cov_input: 0.1 # for IMU as input model + plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane + match_s: 81 + ivox_grid_resolution: 2.0 + gravity: [0.0, 0.0, -9.810] # preknown gravity, use when imu_en is false or start from a non-stationary state + extrinsic_T: [0.0, 0.0, 0.0] + extrinsic_R: [ 1, 0, 0, + 0, 1, 0, + 0, 0, 1 ] + +odometry: + publish_odometry_without_downsample: false + +publish: + path_en: true # false: close the path output + scan_publish_en: true # false: close all the point cloud output + scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame + +pcd_save: + pcd_save_en: false + interval: -1 # how many LiDAR frames saved in each pcd file; + # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames. \ No newline at end of file diff --git a/config/velody16.yaml b/config/velody16.yaml new file mode 100755 index 0000000..a270e2e --- /dev/null +++ b/config/velody16.yaml @@ -0,0 +1,59 @@ +common: + lid_topic: "/velodyne_points" + imu_topic: "/imu/data" + con_frame: false # true: if you need to combine several LiDAR frames into one + con_frame_num: 1 # the number of frames combined + cut_frame: false # true: if you need to cut one LiDAR frame into several subframes + cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency + time_diff_lidar_to_imu: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme) + +preprocess: + lidar_type: 2 + scan_line: 32 + timestamp_unit: 2 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond. + blind: 2.0 + +mapping: + imu_en: true + extrinsic_est_en: false # for aggressive motion, set this variable false + imu_time_inte: 0.01 # = 1 / frequency of IMU + lidar_time_inte: 0.1 + satu_acc: 30.0 # the saturation value of IMU's acceleration. not related to the units + satu_gyro: 35 # the saturation value of IMU's angular velocity. not related to the units + acc_norm: 9.81 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration + lidar_meas_cov: 0.01 # 0.001 + acc_cov_output: 500 + gyr_cov_output: 1000 + b_acc_cov: 0.0001 + b_gyr_cov: 0.0001 + imu_meas_acc_cov: 0.1 #0.1 # 2 + imu_meas_omg_cov: 0.1 #0.1 # 2 + gyr_cov_input: 0.01 # for IMU as input model + acc_cov_input: 0.1 # for IMU as input model + plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane + match_s: 81 + ivox_grid_resolution: 2.0 + gravity: [0.0, 0.0, -9.810] # [-0.30, 0.880, -9.76] # liosam [0.0, 9.810, 0.0] # # preknown gravity, use when imu_en is false or start from a non-stationary state + extrinsic_T: [ 0, 0, 0.28] # ulhk # [-0.5, 1.4, 1.5] # utbm + # extrinsic_R: [ 0, 1, 0, + # -1, 0, 0, + # 0, 0, 1 ] # ulhk 5 6 + # extrinsic_R: [ 0, -1, 0, + # 1, 0, 0, + # 0, 0, 1 ] # utbm 1, 2 + extrinsic_R: [ 1, 0, 0, + 0, 1, 0, + 0, 0, 1 ] # ulhk 4 utbm 3 + +odometry: + publish_odometry_without_downsample: false + +publish: + path_en: true # false: close the path output + scan_publish_en: true # false: close all the point cloud output + scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame + +pcd_save: + pcd_save_en: false + interval: -1 # how many LiDAR frames saved in each pcd file; + # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames. \ No newline at end of file diff --git a/include/.vscode/c_cpp_properties.json b/include/.vscode/c_cpp_properties.json new file mode 100755 index 0000000..d7fe77d --- /dev/null +++ b/include/.vscode/c_cpp_properties.json @@ -0,0 +1,18 @@ +{ + "configurations": [ + { + "browse": { + "databaseFilename": "", + "limitSymbolsToIncludedHeaders": true + }, + "includePath": [ + "/home/ecstasy/catkin_ws/devel/include/**", + "/opt/ros/melodic/include/**", + "/home/ecstasy/catkin_ws/src/FAST_LIO/include/**", + "/usr/include/**" + ], + "name": "ROS" + } + ], + "version": 4 +} \ No newline at end of file diff --git a/include/.vscode/settings.json b/include/.vscode/settings.json new file mode 100755 index 0000000..4a9ea37 --- /dev/null +++ b/include/.vscode/settings.json @@ -0,0 +1,6 @@ +{ + "python.autoComplete.extraPaths": [ + "/home/ecstasy/catkin_ws/devel/lib/python2.7/dist-packages", + "/opt/ros/melodic/lib/python2.7/dist-packages" + ] +} \ No newline at end of file diff --git a/include/IKFoM/.gitignore b/include/IKFoM/.gitignore new file mode 100644 index 0000000..259148f --- /dev/null +++ b/include/IKFoM/.gitignore @@ -0,0 +1,32 @@ +# Prerequisites +*.d + +# Compiled Object files +*.slo +*.lo +*.o +*.obj + +# Precompiled Headers +*.gch +*.pch + +# Compiled Dynamic libraries +*.so +*.dylib +*.dll + +# Fortran module files +*.mod +*.smod + +# Compiled Static libraries +*.lai +*.la +*.a +*.lib + +# Executables +*.exe +*.out +*.app diff --git a/include/IKFoM/.vscode/settings.json b/include/IKFoM/.vscode/settings.json new file mode 100644 index 0000000..d756850 --- /dev/null +++ b/include/IKFoM/.vscode/settings.json @@ -0,0 +1,5 @@ +{ + "files.associations": { + "complex": "cpp" + } +} \ No newline at end of file diff --git a/include/IKFoM/IKFoM_toolkit/.vscode/settings.json b/include/IKFoM/IKFoM_toolkit/.vscode/settings.json new file mode 100755 index 0000000..b619d8a --- /dev/null +++ b/include/IKFoM/IKFoM_toolkit/.vscode/settings.json @@ -0,0 +1,62 @@ +{ + "files.associations": { + "iosfwd": "cpp", + "core": "cpp", + "cctype": "cpp", + "clocale": "cpp", + "cmath": "cpp", + "cstdarg": "cpp", + "cstddef": "cpp", + "cstdio": "cpp", + "cstdlib": "cpp", + "cstring": "cpp", + "ctime": "cpp", + "cwchar": "cpp", + "cwctype": "cpp", + "array": "cpp", + "atomic": "cpp", + "strstream": "cpp", + "*.tcc": "cpp", + "bitset": "cpp", + "chrono": "cpp", + "complex": "cpp", + "cstdint": "cpp", + "deque": "cpp", + "list": "cpp", + "unordered_map": "cpp", + "vector": "cpp", + "exception": "cpp", + "fstream": "cpp", + "functional": "cpp", + "initializer_list": "cpp", + "iomanip": "cpp", + "iostream": "cpp", + "istream": "cpp", + "limits": "cpp", + "new": "cpp", + "ostream": "cpp", + "numeric": "cpp", + "ratio": "cpp", + "sstream": "cpp", + "stdexcept": "cpp", + "streambuf": "cpp", + "system_error": "cpp", + "thread": "cpp", + "cfenv": "cpp", + "cinttypes": "cpp", + "tuple": "cpp", + "type_traits": "cpp", + "utility": "cpp", + "typeinfo": "cpp", + "algorithm": "cpp", + "iterator": "cpp", + "map": "cpp", + "memory": "cpp", + "memory_resource": "cpp", + "optional": "cpp", + "random": "cpp", + "set": "cpp", + "string": "cpp", + "string_view": "cpp" + } +} \ No newline at end of file diff --git a/include/IKFoM/IKFoM_toolkit/esekfom/.vscode/settings.json b/include/IKFoM/IKFoM_toolkit/esekfom/.vscode/settings.json new file mode 100755 index 0000000..c807bd5 --- /dev/null +++ b/include/IKFoM/IKFoM_toolkit/esekfom/.vscode/settings.json @@ -0,0 +1,5 @@ +{ + "files.associations": { + "core": "cpp" + } +} \ No newline at end of file diff --git a/include/IKFoM/IKFoM_toolkit/esekfom/esekfom.hpp b/include/IKFoM/IKFoM_toolkit/esekfom/esekfom.hpp new file mode 100755 index 0000000..75e2f02 --- /dev/null +++ b/include/IKFoM/IKFoM_toolkit/esekfom/esekfom.hpp @@ -0,0 +1,344 @@ +/* + * Copyright (c) 2019--2023, The University of Hong Kong + * All rights reserved. + * + * Author: Dongjiao HE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#ifndef ESEKFOM_EKF_HPP +#define ESEKFOM_EKF_HPP + + +#include +#include + +#include +#include +#include +#include +#include +#include + +#include "../mtk/types/vect.hpp" +#include "../mtk/types/SOn.hpp" +#include "../mtk/types/S2.hpp" +#include "../mtk/types/SEn.hpp" +#include "../mtk/startIdx.hpp" +#include "../mtk/build_manifold.hpp" +#include "util.hpp" + +namespace esekfom { + +using namespace Eigen; + +template +struct dyn_share_modified +{ + bool valid; + bool converge; + T M_Noise; + Eigen::Matrix z; + Eigen::Matrix h_x; + Eigen::Matrix z_IMU; + Eigen::Matrix R_IMU; + bool satu_check[6]; +}; + +template +class esekf{ + + typedef esekf self; + enum{ + n = state::DOF, m = state::DIM, l = measurement::DOF + }; + +public: + + typedef typename state::scalar scalar_type; + typedef Matrix cov; + typedef Matrix cov_; + typedef SparseMatrix spMt; + typedef Matrix vectorized_state; + typedef Matrix flatted_state; + typedef flatted_state processModel(state &, const input &); + typedef Eigen::Matrix processMatrix1(state &, const input &); + typedef Eigen::Matrix processMatrix2(state &, const input &); + typedef Eigen::Matrix processnoisecovariance; + + typedef void measurementModel_dyn_share_modified_cov(state &, Eigen::Matrix3d, Eigen::Matrix3d, dyn_share_modified &); + typedef void measurementModel_dyn_share_modified(state &, dyn_share_modified &); + typedef Eigen::Matrix measurementMatrix1(state &); + typedef Eigen::Matrix measurementMatrix1_dyn(state &); + typedef Eigen::Matrix measurementMatrix2(state &); + typedef Eigen::Matrix measurementMatrix2_dyn(state &); + typedef Eigen::Matrix measurementnoisecovariance; + typedef Eigen::Matrix measurementnoisecovariance_dyn; + + esekf(const state &x = state(), + const cov &P = cov::Identity()): x_(x), P_(P){}; + + void init_dyn_share_modified_2h(processModel f_in, processMatrix1 f_x_in, measurementModel_dyn_share_modified_cov h_dyn_share_in1) + { + f = f_in; + f_x = f_x_in; + // f_w = f_w_in; + h_dyn_share_modified_1 = h_dyn_share_in1; + // h_dyn_share_modified_3 = h_dyn_share_in3; + maximum_iter = 1; + x_.build_S2_state(); + x_.build_SO3_state(); + x_.build_vect_state(); + x_.build_SEN_state(); + } + + void init_dyn_share_modified_3h(processModel f_in, processMatrix1 f_x_in, measurementModel_dyn_share_modified_cov h_dyn_share_in1, measurementModel_dyn_share_modified h_dyn_share_in2) + { + f = f_in; + f_x = f_x_in; + // f_w = f_w_in; + h_dyn_share_modified_1 = h_dyn_share_in1; + h_dyn_share_modified_2 = h_dyn_share_in2; + // h_dyn_share_modified_3 = h_dyn_share_in3; + maximum_iter = 1; + x_.build_S2_state(); + x_.build_SO3_state(); + x_.build_vect_state(); + x_.build_SEN_state(); + } + + // iterated error state EKF propogation + void predict(double &dt, processnoisecovariance &Q, const input &i_in, bool predict_state, bool prop_cov){ + if (predict_state) + { + flatted_state f_ = f(x_, i_in); + x_.oplus(f_, dt); + } + + if (prop_cov) + { + flatted_state f_ = f(x_, i_in); + // state x_before = x_; + + cov_ f_x_ = f_x(x_, i_in); + cov f_x_final; + F_x1 = cov::Identity(); + for (std::vector, int> >::iterator it = x_.vect_state.begin(); it != x_.vect_state.end(); it++) { + int idx = (*it).first.first; + int dim = (*it).first.second; + int dof = (*it).second; + for(int i = 0; i < n; i++){ + for(int j=0; j res_temp_SO3; + MTK::vect<3, scalar_type> seg_SO3; + for (std::vector >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) { + int idx = (*it).first; + int dim = (*it).second; + for(int i = 0; i < 3; i++){ + seg_SO3(i) = -1 * f_(dim + i) * dt; + } + // MTK::SO3 res; + // res.w() = MTK::exp(res.vec(), seg_SO3, scalar_type(1/2)); + F_x1.template block<3, 3>(idx, idx) = MTK::SO3::exp(seg_SO3); // res.normalized().toRotationMatrix(); + res_temp_SO3 = MTK::A_matrix(seg_SO3); + for(int i = 0; i < n; i++){ + f_x_final. template block<3, 1>(idx, i) = res_temp_SO3 * (f_x_. template block<3, 1>(dim, i)); + } + } + + F_x1 += f_x_final * dt; + P_ = F_x1 * P_ * (F_x1).transpose() + Q * (dt * dt); + } + } + + bool update_iterated_dyn_share_modified() { + dyn_share_modified dyn_share; + state x_propagated = x_; + int dof_Measurement; + double m_noise; + for(int i=0; i(0, 0), P_. template block<3, 3>(3, 3), dyn_share); + if(! dyn_share.valid) + { + return false; + // continue; + } + Matrix z = dyn_share.z; + // Matrix R = dyn_share.R; + Matrix h_x = dyn_share.h_x; + // Matrix h_v = dyn_share.h_v; + dof_Measurement = h_x.rows(); + m_noise = dyn_share.M_Noise; + // dof_Measurement_noise = dyn_share.R.rows(); + // vectorized_state dx, dx_new; + // x_.boxminus(dx, x_propagated); + // dx_new = dx; + // P_ = P_propagated; + + Matrix PHT; + Matrix HPHT; + Matrix K_; + if(n > dof_Measurement) + { + PHT = P_. template block(0, 0) * h_x.transpose(); + HPHT = h_x * PHT.topRows(12); + for (int m = 0; m < dof_Measurement; m++) + { + HPHT(m, m) += m_noise; + } + K_= PHT*HPHT.inverse(); + } + else + { + Matrix HTH = m_noise * h_x.transpose() * h_x; + Matrix P_inv = P_.inverse(); + P_inv.template block<12, 12>(0, 0) += HTH; + P_inv = P_inv.inverse(); + K_ = P_inv.template block(0, 0) * h_x.transpose() * m_noise; + } + Matrix dx_ = K_ * z; // - h) + (K_x - Matrix::Identity()) * dx_new; + // state x_before = x_; + + x_.boxplus(dx_); + { + P_ = P_ - K_*h_x*P_. template block<12, n>(0, 0); + } + } + return true; + } + + void update_iterated_dyn_share_IMU() { + + dyn_share_modified dyn_share; + for(int i=0; i z = dyn_share.z_IMU; + + Matrix PHT; + Matrix HP; + Matrix HPHT; + PHT.setZero(); + HP.setZero(); + HPHT.setZero(); + for (int l_ = 0; l_ < 6; l_++) + { + if (!dyn_share.satu_check[l_]) + { + PHT.col(l_) = P_.col(15+l_) + P_.col(24+l_); + HP.row(l_) = P_.row(15+l_) + P_.row(24+l_); + } + } + for (int l_ = 0; l_ < 6; l_++) + { + if (!dyn_share.satu_check[l_]) + { + HPHT.col(l_) = HP.col(15+l_) + HP.col(24+l_); + } + HPHT(l_, l_) += dyn_share.R_IMU(l_); //, l); + } + Eigen::Matrix K = PHT * HPHT.inverse(); + + Matrix dx_ = K * z; + + P_ -= K * HP; + x_.boxplus(dx_); + } + return; + } + + void change_x(state &input_state) + { + x_ = input_state; + + if((!x_.vect_state.size())&&(!x_.SO3_state.size())&&(!x_.S2_state.size())&&(!x_.SEN_state.size())) + { + x_.build_S2_state(); + x_.build_SO3_state(); + x_.build_vect_state(); + x_.build_SEN_state(); + } + } + + void change_P(cov &input_cov) + { + P_ = input_cov; + } + + const state& get_x() const { + return x_; + } + const cov& get_P() const { + return P_; + } + cov P_; + state x_; +private: + measurement m_; + spMt l_; + spMt f_x_1; + spMt f_x_2; + cov F_x1 = cov::Identity(); + cov F_x2 = cov::Identity(); + cov L_ = cov::Identity(); + + processModel *f; + processMatrix1 *f_x; + processMatrix2 *f_w; + + measurementMatrix1 *h_x; + measurementMatrix2 *h_v; + + measurementMatrix1_dyn *h_x_dyn; + measurementMatrix2_dyn *h_v_dyn; + + measurementModel_dyn_share_modified_cov *h_dyn_share_modified_1; + + measurementModel_dyn_share_modified *h_dyn_share_modified_2; + + measurementModel_dyn_share_modified *h_dyn_share_modified_3; + + int maximum_iter = 0; + scalar_type limit[n]; + +public: + EIGEN_MAKE_ALIGNED_OPERATOR_NEW +}; + +} // namespace esekfom + +#endif // ESEKFOM_EKF_HPP diff --git a/include/IKFoM/IKFoM_toolkit/esekfom/util.hpp b/include/IKFoM/IKFoM_toolkit/esekfom/util.hpp new file mode 100755 index 0000000..ab39fc4 --- /dev/null +++ b/include/IKFoM/IKFoM_toolkit/esekfom/util.hpp @@ -0,0 +1,82 @@ +/* + * Copyright (c) 2019--2023, The University of Hong Kong + * All rights reserved. + * + * Author: Dongjiao HE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#ifndef __MEKFOM_UTIL_HPP__ +#define __MEKFOM_UTIL_HPP__ + +#include +#include "../mtk/src/mtkmath.hpp" +namespace esekfom { + +template +class is_same { +public: + operator bool() { + return false; + } +}; +template +class is_same { +public: + operator bool() { + return true; + } +}; + +template +class is_double { +public: + operator bool() { + return false; + } +}; + +template<> +class is_double { +public: + operator bool() { + return true; + } +}; + +template +static T +id(const T &x) +{ + return x; +} + +} // namespace esekfom + +#endif // __MEKFOM_UTIL_HPP__ diff --git a/include/IKFoM/IKFoM_toolkit/mtk/build_manifold.hpp b/include/IKFoM/IKFoM_toolkit/mtk/build_manifold.hpp new file mode 100755 index 0000000..115ec7e --- /dev/null +++ b/include/IKFoM/IKFoM_toolkit/mtk/build_manifold.hpp @@ -0,0 +1,248 @@ +// This is an advanced implementation of the algorithm described in the +// following paper: +// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. +// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 + +/* + * Copyright (c) 2019--2023, The University of Hong Kong + * All rights reserved. + * + * Modifier: Dongjiao HE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +/* + * Copyright (c) 2008--2011, Universitaet Bremen + * All rights reserved. + * + * Author: Christoph Hertzberg + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ +/** + * @file mtk/build_manifold.hpp + * @brief Macro to automatically construct compound manifolds. + * + */ +#ifndef MTK_AUTOCONSTRUCT_HPP_ +#define MTK_AUTOCONSTRUCT_HPP_ + +#include + +#include +#include +#include + +#include "src/SubManifold.hpp" +#include "startIdx.hpp" + +#ifndef PARSED_BY_DOXYGEN +//////// internals ////// + +#define MTK_APPLY_MACRO_ON_TUPLE(r, macro, tuple) macro tuple + +#define MTK_TRANSFORM_COMMA(macro, entries) BOOST_PP_SEQ_ENUM(BOOST_PP_SEQ_TRANSFORM_S(1, MTK_APPLY_MACRO_ON_TUPLE, macro, entries)) + +#define MTK_TRANSFORM(macro, entries) BOOST_PP_SEQ_FOR_EACH_R(1, MTK_APPLY_MACRO_ON_TUPLE, macro, entries) + +#define MTK_CONSTRUCTOR_ARG( type, id) const type& id = type() +#define MTK_CONSTRUCTOR_COPY( type, id) id(id) +#define MTK_BOXPLUS( type, id) id.boxplus(MTK::subvector(__vec, &self::id), __scale); +#define MTK_OPLUS( type, id) id.oplus(MTK::subvector_(__vec, &self::id), __scale); +#define MTK_BOXMINUS( type, id) id.boxminus(MTK::subvector(__res, &self::id), __oth.id); +#define MTK_HAT( type, id) if(id.IDX == idx){id.hat(vec, res);} +#define MTK_JACOB_RIGHT_INV( type, id) if(id.IDX == idx){id.Jacob_right_inv(vec, res);} +#define MTK_JACOB_RIGHT( type, id) if(id.IDX == idx){id.Jacob_right(vec, res);} +#define MTK_S2_hat( type, id) if(id.IDX == idx){id.S2_hat(res);} +#define MTK_S2_Nx_yy( type, id) if(id.IDX == idx){id.S2_Nx_yy(res);} +#define MTK_S2_Mx( type, id) if(id.IDX == idx){id.S2_Mx(res, dx);} +#define MTK_OSTREAM( type, id) << __var.id << " " +#define MTK_ISTREAM( type, id) >> __var.id +#define MTK_S2_state( type, id) if(id.TYP == 1){S2_state.push_back(std::make_pair(id.IDX, id.DIM));} +#define MTK_SO3_state( type, id) if(id.TYP == 2){(SO3_state).push_back(std::make_pair(id.IDX, id.DIM));} +#define MTK_vect_state( type, id) if(id.TYP == 0){(vect_state).push_back(std::make_pair(std::make_pair(id.IDX, id.DIM), type::DOF));} +#define MTK_SEN_state( type, id) if(id.TYP == 4){(SEN_state).push_back(std::make_pair(std::make_pair(id.IDX, id.DIM), type::DOF));} + +#define MTK_SUBVARLIST(seq, S2state, SO3state, SENstate) \ +BOOST_PP_FOR_1( \ + ( \ + BOOST_PP_SEQ_SIZE(seq), \ + BOOST_PP_SEQ_HEAD(seq), \ + BOOST_PP_SEQ_TAIL(seq) (~), \ + 0,\ + 0,\ + S2state,\ + SO3state,\ + SENstate ),\ + MTK_ENTRIES_TEST, MTK_ENTRIES_NEXT, MTK_ENTRIES_OUTPUT) + +#define MTK_PUT_TYPE(type, id, dof, dim, S2state, SO3state, SENstate) \ + MTK::SubManifold id; +#define MTK_PUT_TYPE_AND_ENUM(type, id, dof, dim, S2state, SO3state, SENstate) \ + MTK_PUT_TYPE(type, id, dof, dim, S2state, SO3state, SENstate) \ + enum {DOF = type::DOF + dof}; \ + enum {DIM = type::DIM+dim}; \ + typedef type::scalar scalar; + +#define MTK_ENTRIES_OUTPUT(r, state) MTK_ENTRIES_OUTPUT_I state +#define MTK_ENTRIES_OUTPUT_I(s, head, seq, dof, dim, S2state, SO3state, SENstate) \ + MTK_APPLY_MACRO_ON_TUPLE(~, \ + BOOST_PP_IF(BOOST_PP_DEC(s), MTK_PUT_TYPE, MTK_PUT_TYPE_AND_ENUM), \ + ( BOOST_PP_TUPLE_REM_2 head, dof, dim, S2state, SO3state, SENstate)) + +#define MTK_ENTRIES_TEST(r, state) MTK_TUPLE_ELEM_4_0 state + +//! this used to be BOOST_PP_TUPLE_ELEM_4_0: +#define MTK_TUPLE_ELEM_4_0(a,b,c,d,e,f, g, h) a + +#define MTK_ENTRIES_NEXT(r, state) MTK_ENTRIES_NEXT_I state +#define MTK_ENTRIES_NEXT_I(len, head, seq, dof, dim, S2state, SO3state, SENstate) ( \ + BOOST_PP_DEC(len), \ + BOOST_PP_SEQ_HEAD(seq), \ + BOOST_PP_SEQ_TAIL(seq), \ + dof + BOOST_PP_TUPLE_ELEM_2_0 head::DOF,\ + dim + BOOST_PP_TUPLE_ELEM_2_0 head::DIM,\ + S2state,\ + SO3state,\ + SENstate ) + +#endif /* not PARSED_BY_DOXYGEN */ + + +/** + * Construct a manifold. + * @param name is the class-name of the manifold, + * @param entries is the list of sub manifolds + * + * Entries must be given in a list like this: + * @code + * typedef MTK::trafo > Pose; + * typedef MTK::vect Vec3; + * MTK_BUILD_MANIFOLD(imu_state, + * ((Pose, pose)) + * ((Vec3, vel)) + * ((Vec3, acc_bias)) + * ) + * @endcode + * Whitespace is optional, but the double parentheses are necessary. + * Construction is done entirely in preprocessor. + * After construction @a name is also a manifold. Its members can be + * accessed by names given in @a entries. + * + * @note Variable types are not allowed to have commas, thus types like + * @c vect need to be typedef'ed ahead. + */ +#define MTK_BUILD_MANIFOLD(name, entries) \ +struct name { \ + typedef name self; \ + std::vector > S2_state;\ + std::vector > SO3_state;\ + std::vector, int> > vect_state;\ + std::vector, int> > SEN_state;\ + MTK_SUBVARLIST(entries, S2_state, SO3_state, SEN_state) \ + name ( \ + MTK_TRANSFORM_COMMA(MTK_CONSTRUCTOR_ARG, entries) \ + ) : \ + MTK_TRANSFORM_COMMA(MTK_CONSTRUCTOR_COPY, entries) {}\ + int getDOF() const { return DOF; } \ + void boxplus(const MTK::vectview & __vec, scalar __scale = 1 ) { \ + MTK_TRANSFORM(MTK_BOXPLUS, entries)\ + } \ + void oplus(const MTK::vectview & __vec, scalar __scale = 1 ) { \ + MTK_TRANSFORM(MTK_OPLUS, entries)\ + } \ + void boxminus(MTK::vectview __res, const name& __oth) const { \ + MTK_TRANSFORM(MTK_BOXMINUS, entries)\ + } \ + friend std::ostream& operator<<(std::ostream& __os, const name& __var){ \ + return __os MTK_TRANSFORM(MTK_OSTREAM, entries); \ + } \ + void build_S2_state(){\ + MTK_TRANSFORM(MTK_S2_state, entries)\ + }\ + void build_vect_state(){\ + MTK_TRANSFORM(MTK_vect_state, entries)\ + }\ + void build_SO3_state(){\ + MTK_TRANSFORM(MTK_SO3_state, entries)\ + }\ + void build_SEN_state(){\ + MTK_TRANSFORM(MTK_SEN_state, entries)\ + }\ + void Lie_hat(Eigen::VectorXd &vec, Eigen::MatrixXd &res, int idx) {\ + MTK_TRANSFORM(MTK_HAT, entries)\ + }\ + void Lie_Jacob_Right_Inv(Eigen::VectorXd &vec, Eigen::MatrixXd &res, int idx) {\ + MTK_TRANSFORM(MTK_JACOB_RIGHT_INV, entries)\ + }\ + void Lie_Jacob_Right(Eigen::VectorXd &vec, Eigen::MatrixXd &res, int idx) {\ + MTK_TRANSFORM(MTK_JACOB_RIGHT, entries)\ + }\ + void S2_hat(Eigen::Matrix &res, int idx) {\ + MTK_TRANSFORM(MTK_S2_hat, entries)\ + }\ + void S2_Nx_yy(Eigen::Matrix &res, int idx) {\ + MTK_TRANSFORM(MTK_S2_Nx_yy, entries)\ + }\ + void S2_Mx(Eigen::Matrix &res, Eigen::Matrix dx, int idx) {\ + MTK_TRANSFORM(MTK_S2_Mx, entries)\ + }\ + friend std::istream& operator>>(std::istream& __is, name& __var){ \ + return __is MTK_TRANSFORM(MTK_ISTREAM, entries); \ + } \ +}; + + + +#endif /*MTK_AUTOCONSTRUCT_HPP_*/ diff --git a/include/IKFoM/IKFoM_toolkit/mtk/src/SubManifold.hpp b/include/IKFoM/IKFoM_toolkit/mtk/src/SubManifold.hpp new file mode 100755 index 0000000..a1b13de --- /dev/null +++ b/include/IKFoM/IKFoM_toolkit/mtk/src/SubManifold.hpp @@ -0,0 +1,123 @@ +// This is an advanced implementation of the algorithm described in the +// following paper: +// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. +// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 + +/* + * Copyright (c) 2019--2023, The University of Hong Kong + * All rights reserved. + * + * Modifier: Dongjiao HE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +/* + * Copyright (c) 2008--2011, Universitaet Bremen + * All rights reserved. + * + * Author: Christoph Hertzberg + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ +/** + * @file mtk/src/SubManifold.hpp + * @brief Defines the SubManifold class + */ + + +#ifndef SUBMANIFOLD_HPP_ +#define SUBMANIFOLD_HPP_ + + +#include "vectview.hpp" + + +namespace MTK { + +/** + * @ingroup SubManifolds + * Helper class for compound manifolds. + * This class wraps a manifold T and provides an enum IDX refering to the + * index of the SubManifold within the compound manifold. + * + * Memberpointers to a submanifold can be used for @ref SubManifolds "functions accessing submanifolds". + * + * @tparam T The manifold type of the sub-type + * @tparam idx The index of the sub-type within the compound manifold + */ +template +struct SubManifold : public T +{ + enum {IDX = idx, DIM = dim /*!< index of the sub-type within the compound manifold */ }; + //! manifold type + typedef T type; + + //! Construct from derived type + template + explicit + SubManifold(const X& t) : T(t) {}; + + //! Construct from internal type + //explicit + SubManifold(const T& t) : T(t) {}; + + //! inherit assignment operator + using T::operator=; + +}; + +} // namespace MTK + + +#endif /* SUBMANIFOLD_HPP_ */ diff --git a/include/IKFoM/IKFoM_toolkit/mtk/src/mtkmath.hpp b/include/IKFoM/IKFoM_toolkit/mtk/src/mtkmath.hpp new file mode 100755 index 0000000..a8770e4 --- /dev/null +++ b/include/IKFoM/IKFoM_toolkit/mtk/src/mtkmath.hpp @@ -0,0 +1,294 @@ +// This is an advanced implementation of the algorithm described in the +// following paper: +// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. +// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 + +/* + * Copyright (c) 2019--2023, The University of Hong Kong + * All rights reserved. + * + * Modifier: Dongjiao HE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +/* + * Copyright (c) 2008--2011, Universitaet Bremen + * All rights reserved. + * + * Author: Christoph Hertzberg + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ +/** + * @file mtk/src/mtkmath.hpp + * @brief several math utility functions. + */ + +#ifndef MTKMATH_H_ +#define MTKMATH_H_ + +#include + +#include + +#include "../types/vect.hpp" + +#ifndef M_PI +#define M_PI 3.1415926535897932384626433832795 +#endif + + +namespace MTK { + +namespace internal { + +template +struct traits { + typedef typename Manifold::scalar scalar; + enum {DOF = Manifold::DOF}; + typedef vect vectorized_type; + typedef Eigen::Matrix matrix_type; +}; + +template<> +struct traits : traits > {}; +template<> +struct traits : traits > {}; + +} // namespace internal + +/** + * \defgroup MTKMath Mathematical helper functions + */ +//@{ + +//! constant @f$ \pi @f$ +const double pi = M_PI; + +template inline scalar tolerance(); + +template<> inline float tolerance() { return 1e-5f; } +template<> inline double tolerance() { return 1e-11; } + + +/** + * normalize @a x to @f$[-bound, bound] @f$. + * + * result for @f$ x = bound + 2\cdot n\cdot bound @f$ is arbitrary @f$\pm bound @f$. + */ +template +inline scalar normalize(scalar x, scalar bound){ //not used + if(std::fabs(x) <= bound) return x; + int r = (int)(x *(scalar(1.0)/ bound)); + return x - ((r + (r>>31) + 1) & ~1)*bound; +} + +/** + * Calculate cosine and sinc of sqrt(x2). + * @param x2 the squared angle must be non-negative + * @return a pair containing cos and sinc of sqrt(x2) + */ +template +std::pair cos_sinc_sqrt(const scalar &x2){ + using std::sqrt; + using std::cos; + using std::sin; + static scalar const taylor_0_bound = boost::math::tools::epsilon(); + static scalar const taylor_2_bound = sqrt(taylor_0_bound); + static scalar const taylor_n_bound = sqrt(taylor_2_bound); + + assert(x2>=0 && "argument must be non-negative and must not be nan/-nan"); + + // FIXME check if bigger bounds are possible + if(x2>=taylor_n_bound) { + // slow fall-back solution + scalar x = sqrt(x2); + return std::make_pair(cos(x), sin(x)/x); // x is greater than 0. + } + + // FIXME Replace by Horner-Scheme (4 instead of 5 FLOP/term, numerically more stable, theoretically cos and sinc can be calculated in parallel using SSE2 mulpd/addpd) + // TODO Find optimal coefficients using Remez algorithm + static scalar const inv[] = {1/3., 1/4., 1/5., 1/6., 1/7., 1/8., 1/9.}; + scalar cosi = 1., sinc=1; + scalar term = -1/2. * x2; + for(int i=0; i<3; ++i) { + cosi += term; + term *= inv[2*i]; + sinc += term; + term *= -inv[2*i+1] * x2; + } + + return std::make_pair(cosi, sinc); + +} + +template +Eigen::Matrix hat(const Base& v) { + Eigen::Matrix res; + res << 0, -v[2], v[1], + v[2], 0, -v[0], + -v[1], v[0], 0; + return res; +} + +template +Eigen::Matrix A_inv_trans(const Base& v){ + Eigen::Matrix res; + if(v.norm() > MTK::tolerance()) + { + res = Eigen::Matrix::Identity() + 0.5 * hat(v) + (1 - v.norm() * std::cos(v.norm() / 2) / 2 / std::sin(v.norm() / 2)) * hat(v) * hat(v) / v.squaredNorm(); + + } + else + { + res = Eigen::Matrix::Identity(); + } + + return res; +} + +template +Eigen::Matrix A_inv(const Base& v){ + Eigen::Matrix res; + if(v.norm() > MTK::tolerance()) + { + res = Eigen::Matrix::Identity() - 0.5 * hat(v) + (1 - v.norm() * std::cos(v.norm() / 2) / 2 / std::sin(v.norm() / 2)) * hat(v) * hat(v) / v.squaredNorm(); + + } + else + { + res = Eigen::Matrix::Identity(); + } + + return res; +} + +template +Eigen::Matrix S2_w_expw_( Eigen::Matrix v, scalar length) + { + Eigen::Matrix res; + scalar norm = std::sqrt(v[0]*v[0] + v[1]*v[1]); + if(norm < MTK::tolerance()){ + res = Eigen::Matrix::Zero(); + res(0, 1) = 1; + res(1, 2) = 1; + res /= length; + } + else{ + res << -v[0]*(1/norm-1/std::tan(norm))/std::sin(norm), norm/std::sin(norm), 0, + -v[1]*(1/norm-1/std::tan(norm))/std::sin(norm), 0, norm/std::sin(norm); + res /= length; + } + } + +template +Eigen::Matrix A_matrix(const Base & v){ + Eigen::Matrix res; + double squaredNorm = v[0] * v[0] + v[1] * v[1] + v[2] * v[2]; + double norm = std::sqrt(squaredNorm); + if(norm < MTK::tolerance()){ + res = Eigen::Matrix::Identity(); + } + else{ + res = Eigen::Matrix::Identity() + (1 - std::cos(norm)) / squaredNorm * hat(v) + (1 - std::sin(norm) / norm) / squaredNorm * hat(v) * hat(v); + } + return res; +} + +template +scalar exp(vectview result, vectview vec, const scalar& scale = 1) { + scalar norm2 = vec.squaredNorm(); + std::pair cos_sinc = cos_sinc_sqrt(scale*scale * norm2); + scalar mult = cos_sinc.second * scale; + result = mult * vec; + return cos_sinc.first; +} + + +/** + * Inverse function to @c exp. + * + * @param result @c vectview to the result + * @param w scalar part of input + * @param vec vector part of input + * @param scale scale result by this value + * @param plus_minus_periodicity if true values @f$[w, vec]@f$ and @f$[-w, -vec]@f$ give the same result + */ +template +void log(vectview result, + const scalar &w, const vectview vec, + const scalar &scale, bool plus_minus_periodicity) +{ + // FIXME implement optimized case for vec.squaredNorm() <= tolerance() * (w*w) via Rational Remez approximation ~> only one division + scalar nv = vec.norm(); + if(nv < tolerance()) { + if(!plus_minus_periodicity && w < 0) { + // find the maximal entry: + int i; + nv = vec.cwiseAbs().maxCoeff(&i); + result = scale * std::atan2(nv, w) * vect::Unit(i); + return; + } + nv = tolerance(); + } + scalar s = scale / nv * (plus_minus_periodicity ? std::atan(nv / w) : std::atan2(nv, w) ); + + result = s * vec; +} + + +} // namespace MTK + + +#endif /* MTKMATH_H_ */ diff --git a/include/IKFoM/IKFoM_toolkit/mtk/src/vectview.hpp b/include/IKFoM/IKFoM_toolkit/mtk/src/vectview.hpp new file mode 100755 index 0000000..5025071 --- /dev/null +++ b/include/IKFoM/IKFoM_toolkit/mtk/src/vectview.hpp @@ -0,0 +1,168 @@ + +/* + * Copyright (c) 2008--2011, Universitaet Bremen + * All rights reserved. + * + * Author: Christoph Hertzberg + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ +/** + * @file mtk/src/vectview.hpp + * @brief Wrapper class around a pointer used as interface for plain vectors. + */ + +#ifndef VECTVIEW_HPP_ +#define VECTVIEW_HPP_ + +#include + +namespace MTK { + +/** + * A view to a vector. + * Essentially, @c vectview is only a pointer to @c scalar but can be used directly in @c Eigen expressions. + * The dimension of the vector is given as template parameter and type-checked when used in expressions. + * Data has to be modifiable. + * + * @tparam scalar Scalar type of the vector. + * @tparam dim Dimension of the vector. + * + * @todo @c vectview can be replaced by simple inheritance of @c Eigen::Map, as soon as they get const-correct + */ +namespace internal { + template + struct CovBlock { + typedef typename Eigen::Block, T1::DOF, T2::DOF> Type; + typedef typename Eigen::Block, T1::DOF, T2::DOF> ConstType; + }; + + template + struct CovBlock_ { + typedef typename Eigen::Block, T1::DIM, T2::DIM> Type; + typedef typename Eigen::Block, T1::DIM, T2::DIM> ConstType; + }; + + template + struct CrossCovBlock { + typedef typename Eigen::Block, T1::DOF, T2::DOF> Type; + typedef typename Eigen::Block, T1::DOF, T2::DOF> ConstType; + }; + + template + struct CrossCovBlock_ { + typedef typename Eigen::Block, T1::DIM, T2::DIM> Type; + typedef typename Eigen::Block, T1::DIM, T2::DIM> ConstType; + }; + + template + struct VectviewBase { + typedef Eigen::Matrix matrix_type; + typedef typename matrix_type::MapType Type; + typedef typename matrix_type::ConstMapType ConstType; + }; + + template + struct UnalignedType { + typedef T type; + }; +} + +template +class vectview : public internal::VectviewBase::Type { + typedef internal::VectviewBase VectviewBase; +public: + //! plain matrix type + typedef typename VectviewBase::matrix_type matrix_type; + //! base type + typedef typename VectviewBase::Type base; + //! construct from pointer + explicit + vectview(scalar* data, int dim_=dim) : base(data, dim_) {} + //! construct from plain matrix + vectview(matrix_type& m) : base(m.data(), m.size()) {} + //! construct from another @c vectview + vectview(const vectview &v) : base(v) {} + //! construct from Eigen::Block: + template + vectview(Eigen::VectorBlock block) : base(&block.coeffRef(0), block.size()) {} + template + vectview(Eigen::Block block) : base(&block.coeffRef(0), block.size()) {} + + //! inherit assignment operator + using base::operator=; + //! data pointer + scalar* data() {return const_cast(base::data());} +}; + +/** + * @c const version of @c vectview. + * Compared to @c Eigen::Map this implementation is const correct, i.e., + * data will not be modifiable using this view. + * + * @tparam scalar Scalar type of the vector. + * @tparam dim Dimension of the vector. + * + * @sa vectview + */ +template +class vectview : public internal::VectviewBase::ConstType { + typedef internal::VectviewBase VectviewBase; +public: + //! plain matrix type + typedef typename VectviewBase::matrix_type matrix_type; + //! base type + typedef typename VectviewBase::ConstType base; + //! construct from const pointer + explicit + vectview(const scalar* data, int dim_ = dim) : base(data, dim_) {} + //! construct from column vector + template + vectview(const Eigen::Matrix& m) : base(m.data()) {} + //! construct from row vector + template + vectview(const Eigen::Matrix& m) : base(m.data()) {} + //! construct from another @c vectview + vectview(vectview x) : base(x.data()) {} + //! construct from base + vectview(const base &x) : base(x) {} + /** + * Construct from Block + * @todo adapt this, when Block gets const-correct + */ + template + vectview(Eigen::VectorBlock block) : base(&block.coeffRef(0)) {} + template + vectview(Eigen::Block block) : base(&block.coeffRef(0)) {} + +}; + + +} // namespace MTK + +#endif /* VECTVIEW_HPP_ */ diff --git a/include/IKFoM/IKFoM_toolkit/mtk/startIdx.hpp b/include/IKFoM/IKFoM_toolkit/mtk/startIdx.hpp new file mode 100755 index 0000000..4dc2958 --- /dev/null +++ b/include/IKFoM/IKFoM_toolkit/mtk/startIdx.hpp @@ -0,0 +1,328 @@ +// This is an advanced implementation of the algorithm described in the +// following paper: +// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. +// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 + +/* + * Copyright (c) 2019--2023, The University of Hong Kong + * All rights reserved. + * + * Modifier: Dongjiao HE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +/* + * Copyright (c) 2008--2011, Universitaet Bremen + * All rights reserved. + * + * Author: Christoph Hertzberg + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ +/** + * @file mtk/startIdx.hpp + * @brief Tools to access sub-elements of compound manifolds. + */ +#ifndef GET_START_INDEX_H_ +#define GET_START_INDEX_H_ + +#include + +#include "src/SubManifold.hpp" +#include "src/vectview.hpp" + +namespace MTK { + + +/** + * \defgroup SubManifolds Accessing Submanifolds + * For compound manifolds constructed using MTK_BUILD_MANIFOLD, member pointers + * can be used to get sub-vectors or matrix-blocks of a corresponding big matrix. + * E.g. for a type @a pose consisting of @a orient and @a trans the member pointers + * @c &pose::orient and @c &pose::trans give all required information and are still + * valid if the base type gets extended or the actual types of @a orient and @a trans + * change (e.g. from 2D to 3D). + * + * @todo Maybe require manifolds to typedef MatrixType and VectorType, etc. + */ +//@{ + +/** + * Determine the index of a sub-variable within a compound variable. + */ +template +int getStartIdx( MTK::SubManifold Base::*) +{ + return idx; +} + +template +int getStartIdx_( MTK::SubManifold Base::*) +{ + return dim; +} + +/** + * Determine the degrees of freedom of a sub-variable within a compound variable. + */ +template +int getDof( MTK::SubManifold Base::*) +{ + return T::DOF; +} +template +int getDim( MTK::SubManifold Base::*) +{ + return T::DIM; +} + +/** + * set the diagonal elements of a covariance matrix corresponding to a sub-variable + */ +template +void setDiagonal(Eigen::Matrix &cov, + MTK::SubManifold Base::*, const typename Base::scalar &val) +{ + cov.diagonal().template segment(idx).setConstant(val); +} + +template +void setDiagonal_(Eigen::Matrix &cov, + MTK::SubManifold Base::*, const typename Base::scalar &val) +{ + cov.diagonal().template segment(dim).setConstant(val); +} + +/** + * Get the subblock of corresponding to two members, i.e. + * \code + * Eigen::Matrix m; + * MTK::subblock(m, &Pose::orient, &Pose::trans) = some_expression; + * MTK::subblock(m, &Pose::trans, &Pose::orient) = some_expression.trans(); + * \endcode + * lets you modify mixed covariance entries in a bigger covariance matrix. + */ +template +typename MTK::internal::CovBlock::Type +subblock(Eigen::Matrix &cov, + MTK::SubManifold Base::*, MTK::SubManifold Base::*) +{ + return cov.template block(idx1, idx2); +} + +template +typename MTK::internal::CovBlock_::Type +subblock_(Eigen::Matrix &cov, + MTK::SubManifold Base::*, MTK::SubManifold Base::*) +{ + return cov.template block(dim1, dim2); +} + +template +typename MTK::internal::CrossCovBlock::Type +subblock(Eigen::Matrix &cov, MTK::SubManifold Base1::*, MTK::SubManifold Base2::*) +{ + return cov.template block(idx1, idx2); +} + +template +typename MTK::internal::CrossCovBlock_::Type +subblock_(Eigen::Matrix &cov, MTK::SubManifold Base1::*, MTK::SubManifold Base2::*) +{ + return cov.template block(dim1, dim2); +} +/** + * Get the subblock of corresponding to a member, i.e. + * \code + * Eigen::Matrix m; + * MTK::subblock(m, &Pose::orient) = some_expression; + * \endcode + * lets you modify covariance entries in a bigger covariance matrix. + */ +template +typename MTK::internal::CovBlock_::Type +subblock_(Eigen::Matrix &cov, + MTK::SubManifold Base::*) +{ + return cov.template block(dim, dim); +} + +template +typename MTK::internal::CovBlock::Type +subblock(Eigen::Matrix &cov, + MTK::SubManifold Base::*) +{ + return cov.template block(idx, idx); +} + +template +class get_cov { +public: + typedef Eigen::Matrix type; + typedef const Eigen::Matrix const_type; +}; + +template +class get_cov_ { +public: + typedef Eigen::Matrix type; + typedef const Eigen::Matrix const_type; +}; + +template +class get_cross_cov { +public: + typedef Eigen::Matrix type; + typedef const type const_type; +}; + +template +class get_cross_cov_ { +public: + typedef Eigen::Matrix type; + typedef const type const_type; +}; + + +template +vectview +subvector_impl_(vectview vec, SubManifold Base::*) +{ + return vec.template segment(dim); +} + +template +vectview +subvector_impl(vectview vec, SubManifold Base::*) +{ + return vec.template segment(idx); +} + +/** + * Get the subvector corresponding to a sub-manifold from a bigger vector. + */ + template +vectview +subvector_(vectview vec, SubManifold Base::* ptr) +{ + return subvector_impl_(vec, ptr); +} + +template +vectview +subvector(vectview vec, SubManifold Base::* ptr) +{ + return subvector_impl(vec, ptr); +} + +/** + * @todo This should be covered already by subvector(vectview vec,SubManifold Base::*) + */ +template +vectview +subvector(Eigen::Matrix& vec, SubManifold Base::* ptr) +{ + return subvector_impl(vectview(vec), ptr); +} + +template +vectview +subvector_(Eigen::Matrix& vec, SubManifold Base::* ptr) +{ + return subvector_impl_(vectview(vec), ptr); +} + +template +vectview +subvector_(const Eigen::Matrix& vec, SubManifold Base::* ptr) +{ + return subvector_impl_(vectview(vec), ptr); +} + +template +vectview +subvector(const Eigen::Matrix& vec, SubManifold Base::* ptr) +{ + return subvector_impl(vectview(vec), ptr); +} + + +/** + * const version of subvector(vectview vec,SubManifold Base::*) + */ +template +vectview +subvector_impl(const vectview cvec, SubManifold Base::*) +{ + return cvec.template segment(idx); +} + +template +vectview +subvector_impl_(const vectview cvec, SubManifold Base::*) +{ + return cvec.template segment(dim); +} + +template +vectview +subvector(const vectview cvec, SubManifold Base::* ptr) +{ + return subvector_impl(cvec, ptr); +} + + +} // namespace MTK + +#endif // GET_START_INDEX_H_ diff --git a/include/IKFoM/IKFoM_toolkit/mtk/types/S2.hpp b/include/IKFoM/IKFoM_toolkit/mtk/types/S2.hpp new file mode 100755 index 0000000..b19f9b0 --- /dev/null +++ b/include/IKFoM/IKFoM_toolkit/mtk/types/S2.hpp @@ -0,0 +1,327 @@ +// This is a NEW implementation of the algorithm described in the +// following paper: +// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. +// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 + +/* + * Copyright (c) 2019--2023, The University of Hong Kong + * All rights reserved. + * + * Modifier: Dongjiao HE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +/* + * Copyright (c) 2008--2011, Universitaet Bremen + * All rights reserved. + * + * Author: Christoph Hertzberg + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ +/** + * @file mtk/types/S2.hpp + * @brief Unit vectors on the sphere, or directions in 3D. + */ +#ifndef S2_H_ +#define S2_H_ + + +#include "vect.hpp" + +#include "SOn.hpp" +#include "../src/mtkmath.hpp" + + + + +namespace MTK { + +/** + * Manifold representation of @f$ S^2 @f$. + * Used for unit vectors on the sphere or directions in 3D. + * + * @todo add conversions from/to polar angles? + */ +template +struct S2 { + + typedef _scalar scalar; + typedef vect<3, scalar> vect_type; + typedef SO3 SO3_type; + typedef typename vect_type::base vec3; + scalar length = scalar(den)/scalar(num); + enum {DOF=2, TYP = 1, DIM = 3}; + +//private: + /** + * Unit vector on the sphere, or vector pointing in a direction + */ + vect_type vec; + +public: + S2() { + if(S2_typ == 3) vec=length * vec3(0, 0, std::sqrt(1)); + if(S2_typ == 2) vec=length * vec3(0, std::sqrt(1), 0); + if(S2_typ == 1) vec=length * vec3(std::sqrt(1), 0, 0); + } + S2(const scalar &x, const scalar &y, const scalar &z) : vec(vec3(x, y, z)) { + vec.normalize(); + vec = vec * length; + } + + S2(const vect_type &_vec) : vec(_vec) { + vec.normalize(); + vec = vec * length; + } + + void oplus(MTK::vectview delta, scalar scale = 1) + { + // SO3_type res; + // res.w() = MTK::exp(res.vec(), delta, scalar(scale/2)); + vec = SO3_type::exp(delta) * vec; // res.normalized().toRotationMatrix() * vec; + } + + void boxplus(MTK::vectview delta, scalar scale=1) { + Eigen::Matrix Bx; + S2_Bx(Bx); + vect_type Bu = Bx*delta; + // SO3_type res; + // res.w() = MTK::exp(res.vec(), Bu, scalar(scale/2)); + vec = SO3_type::exp(delta) * vec; // res.normalized().toRotationMatrix() * vec; + } + + void boxminus(MTK::vectview res, const S2& other) const { + scalar v_sin = (MTK::hat(vec)*other.vec).norm(); + scalar v_cos = vec.transpose() * other.vec; + scalar theta = std::atan2(v_sin, v_cos); + if(v_sin < MTK::tolerance()) + { + if(std::fabs(theta) > MTK::tolerance() ) + { + res[0] = 3.1415926; + res[1] = 0; + } + else{ + res[0] = 0; + res[1] = 0; + } + } + else + { + S2 other_copy = other; + Eigen::MatrixBx; + other_copy.S2_Bx(Bx); + res = theta/v_sin * Bx.transpose() * MTK::hat(other.vec)*vec; + } + } + + void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + + void S2_hat(Eigen::Matrix &res) + { + Eigen::Matrix skew_vec; + skew_vec << scalar(0), -vec[2], vec[1], + vec[2], scalar(0), -vec[0], + -vec[1], vec[0], scalar(0); + res = skew_vec; + } + + + void S2_Bx(Eigen::Matrix &res) + { + if(S2_typ == 3) + { + if(vec[2] + length > tolerance()) + { + + res << length - vec[0]*vec[0]/(length+vec[2]), -vec[0]*vec[1]/(length+vec[2]), + -vec[0]*vec[1]/(length+vec[2]), length-vec[1]*vec[1]/(length+vec[2]), + -vec[0], -vec[1]; + res /= length; + } + else + { + res = Eigen::Matrix::Zero(); + res(1, 1) = -1; + res(2, 0) = 1; + } + } + else if(S2_typ == 2) + { + if(vec[1] + length > tolerance()) + { + + res << length - vec[0]*vec[0]/(length+vec[1]), -vec[0]*vec[2]/(length+vec[1]), + -vec[0], -vec[2], + -vec[0]*vec[2]/(length+vec[1]), length-vec[2]*vec[2]/(length+vec[1]); + res /= length; + } + else + { + res = Eigen::Matrix::Zero(); + res(1, 1) = -1; + res(2, 0) = 1; + } + } + else + { + if(vec[0] + length > tolerance()) + { + + res << -vec[1], -vec[2], + length - vec[1]*vec[1]/(length+vec[0]), -vec[2]*vec[1]/(length+vec[0]), + -vec[2]*vec[1]/(length+vec[0]), length-vec[2]*vec[2]/(length+vec[0]); + res /= length; + } + else + { + res = Eigen::Matrix::Zero(); + res(1, 1) = -1; + res(2, 0) = 1; + } + } + } + + void S2_Nx(Eigen::Matrix &res, S2& subtrahend) + { + if((vec+subtrahend.vec).norm() > tolerance()) + { + Eigen::Matrix Bx; + S2_Bx(Bx); + if((vec-subtrahend.vec).norm() > tolerance()) + { + scalar v_sin = (MTK::hat(vec)*subtrahend.vec).norm(); + scalar v_cos = vec.transpose() * subtrahend.vec; + + res = Bx.transpose() * (std::atan2(v_sin, v_cos)/v_sin*MTK::hat(vec)+MTK::hat(vec)*subtrahend.vec*((-v_cos/v_sin/v_sin/length/length/length/length+std::atan2(v_sin, v_cos)/v_sin/v_sin/v_sin)*subtrahend.vec.transpose()*MTK::hat(vec)*MTK::hat(vec)-vec.transpose()/length/length/length/length)); + } + else + { + res = 1/length/length*Bx.transpose()*MTK::hat(vec); + } + } + else + { + std::cerr << "No N(x, y) for x=-y" << std::endl; + std::exit(100); + } + } + + void S2_Nx_yy(Eigen::Matrix &res) + { + Eigen::Matrix Bx; + S2_Bx(Bx); + res = 1/length/length*Bx.transpose()*MTK::hat(vec); + } + + void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) + { + Eigen::Matrix Bx; + S2_Bx(Bx); + if(delta.norm() < tolerance()) + { + res = -MTK::hat(vec)*Bx; + } + else{ + vect_type Bu = Bx*delta; + // SO3_type exp_delta; + // exp_delta.w() = MTK::exp(exp_delta.vec(), Bu, scalar(1/2)); + res = -SO3_type::exp(Bu)*MTK::hat(vec)*MTK::A_matrix(Bu).transpose()*Bx; // exp_delta.normalized().toRotationMatrix() + } + } + + operator const vect_type&() const{ + return vec; + } + + const vect_type& get_vect() const { + return vec; + } + + friend S2 operator*(const SO3& rot, const S2& dir) + { + S2 ret; + ret.vec = rot * dir.vec; + return ret; + } + + scalar operator[](int idx) const {return vec[idx]; } + + friend std::ostream& operator<<(std::ostream &os, const S2& vec){ + return os << vec.vec.transpose() << " "; + } + friend std::istream& operator>>(std::istream &is, S2& vec){ + for(int i=0; i<3; ++i) + is >> vec.vec[i]; + vec.vec.normalize(); + vec.vec = vec.vec * vec.length; + return is; + + } +}; + + +} // namespace MTK + + +#endif /*S2_H_*/ diff --git a/include/IKFoM/IKFoM_toolkit/mtk/types/SEn.hpp b/include/IKFoM/IKFoM_toolkit/mtk/types/SEn.hpp new file mode 100755 index 0000000..2270136 --- /dev/null +++ b/include/IKFoM/IKFoM_toolkit/mtk/types/SEn.hpp @@ -0,0 +1,334 @@ +// This is an advanced implementation of the algorithm described in the +// following paper: +// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. +// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 + +/* + * Copyright (c) 2019--2023, The University of Hong Kong + * All rights reserved. + * + * Modifier: Dongjiao HE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +/* + * Copyright (c) 2008--2011, Universitaet Bremen + * All rights reserved. + * + * Author: Christoph Hertzberg + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ +/** + * @file mtk/types/SEn.hpp + * @brief Standard Orthogonal Groups i.e.\ rotatation groups. + */ +#ifndef SEN_H_ +#define SEN_H_ + +#include + +#include "SOn.hpp" +#include "vect.hpp" +#include "../src/mtkmath.hpp" + + +namespace MTK { + + +/** + * Three-dimensional orientations represented as Quaternion. + * It is assumed that the internal Quaternion always stays normalized, + * should this not be the case, call inherited member function @c normalize(). + */ +template +struct SEN { + enum {DOF = num_of_vec_plus1, DIM = num_of_vec_plus1, TYP = 4}; + typedef _scalar scalar; + typedef Eigen::Matrix base; + typedef SO3 SO3_type; + // typedef Eigen::Quaternion base; + // typedef Eigen::Quaternion Quaternion; + typedef vect vect_type; + SO3_type SO3_data; + base mat; + + /** + * Construct from real part and three imaginary parts. + * Quaternion is normalized after construction. + */ + // SEN(const base& src) : mat(src) { + // // base::normalize(); + // } + + /** + * Construct from Eigen::Quaternion. + * @note Non-normalized input may result result in spurious behavior. + */ + SEN(const base& src = base::Identity()) : mat(src) {} + + /** + * Construct from rotation matrix. + * @note Invalid rotation matrices may lead to spurious behavior. + */ + // template + // SO3(const Eigen::MatrixBase& matrix) : base(matrix) {} + + /** + * Construct from arbitrary rotation type. + * @note Invalid rotation matrices may lead to spurious behavior. + */ + // template + // SO3(const Eigen::RotationBase& rotation) : base(rotation.derived()) {} + + //! @name Manifold requirements + + void boxplus(MTK::vectview vec, scalar scale=1) { + SEN delta = exp(vec, scale); // ? + mat = mat * delta.mat; + } + void boxminus(MTK::vectview res, const SEN& other) const { + base error_mat = other.mat.inverse() * mat; + res = log(error_mat); + } + //} + + void oplus(MTK::vectview vec, scalar scale=1) { + SEN delta = exp(vec, scale); + mat = mat * delta.mat; + } + + // void hat(MTK::vectview& v, Eigen::Matrix &res) { + void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + res = Eigen::Matrix::Zero(); + Eigen::Matrix psi; + psi << 0, -v[2], v[1], + v[2], 0, -v[0], + -v[1], v[0], 0; + res.block<3, 3>(0, 0) = psi; + for(int i = 3; i < v.size() / 3 + 2; i++) + { + res.block<3, 1>(0, i) = v.segment<3>(i + (i-3)*3); + } + // return res; + } + + // void Jacob_right_inv(MTK::vectview vec, Eigen::Matrix & res){ + void Jacob_right_inv(Eigen::VectorXd& vec, Eigen::MatrixXd &res){ + res = Eigen::Matrix::Zero(); + Eigen::Matrix M_v; + Eigen::VectorXd vec_psi, vec_ro; + Eigen::MatrixXd jac_v; + Eigen::MatrixXd hat_v, hat_ro; + vec_psi = vec.segment<3>(0); + // Eigen::Matrix ; + SO3_data.hat(vec_psi, hat_v); + SO3_data.Jacob_right_inv(vec_psi, jac_v); + double norm = vec_psi.norm(); + for(int i = 0; i < vec.size() / 3; i++) + { + res.block<3, 3>(i*3, i*3) = jac_v; + } + for(int i = 1; i < vec.size() / 3; i++) + { + vec_ro = vec.segment<3>(i * 3); + SO3_data.hat(vec_ro, hat_ro); + if(norm > MTK::tolerance()) + { + res.block<3,3>(i*3, 0) = 0.5 * hat_ro + (1 - norm * std::cos(norm / 2) / 2 / std::sin(norm / 2))/norm/norm * (hat_ro * hat_v + hat_v * hat_ro) + ((2 - norm * std::cos(norm / 2) / 2 / std::sin(norm / 2)) / 2 / norm / norm / norm / norm - 1 / 8 / norm / norm / std::sin(norm / 2) / std::sin(norm / 2)) * hat_v * (hat_ro * hat_v + hat_v * hat_ro) * hat_v; + } + else + { + res.block<3,3>(i*3, 0) = 0.5 * hat_ro; + } + + } + // return res; + } + + // void Jacob_right(MTK::vectview & vec, Eigen::Matrix &res){ + void Jacob_right(Eigen::VectorXd& vec, Eigen::MatrixXd &res){ + res = Eigen::Matrix::Zero(); + Eigen::MatrixXd hat_v, hat_ro; + Eigen::VectorXd vec_psi, vec_ro; + Eigen::MatrixXd jac_v; + vec_psi = vec.segment<3>(0); + // Eigen::Matrix ; + SO3_data.hat(vec_psi, hat_v); + SO3_data.Jacob_right(vec_psi, jac_v); + // double squaredNorm = v[0] * v[0] + v[1] * v[1] + v[2] * v[2]; + // double norm = std::sqrt(squaredNorm); + double norm = vec_psi.norm(); + for(int i = 0; i < vec.size() / 3; i++) + { + res.block<3, 3>(i*3, i*3) = jac_v; + } + for(int i = 1; i < vec.size() / 3; i++) + { + vec_ro = vec.segment<3>(i * 3); + SO3_data.hat(vec_ro, hat_ro); + if(norm > MTK::tolerance()) + { + res.block<3,3>(i*3, 0) = -1 * jac_v * (0.5 * hat_ro + (1 - norm * std::cos(norm / 2) / 2 / std::sin(norm / 2))/norm/norm * (hat_ro * hat_v + hat_v * hat_ro) + ((2 - norm * std::cos(norm / 2) / 2 / std::sin(norm / 2)) / 2 / norm / norm / norm / norm - 1 / 8 / norm / norm / std::sin(norm / 2) / std::sin(norm / 2)) * hat_v * (hat_ro * hat_v + hat_v * hat_ro) * hat_v) * jac_v; + } + else + { + res.block<3,3>(i*3, 0) = -0.5 * jac_v * hat_ro * jac_v; + } + + } + // return res; + } + + void S2_hat(Eigen::Matrix &res) + { + std::cerr << "wrong idx for S2" << std::endl; + res = Eigen::Matrix::Zero(); + } + void S2_Nx_yy(Eigen::Matrix &res) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + friend std::ostream& operator<<(std::ostream &os, const SEN& q){ + for(int i=0; i>(std::istream &is, SEN& q){ + // vect coeffs; + for(int i=0; i> q.mat(i, j); + } + } + // is >> q.mat; + // coeffs; + // q.coeffs() = coeffs.normalized(); + return is; + } + + //! @name Helper functions + //{ + /** + * Calculate the exponential map. In matrix terms this would correspond + * to the Rodrigues formula. + */ + // FIXME vectview<> can't be constructed from every MatrixBase<>, use const Vector3x& as workaround +// static SO3 exp(MTK::vectview dvec, scalar scale = 1){ + static SEN exp(const Eigen::Matrix& dvec, scalar scale = 1){ + SEN res; + res.mat = Eigen::Matrix::Identity(); + Eigen::Matrix exp_; //, jac; + Eigen::MatrixXd jac; + Eigen::Matrix psi; + Eigen::VectorXd minus_psi; + psi = dvec.template block<3,1>(0, 0); + minus_psi = -psi; + SO3_type SO3_temp; + exp_ = SO3_type::exp(psi); + SO3_temp.Jacob_right(minus_psi, jac); + res.mat.template block<3,3>(0, 0) = exp_; + for(int i = 3; i < DOF / 3 + 2; i++) + { + res.mat.template block<3, 1>(0, i) = jac * dvec.template block<3,1>(i + (i-3)*3,0); + } + return res; + } + /** + * Calculate the inverse of @c exp. + * Only guarantees that exp(log(x)) == x + */ + static Eigen::Matrix log(base &orient){ + Eigen::Matrix res; + Eigen::Matrix psi; + Eigen::VectorXd minus_psi; + Eigen::Matrix mat_psi; + Eigen::MatrixXd jac; + mat_psi = orient.template block<3, 3>(0, 0); + SO3_type SO3_temp; + SO3_type exp_psi(mat_psi); + psi = SO3_type::log(exp_psi); + minus_psi = -psi; + SO3_temp.Jacob_right_inv(minus_psi, jac); + for(int i = 3; i < dim_of_mat; i++) + { + res.template block<3,1>(i + (i-3)*3,0) = jac * orient.template block<3, 1>(0, i); + } + return res; + } +}; + + +} // namespace MTK + +#endif /*SON_H_*/ + diff --git a/include/IKFoM/IKFoM_toolkit/mtk/types/SOn.hpp b/include/IKFoM/IKFoM_toolkit/mtk/types/SOn.hpp new file mode 100755 index 0000000..155ecf7 --- /dev/null +++ b/include/IKFoM/IKFoM_toolkit/mtk/types/SOn.hpp @@ -0,0 +1,361 @@ +// This is an advanced implementation of the algorithm described in the +// following paper: +// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. +// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 + +/* + * Copyright (c) 2019--2023, The University of Hong Kong + * All rights reserved. + * + * Modifier: Dongjiao HE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +/* + * Copyright (c) 2008--2011, Universitaet Bremen + * All rights reserved. + * + * Author: Christoph Hertzberg + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ +/** + * @file mtk/types/SOn.hpp + * @brief Standard Orthogonal Groups i.e.\ rotatation groups. + */ +#ifndef SON_H_ +#define SON_H_ + +#include + +#include "vect.hpp" +#include "../src/mtkmath.hpp" + + +namespace MTK { + + +/** + * Two-dimensional orientations represented as scalar. + * There is no guarantee that the representing scalar is within any interval, + * but the result of boxminus will always have magnitude @f$\le\pi @f$. + */ +template +struct SO2 : public Eigen::Rotation2D<_scalar> { + enum {DOF = 1, DIM = 2, TYP = 3}; + + typedef _scalar scalar; + typedef Eigen::Rotation2D base; + typedef vect vect_type; + + //! Construct from angle + SO2(const scalar& angle = 0) : base(angle) { } + + //! Construct from Eigen::Rotation2D + SO2(const base& src) : base(src) {} + + /** + * Construct from 2D vector. + * Resulting orientation will rotate the first unit vector to point to vec. + */ + SO2(const vect_type &vec) : base(atan2(vec[1], vec[0])) {}; + + + //! Calculate @c this->inverse() * @c r + SO2 operator%(const base &r) const { + return base::inverse() * r; + } + + //! Calculate @c this->inverse() * @c r + template + vect_type operator%(const Eigen::MatrixBase &vec) const { + return base::inverse() * vec; + } + + //! Calculate @c *this * @c r.inverse() + SO2 operator/(const SO2 &r) const { + return *this * r.inverse(); + } + + //! Gets the angle as scalar. + operator scalar() const { + return base::angle(); + } + void S2_hat(Eigen::Matrix &res) + { + res = Eigen::Matrix::Zero(); + } + //! @name Manifold requirements + void S2_Nx_yy(Eigen::Matrix &res) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + void oplus(MTK::vectview vec, scalar scale = 1) { + base::angle() += scale * vec[0]; + } + + void boxplus(MTK::vectview vec, scalar scale = 1) { + base::angle() += scale * vec[0]; + } + void boxminus(MTK::vectview res, const SO2& other) const { + res[0] = MTK::normalize(base::angle() - other.angle(), scalar(MTK::pi)); + } + + friend std::istream& operator>>(std::istream &is, SO2& ang){ + return is >> ang.angle(); + } + void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } +}; + + +/** + * Three-dimensional orientations represented as Quaternion. + * It is assumed that the internal Quaternion always stays normalized, + * should this not be the case, call inherited member function @c normalize(). + */ +template //, int Options = Eigen::AutoAlign> +struct SO3 : public Eigen::Matrix<_scalar, 3, 3> { + enum {DOF = 3, DIM = 3, TYP = 2}; + typedef _scalar scalar; + typedef Eigen::Matrix base; + typedef Eigen::Matrix Matrix; + typedef vect vect_type; + + /** + * Construct from Eigen::Quaternion. + * @note Non-normalized input may result result in spurious behavior. + */ + SO3(const base& src = base::Identity()) : base(src) {} + + /** + * Construct from rotation matrix. + * @note Invalid rotation matrices may lead to spurious behavior. + */ + template + SO3(const Eigen::MatrixBase& matrix) : base(matrix) {} + + /** + * Construct from arbitrary rotation type. + * @note Invalid rotation matrices may lead to spurious behavior. + */ + // template + // SO3(const Eigen::RotationBase& rotation) : base(rotation.derived()) {} + + //! @name Manifold requirements + + // SO3 operator=(const base &r) const { + // return r; + // } + + // //! Calculate @c *this * @c r.inverse() + // SO3 operator*(const SO3 &r) const { + // return *this * r; + // } + + void boxplus(MTK::vectview vec, scalar scale=1) { + SO3 delta = exp(vec, scale); + *this = *this * delta; + } + void boxminus(MTK::vectview res, const SO3& other) const { + res = SO3::log(other.transpose() * *this); + } + //} + + void oplus(MTK::vectview vec, scalar scale=1) { + SO3 delta = exp(vec, scale); + *this = *this * delta; + } + + // void hat(MTK::vectview& v, Eigen::Matrix &res) { + void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + // Eigen::Matrix res; + res << 0, -v[2], v[1], + v[2], 0, -v[0], + -v[1], v[0], 0; + // return res; + } + + // void Jacob_right_inv(MTK::vectview vec, Eigen::Matrix & res){ + void Jacob_right_inv(Eigen::VectorXd& vec, Eigen::MatrixXd &res){ + Eigen::MatrixXd hat_v; + hat(vec, hat_v); + if(vec.norm() > MTK::tolerance()) + { + res = Eigen::Matrix::Identity() + 0.5 * hat_v + (1 - vec.norm() * std::cos(vec.norm() / 2) / 2 / std::sin(vec.norm() / 2)) * hat_v * hat_v / vec.squaredNorm(); + } + else + { + res = Eigen::Matrix::Identity(); + } + // return res; + } + + // void Jacob_right(MTK::vectview & v, Eigen::Matrix &res){ + void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res){ + Eigen::MatrixXd hat_v; + hat(v, hat_v); + double squaredNorm = v[0] * v[0] + v[1] * v[1] + v[2] * v[2]; + double norm = std::sqrt(squaredNorm); + if(norm < MTK::tolerance()){ + res = Eigen::Matrix::Identity(); + } + else{ + res = Eigen::Matrix::Identity() - (1 - std::cos(norm)) / squaredNorm * hat_v + (1 - std::sin(norm) / norm) / squaredNorm * hat_v * hat_v; + } + // return res; + } + + void S2_hat(Eigen::Matrix &res) + { + res = Eigen::Matrix::Zero(); + } + void S2_Nx_yy(Eigen::Matrix &res) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + friend std::ostream& operator<<(std::ostream &os, const SO3& q){ // wrong! + return os << q.data() << " "; + } + + friend std::istream& operator>>(std::istream &is, SO3& q){ // wrong! + SO3 coeffs; + is >> coeffs; + q = coeffs; + return is; + } + + //! @name Helper functions + //{ + /** + * Calculate the exponential map. In matrix terms this would correspond + * to the Rodrigues formula. + */ + // FIXME vectview<> can't be constructed from every MatrixBase<>, use const Vector3x& as workaround +// static SO3 exp(MTK::vectview dvec, scalar scale = 1){ + static SO3 exp(const Eigen::Matrix& dvec, scalar scale = 1){ + Eigen::Matrix ang = dvec * scale; + double ang_norm = ang.norm(); + Eigen::Matrix Eye3 = Eigen::Matrix::Identity(); + if (ang_norm > 0.0000001) + { + Eigen::Matrix r_axis = ang / ang_norm; + Eigen::Matrix K; + K << 0.0, -r_axis(2), r_axis(1), + r_axis(2), 0.0, -r_axis(0), + -r_axis(1), r_axis(0), 0.0; // SKEW_SYM_MATRX(r_axis); + /// Roderigous Tranformation + return Eye3 + std::sin(ang_norm) * K + (1.0 - std::cos(ang_norm)) * K * K; + } + else + { + return Eye3; + } + } + /** + * Calculate the inverse of @c exp. + * Only guarantees that exp(log(x)) == x + */ + static Eigen::Vector3d log(const SO3 &orient){ + double theta = (orient.trace() > 3.0 - 1e-6) ? 0.0 : std::acos(0.5 * (orient.trace() - 1)); + Eigen::Matrix K(orient(2,1) - orient(1,2), orient(0,2) - orient(2,0), orient(1,0) - orient(0,1)); + return (std::abs(theta) < 0.001) ? (0.5 * K) : (0.5 * theta / std::sin(theta) * K); + } +}; + +namespace internal { +template +struct UnalignedType >{ + typedef SO2 type; +}; + +template +struct UnalignedType >{ + typedef SO3 type; +}; + +} // namespace internal + + +} // namespace MTK + +#endif /*SON_H_*/ + diff --git a/include/IKFoM/IKFoM_toolkit/mtk/types/vect.hpp b/include/IKFoM/IKFoM_toolkit/mtk/types/vect.hpp new file mode 100755 index 0000000..d75e6d7 --- /dev/null +++ b/include/IKFoM/IKFoM_toolkit/mtk/types/vect.hpp @@ -0,0 +1,511 @@ +// This is an advanced implementation of the algorithm described in the +// following paper: +// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. +// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119 + +/* + * Copyright (c) 2019--2023, The University of Hong Kong + * All rights reserved. + * + * Modifier: Dongjiao HE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +/* + * Copyright (c) 2008--2011, Universitaet Bremen + * All rights reserved. + * + * Author: Christoph Hertzberg + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ +/** + * @file mtk/types/vect.hpp + * @brief Basic vectors interpreted as manifolds. + * + * This file also implements a simple wrapper for matrices, for arbitrary scalars + * and for positive scalars. + */ +#ifndef VECT_H_ +#define VECT_H_ + +#include +#include +#include + +#include "../src/vectview.hpp" + +namespace MTK { + +static const Eigen::IOFormat IO_no_spaces(Eigen::StreamPrecision, Eigen::DontAlignCols, ",", ",", "", "", "[", "]"); + + +/** + * A simple vector class. + * Implementation is basically a wrapper around Eigen::Matrix with manifold + * requirements added. + */ +template +struct vect : public Eigen::Matrix<_scalar, D, 1, _Options> { + typedef Eigen::Matrix<_scalar, D, 1, _Options> base; + enum {DOF = D, DIM = D, TYP = 0}; + typedef _scalar scalar; + + //using base::operator=; + + /** Standard constructor. Sets all values to zero. */ + vect(const base &src = base::Zero()) : base(src) {} + + /** Constructor copying the value of the expression \a other */ + template + EIGEN_STRONG_INLINE vect(const Eigen::DenseBase& other) : base(other) {} + + /** Construct from memory. */ + vect(const scalar* src, int size = DOF) : base(base::Map(src, size)) { } + + void boxplus(MTK::vectview vec, scalar scale=1) { + *this += scale * vec; + } + void boxminus(MTK::vectview res, const vect& other) const { + res = *this - other; + } + + void oplus(MTK::vectview vec, scalar scale=1) { + *this += scale * vec; + } + + void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + + void S2_hat(Eigen::Matrix &res) + { + res = Eigen::Matrix::Zero(); + } + + void S2_Nx_yy(Eigen::Matrix &res) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + friend std::ostream& operator<<(std::ostream &os, const vect& v){ + // Eigen sometimes messes with the streams flags, so output manually: + for(int i=0; i>(std::istream &is, vect& v){ + char term=0; + is >> std::ws; // skip whitespace + switch(is.peek()) { + case '(': term=')'; is.ignore(1); break; + case '[': term=']'; is.ignore(1); break; + case '{': term='}'; is.ignore(1); break; + default: break; + } + if(D==Eigen::Dynamic) { + assert(term !=0 && "Dynamic vectors must be embraced"); + std::vector temp; + while(is.good() && is.peek() != term) { + scalar x; + is >> x; + temp.push_back(x); + if(is.peek()==',') is.ignore(1); + } + v = vect::Map(temp.data(), temp.size()); + } else + for(int i=0; i> v[i]; + if(is.peek()==',') { // ignore commas between values + is.ignore(1); + } + } + if(term!=0) { + char x; + is >> x; + if(x!=term) { + is.setstate(is.badbit); +// assert(x==term && "start and end bracket do not match!"); + } + } + return is; + } + + template + vectview tail(){ + BOOST_STATIC_ASSERT(0< dim && dim <= DOF); + return base::template tail(); + } + template + vectview tail() const{ + BOOST_STATIC_ASSERT(0< dim && dim <= DOF); + return base::template tail(); + } + template + vectview head(){ + BOOST_STATIC_ASSERT(0< dim && dim <= DOF); + return base::template head(); + } + template + vectview head() const{ + BOOST_STATIC_ASSERT(0< dim && dim <= DOF); + return base::template head(); + } +}; + + +/** + * A simple matrix class. + * Implementation is basically a wrapper around Eigen::Matrix with manifold + * requirements added, i.e., matrix is viewed as a plain vector for that. + */ +template::Options> +struct matrix : public Eigen::Matrix<_scalar, M, N, _Options> { + typedef Eigen::Matrix<_scalar, M, N, _Options> base; + enum {DOF = M * N, TYP = 4, DIM=0}; + typedef _scalar scalar; + + using base::operator=; + + /** Standard constructor. Sets all values to zero. */ + matrix() { + base::setZero(); + } + + /** Constructor copying the value of the expression \a other */ + template + EIGEN_STRONG_INLINE matrix(const Eigen::MatrixBase& other) : base(other) {} + + /** Construct from memory. */ + matrix(const scalar* src) : base(src) { } + + void boxplus(MTK::vectview vec, scalar scale = 1) { + *this += scale * base::Map(vec.data()); + } + void boxminus(MTK::vectview res, const matrix& other) const { + base::Map(res.data()) = *this - other; + } + + void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + + void S2_hat(Eigen::Matrix &res) + { + res = Eigen::Matrix::Zero(); + } + + void oplus(MTK::vectview vec, scalar scale = 1) { + *this += scale * base::Map(vec.data()); + } + + void S2_Nx_yy(Eigen::Matrix &res) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + friend std::ostream& operator<<(std::ostream &os, const matrix& mat){ + for(int i=0; i>(std::istream &is, matrix& mat){ + for(int i=0; i> mat.data()[i]; + } + return is; + } +};// @todo What if M / N = Eigen::Dynamic? + + + +/** + * A simple scalar type. + */ +template +struct Scalar { + enum {DOF = 1, TYP = 5, DIM=0}; + typedef _scalar scalar; + + scalar value; + + Scalar(const scalar& value = scalar(0)) : value(value) {} + operator const scalar&() const { return value; } + operator scalar&() { return value; } + Scalar& operator=(const scalar& val) { value = val; return *this; } + + void S2_hat(Eigen::Matrix &res) + { + res = Eigen::Matrix::Zero(); + } + + void S2_Nx_yy(Eigen::Matrix &res) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + void oplus(MTK::vectview vec, scalar scale=1) { + value += scale * vec[0]; + } + + void boxplus(MTK::vectview vec, scalar scale=1) { + value += scale * vec[0]; + } + void boxminus(MTK::vectview res, const Scalar& other) const { + res[0] = *this - other; + } + + void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } +}; + +/** + * Positive scalars. + * Boxplus is implemented using multiplication by @f$x\boxplus\delta = x\cdot\exp(\delta) @f$. + */ +template +struct PositiveScalar { + enum {DOF = 1, TYP = 6, DIM=0}; + typedef _scalar scalar; + + scalar value; + + PositiveScalar(const scalar& value = scalar(1)) : value(value) { + assert(value > scalar(0)); + } + operator const scalar&() const { return value; } + PositiveScalar& operator=(const scalar& val) { assert(val>0); value = val; return *this; } + + void boxplus(MTK::vectview vec, scalar scale = 1) { + value *= std::exp(scale * vec[0]); + } + void boxminus(MTK::vectview res, const PositiveScalar& other) const { + res[0] = std::log(*this / other); + } + + void oplus(MTK::vectview vec, scalar scale = 1) { + value *= std::exp(scale * vec[0]); + } + + void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + + void S2_hat(Eigen::Matrix &res) + { + res = Eigen::Matrix::Zero(); + } + + void S2_Nx_yy(Eigen::Matrix &res) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + + friend std::istream& operator>>(std::istream &is, PositiveScalar& s){ + is >> s.value; + assert(s.value > 0); + return is; + } +}; + +template +struct Complex : public std::complex<_scalar>{ + enum {DOF = 2, TYP = 7, DIM=0}; + typedef _scalar scalar; + + typedef std::complex Base; + + Complex(const Base& value) : Base(value) {} + Complex(const scalar& re = 0.0, const scalar& im = 0.0) : Base(re, im) {} + Complex(const MTK::vectview &in) : Base(in[0], in[1]) {} + template + Complex(const Eigen::DenseBase &in) : Base(in[0], in[1]) {} + + void boxplus(MTK::vectview vec, scalar scale = 1) { + Base::real() += scale * vec[0]; + Base::imag() += scale * vec[1]; + }; + void boxminus(MTK::vectview res, const Complex& other) const { + Complex diff = *this - other; + res << diff.real(), diff.imag(); + } + + void S2_hat(Eigen::Matrix &res) + { + res = Eigen::Matrix::Zero(); + } + + void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) { + std::cout << "wrong idx" << std::endl; + } + + void oplus(MTK::vectview vec, scalar scale = 1) { + Base::real() += scale * vec[0]; + Base::imag() += scale * vec[1]; + }; + + void S2_Nx_yy(Eigen::Matrix &res) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + void S2_Mx(Eigen::Matrix &res, MTK::vectview delta) + { + std::cerr << "wrong idx for S2" << std::endl; + std::exit(100); + res = Eigen::Matrix::Zero(); + } + + scalar squaredNorm() const { + return std::pow(Base::real(),2) + std::pow(Base::imag(),2); + } + + const scalar& operator()(int i) const { + assert(0<=i && i<2 && "Index out of range"); + return i==0 ? Base::real() : Base::imag(); + } + scalar& operator()(int i){ + assert(0<=i && i<2 && "Index out of range"); + return i==0 ? Base::real() : Base::imag(); + } +}; + + +namespace internal { + +template +struct UnalignedType >{ + typedef vect type; +}; + +} // namespace internal + + +} // namespace MTK + + + + +#endif /*VECT_H_*/ diff --git a/include/IKFoM/IKFoM_toolkit/mtk/types/wrapped_cv_mat.hpp b/include/IKFoM/IKFoM_toolkit/mtk/types/wrapped_cv_mat.hpp new file mode 100755 index 0000000..b6643f1 --- /dev/null +++ b/include/IKFoM/IKFoM_toolkit/mtk/types/wrapped_cv_mat.hpp @@ -0,0 +1,113 @@ +/* + * Copyright (c) 2010--2011, Universitaet Bremen and DFKI GmbH + * All rights reserved. + * + * Author: Rene Wagner + * Christoph Hertzberg + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following + * disclaimer in the documentation and/or other materials provided + * with the distribution. + * * Neither the name of the Universitaet Bremen nor the DFKI GmbH + * nor the names of its contributors may be used to endorse or + * promote products derived from this software without specific + * prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS + * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, + * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN + * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#ifndef WRAPPED_CV_MAT_HPP_ +#define WRAPPED_CV_MAT_HPP_ + +#include +#include + +namespace MTK { + +template +struct cv_f_type; + +template<> +struct cv_f_type +{ + enum {value = CV_64F}; +}; + +template<> +struct cv_f_type +{ + enum {value = CV_32F}; +}; + +/** + * cv_mat wraps a CvMat around an Eigen Matrix + */ +template +class cv_mat : public matrix +{ + typedef matrix base_type; + enum {type_ = cv_f_type::value}; + CvMat cv_mat_; + +public: + cv_mat() + { + cv_mat_ = cvMat(rows, cols, type_, base_type::data()); + } + + cv_mat(const cv_mat& oth) : base_type(oth) + { + cv_mat_ = cvMat(rows, cols, type_, base_type::data()); + } + + template + cv_mat(const Eigen::MatrixBase &value) : base_type(value) + { + cv_mat_ = cvMat(rows, cols, type_, base_type::data()); + } + + template + cv_mat& operator=(const Eigen::MatrixBase &value) + { + base_type::operator=(value); + return *this; + } + + cv_mat& operator=(const cv_mat& value) + { + base_type::operator=(value); + return *this; + } + + // FIXME: Maybe overloading operator& is not a good idea ... + CvMat* operator&() + { + return &cv_mat_; + } + const CvMat* operator&() const + { + return &cv_mat_; + } +}; + +} // namespace MTK + +#endif /* WRAPPED_CV_MAT_HPP_ */ diff --git a/include/IKFoM/LICENSE b/include/IKFoM/LICENSE new file mode 100644 index 0000000..d159169 --- /dev/null +++ b/include/IKFoM/LICENSE @@ -0,0 +1,339 @@ + GNU GENERAL PUBLIC LICENSE + Version 2, June 1991 + + Copyright (C) 1989, 1991 Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The licenses for most software are designed to take away your +freedom to share and change it. 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If this is what you want to do, use the GNU Lesser General +Public License instead of this License. diff --git a/include/IKFoM/README.md b/include/IKFoM/README.md new file mode 100644 index 0000000..53a7dc5 --- /dev/null +++ b/include/IKFoM/README.md @@ -0,0 +1,489 @@ +## IKFoM +**IKFoM** (Iterated Kalman Filters on Manifolds) is a computationally efficient and convenient toolkit for deploying iterated Kalman filters on various robotic systems, especially systems operating on high-dimension manifold. It implements a manifold-embedding Kalman filter which separates the manifold structures from system descriptions and is able to be used by only defining the system in a canonical form and calling the respective steps accordingly. The current implementation supports the full iterated Kalman filtering for systems on manifold and any of its sub-manifolds, and it is extendable to other types of manifold when necessary. + + +**Developers** + +[Dongjiao He](https://github.com/Joanna-HE) + +**Our related video**: https://youtu.be/sz_ZlDkl6fA + +## 1. Prerequisites + +### 1.1. **Eigen && Boost** +Eigen >= 3.3.4, Follow [Eigen Installation](http://eigen.tuxfamily.org/index.php?title=Main_Page). + +Boost >= 1.65. + +## 2. Usage when the measurement is of constant dimension and type. +Clone the repository: + +``` + git clone https://github.com/hku-mars/IKFoM.git +``` + +1. include the necessary head file: +``` +#include +``` +2. Select and instantiate the primitive manifolds: +``` + typedef MTK::SO3 SO3; // scalar type of variable: double + typedef MTK::vect<3, double> vect3; // dimension of the defined Euclidean variable: 3 + typedef MTK::S2 S2; // length of the S2 variable: 98/10; choose e1 as the original point of rotation: 1 +``` +3. Build system state, input and measurement as compound manifolds which are composed of the primitive manifolds: +``` +MTK_BUILD_MANIFOLD(state, // name of compound manifold: state +((vect3, pos)) // ((primitive manifold type, name of variable)) +((vect3, vel)) +((SO3, rot)) +((vect3, bg)) +((vect3, ba)) +((S2, grav)) +((SO3, offset_R_L_I)) +((vect3, offset_T_L_I)) +); +``` +4. Implement the vector field that is defined as , and its differentiation , , where w=0 could be left out: +``` +Eigen::Matrix f(state &s, const input &i) { + Eigen::Matrix res = Eigen::Matrix::Zero(); + res(0) = s.vel[0]; + res(1) = s.vel[1]; + res(2) = s.vel[2]; + return res; +} +Eigen::Matrix df_dx(state &s, const input &i) //notice S2 has length of 3 and dimension of 2 { + Eigen::Matrix cov = Eigen::Matrix::Zero(); + cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity(); + return cov; +} +Eigen::Matrix df_dw(state &s, const input &i) { + Eigen::Matrix cov = Eigen::Matrix::Zero(); + cov.template block<3, 3>(12, 3) = -s.rot.toRotationMatrix(); + return cov; +} +``` +Those functions would be called during the ekf state predict + +5. Implement the output equation and its differentiation , : +``` +measurement h(state &s, bool &valid) // the iteration stops before convergence whenever the user set valid as false +{ + if (condition){ valid = false; + } // other conditions could be used to stop the ekf update iteration before convergence, otherwise the iteration will not stop until the condition of convergence is satisfied. + measurement h_; + h_.position = s.pos; + return h_; +} +Eigen::Matrix dh_dx(state &s) {} +Eigen::Matrix dh_dv(state &s) {} +``` +Those functions would be called during the ekf state update + +6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance. + +(1) initial state and covariance: +``` +state init_state; +esekfom::esekf::cov init_P; +esekfom::esekf kf(init_state,init_P); +``` +(2) default state and covariance: +``` +esekfom::esekf kf; +``` +where **process_noise_dof** is the dimension of process noise, with the type of std int, and so for **measurement_noise_dof**. + +7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object: +``` +double epsi[state_dof] = {0.001}; +fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged +kf.init(f, df_dx, df_dw, h, dh_dx, dh_dv, Maximum_iter, epsi); +``` +8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed: +``` +kf.predict(dt, Q, in); // process noise covariance: Q, an Eigen matrix +``` +9. Once a measurement **z** is received, an iterated update is executed: +``` +kf.update_iterated(z, R); // measurement noise covariance: R, an Eigen matrix +``` +*Remarks(1):* +- We also combine the output equation and its differentiation into an union function, whose usage is the same as the above steps 1-4, and steps 5-9 are shown as follows. + +5. Implement the output equation and its differentiation , : +``` +measurement h_share(state &s, esekfom::share_datastruct &share_data) +{ + if(share_data.converge) {} // this value is true means iteration is converged + if(condition) share_data.valid = false; // the iteration stops before convergence when this value is false if other conditions are satified + share_data.h_x = H_x; // H_x is the result matrix of the first differentiation + share_data.h_v = H_v; // H_v is the result matrix of the second differentiation + share_data.R = R; // R is the measurement noise covariance + share_data.z = z; // z is the obtained measurement + + measurement h_; + h_.position = s.pos; + return h_; +} +``` +This function would be called during ekf state update, and the output function and its derivatives, the measurement and the measurement noise would be obtained from this one union function + +6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance. + +(1) initial state and covariance: +``` +state init_state; +esekfom::esekf::cov init_P; +esekfom::esekf kf(init_state,init_P); +``` +(2) default state and covariance: +``` +esekfom::esekf kf; +``` +7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object: +``` +double epsi[state_dof] = {0.001}; +fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged +kf.init_share(f, df_dx, df_dw, h_share, Maximum_iter, epsi); +``` +8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed: +``` +kf.predict(dt, Q, in); // process noise covariance: Q +``` +9. Once a measurement **z** is received, an iterated update is executed: +``` +kf.update_iterated_share(); +``` + +*Remarks(2):* +- The value of the state **x** and the covariance **P** are able to be changed by functions **change_x()** and **change_P()**: +``` +state set_x; +kf.change_x(set_x); +esekfom::esekf::cov set_P; +kf.change_P(set_P); +``` + +## 3. Usage when the measurement is an Eigen vector of changing dimension. + +Clone the repository: + +``` + git clone https://github.com/hku-mars/IKFoM.git +``` + +1. include the necessary head file: +``` +#include +``` +2. Select and instantiate the primitive manifolds: +``` + typedef MTK::SO3 SO3; // scalar type of variable: double + typedef MTK::vect<3, double> vect3; // dimension of the defined Euclidean variable: 3 + typedef MTK::S2 S2; // length of the S2 variable: 98/10; choose e1 as the original point of rotation: 1 +``` +3. Build system state and input as compound manifolds which are composed of the primitive manifolds: +``` +MTK_BUILD_MANIFOLD(state, // name of compound manifold: state +((vect3, pos)) // ((primitive manifold type, name of variable)) +((vect3, vel)) +((SO3, rot)) +((vect3, bg)) +((vect3, ba)) +((S2, grav)) +((SO3, offset_R_L_I)) +((vect3, offset_T_L_I)) +); +``` +4. Implement the vector field that is defined as , and its differentiation , , where w=0 could be left out: +``` +Eigen::Matrix f(state &s, const input &i) { + Eigen::Matrix res = Eigen::Matrix::Zero(); + res(0) = s.vel[0]; + res(1) = s.vel[1]; + res(2) = s.vel[2]; + return res; +} +Eigen::Matrix df_dx(state &s, const input &i) //notice S2 has length of 3 and dimension of 2 { + Eigen::Matrix cov = Eigen::Matrix::Zero(); + cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity(); + return cov; +} +Eigen::Matrix df_dw(state &s, const input &i) { + Eigen::Matrix cov = Eigen::Matrix::Zero(); + cov.template block<3, 3>(12, 3) = -s.rot.toRotationMatrix(); + return cov; +} +``` +Those functions would be called during ekf state predict + +5. Implement the output equation and its differentiation , : +``` +Eigen::Matrix h(state &s, bool &valid) //the iteration stops before convergence when valid is false { + if (condition){ valid = false; + } // other conditions could be used to stop the ekf update iteration before convergence, otherwise the iteration will not stop until the condition of convergence is satisfied. + Eigen::Matrix h_; + h_(0) = s.pos[0]; + return h_; +} +Eigen::Matrix dh_dx(state &s) {} +Eigen::Matrix dh_dv(state &s) {} +``` +Those functions would be called during ekf state update + +6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance. + +(1) initial state and covariance: +``` +state init_state; +esekfom::esekf::cov init_P; +esekfom::esekf kf(init_state,init_P); +``` +(2) default state and covariance: +``` +esekfom::esekf kf; +``` +where **process_noise_dof** is the dimension of process noise, with the type of std int, and so for **measurement_noise_dof** + +7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object: +``` +double epsi[state_dof] = {0.001}; +fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged +kf.init_dyn(f, df_dx, df_dw, h, dh_dx, dh_dv, Maximum_iter, epsi); +``` +8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed: +``` +kf.predict(dt, Q, in); // process noise covariance: Q, an Eigen matrix +``` +9. Once a measurement **z** is received, an iterated update is executed: +``` +kf.update_iterated_dyn(z, R); // measurement noise covariance: R, an Eigen matrix +``` +*Remarks(1):* +- We also combine the output equation and its differentiation into an union function, whose usage is the same as the above steps 1-4, and steps 5-9 are shown as follows. +5. Implement the output equation and its differentiation , : +``` +Eigen::Matrix h_dyn_share(state &s, esekfom::dyn_share_datastruct &dyn_share_data) +{ + if(dyn_share_data.converge) {} // this value is true means iteration is converged + if(condition) share_data.valid = false; // the iteration stops before convergence when this value is false if other conditions are satified + dyn_share_data.h_x = H_x; // H_x is the result matrix of the first differentiation + dyn_share_data.h_v = H_v; // H_v is the result matrix of the second differentiation + dyn_share_data.R = R; // R is the measurement noise covariance + dyn_share_data.z = z; // z is the obtained measurement + + Eigen::Matrix h_; + h_(0) = s.pos[0]; + return h_; +} +This function would be called during ekf state update, and the output function and its derivatives, the measurement and the measurement noise would be obtained from this one union function +``` +6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance. +(1) initial state and covariance: +``` +state init_state; +esekfom::esekf::cov init_P; +esekfom::esekf kf(init_state,init_P); +``` +(2) default state and covariance: +``` +esekfom::esekf kf; +``` +7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object: +``` +double epsi[state_dof] = {0.001}; +fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged +kf.init_dyn_share(f, df_dx, df_dw, h_dyn_share, Maximum_iter, epsi); +``` +8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed: +``` +kf.predict(dt, Q, in); // process noise covariance: Q, an Eigen matrix +``` +9. Once a measurement **z** is received, an iterated update is executed: +``` +kf.update_iterated_dyn_share(); +``` + +*Remarks(2):* +- The value of the state **x** and the covariance **P** are able to be changed by functions **change_x()** and **change_P()**: +``` +state set_x; +kf.change_x(set_x); +esekfom::esekf::cov set_P; +kf.change_P(set_P); +``` + +## 4. Usage when the measurement is a changing manifold during the run time. + +Clone the repository: + +``` + git clone https://github.com/hku-mars/IKFoM.git +``` + +1. include the necessary head file: +``` +#include +``` +2. Select and instantiate the primitive manifolds: +``` + typedef MTK::SO3 SO3; // scalar type of variable: double + typedef MTK::vect<3, double> vect3; // dimension of the defined Euclidean variable: 3 + typedef MTK::S2 S2; // length of the S2 variable: 98/10; choose e1 as the original point of rotation: 1 +``` +3. Build system state and input as compound manifolds which are composed of the primitive manifolds: +``` +MTK_BUILD_MANIFOLD(state, // name of compound manifold: state +((vect3, pos)) // ((primitive manifold type, name of variable)) +((vect3, vel)) +((SO3, rot)) +((vect3, bg)) +((vect3, ba)) +((S2, grav)) +((SO3, offset_R_L_I)) +((vect3, offset_T_L_I)) +); +``` +4. Implement the vector field that is defined as , and its differentiation , , where w=0 could be left out: +``` +Eigen::Matrix f(state &s, const input &i) { + Eigen::Matrix res = Eigen::Matrix::Zero(); + res(0) = s.vel[0]; + res(1) = s.vel[1]; + res(2) = s.vel[2]; + return res; +} +Eigen::Matrix df_dx(state &s, const input &i) //notice S2 has length of 3 and dimension of 2 { + Eigen::Matrix cov = Eigen::Matrix::Zero(); + cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity(); + return cov; +} +Eigen::Matrix df_dw(state &s, const input &i) { + Eigen::Matrix cov = Eigen::Matrix::Zero(); + cov.template block<3, 3>(12, 3) = -s.rot.toRotationMatrix(); + return cov; +} +``` +Those functions would be called during ekf state predict + +5. Implement the differentiation of the output equation , : +``` +Eigen::Matrix dh_dx(state &s, bool &valid) {} //the iteration stops before convergence when valid is false +Eigen::Matrix dh_dv(state &s, bool &valid) {} +``` +Those functions would be called during ekf state update + +6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance. + +(1) initial state and covariance: +``` +state init_state; +esekfom::esekf::cov init_P; +esekfom::esekf kf(init_state,init_P); +``` +(2) +``` +esekfom::esekf kf; +``` +Where **process_noise_dof** is the dimension of process noise, of type of std int + +7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object: +``` +double epsi[state_dof] = {0.001}; +fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged +kf.init_dyn_runtime(f, df_dx, df_dw, dh_dx, dh_dv, Maximum_iter, epsi); +``` +8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed: +``` +kf.predict(dt, Q, in); // process noise covariance: Q +``` +9. Once a measurement **z** is received, build system measurement as compound manifolds following step 3 and implement the output equation : +``` +measurement h(state &s, bool &valid) //the iteration stops before convergence when valid is false +{ + if (condition) valid = false; // the update iteration could be stopped when the condition other than convergence is satisfied + measurement h_; + h_.pos = s.pos; + return h_; +} +``` +then an iterated update is executed: +``` +kf.update_iterated_dyn_runtime(z, R, h); // measurement noise covariance: R, an Eigen matrix +``` +*Remarks(1):* +- We also combine the output equation and its differentiation into an union function, whose usage is the same as the above steps 1-4, and steps 5-9 are shown as follows. +5. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance. + +(1) initial state and covariance: +``` +state init_state; +esekfom::esekf::cov init_P; +esekfom::esekf kf(init_state,init_P); +``` +(2) default state and covariance: +``` +esekfom::esekf kf; +``` +6. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object: +``` +double epsi[state_dof] = {0.001}; +fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged +kf.init_dyn_runtime_share(f, df_dx, df_dw, Maximum_iter, epsi); +``` +7. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed: +``` +kf.predict(dt, Q, in); // process noise covariance: Q. an Eigen matrix +``` +8. Once a measurement **z** is received, build system measurement as compound manifolds following step 3 and implement the output equation and its differentiation , : +``` +measurement h_dyn_runtime_share(state &s, esekfom::dyn_runtime_share_datastruct &dyn_runtime_share_data) +{ + if(dyn_runtime_share_data.converge) {} // this value is true means iteration is converged + if(condition) dyn_runtime_share_data.valid = false; // the iteration stops before convergence when this value is false, if conditions other than convergence is satisfied + dyn_runtime_share_data.h_x = H_x; // H_x is the result matrix of the first differentiation + dyn_runtime_share_data.h_v = H_v; // H_v is the result matrix of the second differentiation + dyn_runtime_share_data.R = R; // R is the measurement noise covariance + + measurement h_; + h_.pos = s.pos; + return h_; +} +``` +This function would be called during ekf state update, and the output function and its derivatives, the measurement and the measurement noise would be obtained from this one union function + +then an iterated update is executed: +``` +kf.update_iterated_dyn_runtime_share(z, h_dyn_runtime_share); +``` + +*Remarks(2):* +- The value of the state **x** and the covariance **P** are able to be changed by functions **change_x()** and **change_P()**: +``` +state set_x; +kf.change_x(set_x); +esekfom::esekf::cov set_P; +kf.change_P(set_P); +``` + +## 5. Run the sample +Clone the repository: + +``` + git clone https://github.com/hku-mars/IKFoM.git +``` +In the **Samples** file folder, there is the scource code that applys the **IKFoM** on the original source code from [FAST LIO](https://github.com/hku-mars/FAST_LIO). Please follow the README.md shown in that repository excepting the step **2. Build**, which is modified as: +``` +cd ~/catkin_ws/src +cp -r ~/IKFoM/Samples/FAST_LIO-stable FAST_LIO-stable +cd .. +catkin_make +source devel/setup.bash +``` + +## 6.Acknowledgments +Thanks for C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds. + diff --git a/include/common_lib.h b/include/common_lib.h new file mode 100755 index 0000000..c323859 --- /dev/null +++ b/include/common_lib.h @@ -0,0 +1,239 @@ +#ifndef COMMON_LIB_H +#define COMMON_LIB_H + +#include +#include +#include +#include +#include +#include +#include +#include +#include <../include/IKFoM/IKFoM_toolkit/esekfom/esekfom.hpp> +#include + +using namespace std; +using namespace Eigen; + + +typedef MTK::vect<3, double> vect3; +typedef MTK::SO3 SO3; +typedef MTK::S2 S2; +typedef MTK::vect<1, double> vect1; +typedef MTK::vect<2, double> vect2; + +MTK_BUILD_MANIFOLD(state_input, +((vect3, pos)) +((SO3, rot)) +((SO3, offset_R_L_I)) +((vect3, offset_T_L_I)) +((vect3, vel)) +((vect3, bg)) +((vect3, ba)) +((vect3, gravity)) +); + +MTK_BUILD_MANIFOLD(state_output, +((vect3, pos)) +((SO3, rot)) +((SO3, offset_R_L_I)) +((vect3, offset_T_L_I)) +((vect3, vel)) +((vect3, omg)) +((vect3, acc)) +((vect3, gravity)) +((vect3, bg)) +((vect3, ba)) +); + +MTK_BUILD_MANIFOLD(input_ikfom, +((vect3, acc)) +((vect3, gyro)) +); + +MTK_BUILD_MANIFOLD(process_noise_input, +((vect3, ng)) +((vect3, na)) +((vect3, nbg)) +((vect3, nba)) +); + +MTK_BUILD_MANIFOLD(process_noise_output, +((vect3, vel)) +((vect3, ng)) +((vect3, na)) +((vect3, nbg)) +((vect3, nba)) +); + +extern esekfom::esekf kf_input; +extern esekfom::esekf kf_output; + +#define PBWIDTH 30 +#define PBSTR "||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||" + +#define PI_M (3.14159265358) +#define G_m_s2 (9.81) // Gravaty const in GuangDong/China +#define DIM_STATE (24) // Dimension of states (Let Dim(SO(3)) = 3) +#define DIM_PROC_N (12) // Dimension of process noise (Let Dim(SO(3)) = 3) +#define CUBE_LEN (6.0) +#define LIDAR_SP_LEN (2) +#define INIT_COV (0.0001) +#define NUM_MATCH_POINTS (5) +#define MAX_MEAS_DIM (10000) + +#define VEC_FROM_ARRAY(v) v[0],v[1],v[2] +#define VEC_FROM_ARRAY_SIX(v) v[0],v[1],v[2],v[3],v[4],v[5] +#define MAT_FROM_ARRAY(v) v[0],v[1],v[2],v[3],v[4],v[5],v[6],v[7],v[8] +#define CONSTRAIN(v,min,max) ((v>min)?((v (mat.data(), mat.data() + mat.rows() * mat.cols()) +#define DEBUG_FILE_DIR(name) (string(string(ROOT_DIR) + "Log/"+ name)) + +typedef pcl::PointXYZINormal PointType; +typedef pcl::PointXYZRGB PointTypeRGB; +typedef pcl::PointCloud PointCloudXYZI; +typedef pcl::PointCloud PointCloudXYZRGB; +typedef vector> PointVector; +typedef Vector3d V3D; +typedef Matrix3d M3D; +typedef Vector3f V3F; +typedef Matrix3f M3F; + +#define MD(a,b) Matrix +#define VD(a) Matrix +#define MF(a,b) Matrix +#define VF(a) Matrix + +const M3D Eye3d(M3D::Identity()); +const M3F Eye3f(M3F::Identity()); +const V3D Zero3d(0, 0, 0); +const V3F Zero3f(0, 0, 0); + +struct MeasureGroup // Lidar data and imu dates for the curent process +{ + MeasureGroup() + { + lidar_beg_time = 0.0; + lidar_last_time = 0.0; + this->lidar.reset(new PointCloudXYZI()); + }; + double lidar_beg_time; + double lidar_last_time; + PointCloudXYZI::Ptr lidar; + deque imu; +}; + +template +T calc_dist(PointType p1, PointType p2){ + T d = (p1.x - p2.x) * (p1.x - p2.x) + (p1.y - p2.y) * (p1.y - p2.y) + (p1.z - p2.z) * (p1.z - p2.z); + return d; +} + +template +T calc_dist(Eigen::Vector3d p1, PointType p2){ + T d = (p1(0) - p2.x) * (p1(0) - p2.x) + (p1(1) - p2.y) * (p1(1) - p2.y) + (p1(2) - p2.z) * (p1(2) - p2.z); + return d; +} + +template +std::vector time_compressing(const PointCloudXYZI::Ptr &point_cloud) +{ + int points_size = point_cloud->points.size(); + int j = 0; + std::vector time_seq; + // time_seq.clear(); + time_seq.reserve(points_size); + for(int i = 0; i < points_size - 1; i++) + { + j++; + if (point_cloud->points[i+1].curvature > point_cloud->points[i].curvature) + { + time_seq.emplace_back(j); + j = 0; + } + } +// if (j == 0) +// { +// time_seq.emplace_back(1); +// } +// else + { + time_seq.emplace_back(j+1); + } + return time_seq; +} + +/* comment +plane equation: Ax + By + Cz + D = 0 +convert to: A/D*x + B/D*y + C/D*z = -1 +solve: A0*x0 = b0 +where A0_i = [x_i, y_i, z_i], x0 = [A/D, B/D, C/D]^T, b0 = [-1, ..., -1]^T +normvec: normalized x0 +*/ +template +bool esti_normvector(Matrix &normvec, const PointVector &point, const T &threshold, const int &point_num) +{ + MatrixXf A(point_num, 3); + MatrixXf b(point_num, 1); + b.setOnes(); + b *= -1.0f; + + for (int j = 0; j < point_num; j++) + { + A(j,0) = point[j].x; + A(j,1) = point[j].y; + A(j,2) = point[j].z; + } + normvec = A.colPivHouseholderQr().solve(b); + + for (int j = 0; j < point_num; j++) + { + if (fabs(normvec(0) * point[j].x + normvec(1) * point[j].y + normvec(2) * point[j].z + 1.0f) > threshold) + { + return false; + } + } + + normvec.normalize(); + return true; +} + +template +bool esti_plane(Matrix &pca_result, const PointVector &point, const T &threshold) +{ + Matrix A; + Matrix b; + A.setZero(); + b.setOnes(); + b *= -1.0f; + + for (int j = 0; j < NUM_MATCH_POINTS; j++) + { + A(j,0) = point[j].x; + A(j,1) = point[j].y; + A(j,2) = point[j].z; + } + + Matrix normvec = A.colPivHouseholderQr().solve(b); + + T n = normvec.norm(); + pca_result(0) = normvec(0) / n; + pca_result(1) = normvec(1) / n; + pca_result(2) = normvec(2) / n; + pca_result(3) = 1.0 / n; + + for (int j = 0; j < NUM_MATCH_POINTS; j++) + { + if (fabs(pca_result(0) * point[j].x + pca_result(1) * point[j].y + pca_result(2) * point[j].z + pca_result(3)) > threshold) + { + return false; + } + } + return true; +} +// const bool time_list(PointType &x, PointType &y); // {return (x.curvature < y.curvature);}; +// template +// const bool time_list(PointType &x, PointType &y) {return (x.curvature < y.curvature);}; + +#endif \ No newline at end of file diff --git a/include/ivox/eigen_types.h b/include/ivox/eigen_types.h new file mode 100644 index 0000000..51aa389 --- /dev/null +++ b/include/ivox/eigen_types.h @@ -0,0 +1,84 @@ +// +// Created by xiang on 2021/7/16. +// + +#ifndef FASTER_LIO_EIGEN_TYPES_H +#define FASTER_LIO_EIGEN_TYPES_H + +#include +#include +#include + +/// alias for eigen +using Vec2i = Eigen::Vector2i; +using Vec3i = Eigen::Vector3i; + +using Vec2d = Eigen::Vector2d; +using Vec2f = Eigen::Vector2f; +using Vec3d = Eigen::Vector3d; +using Vec3f = Eigen::Vector3f; +using Vec5d = Eigen::Matrix; +using Vec5f = Eigen::Matrix; +using Vec6d = Eigen::Matrix; +using Vec6f = Eigen::Matrix; +using Vec15d = Eigen::Matrix; + +using Mat1d = Eigen::Matrix; +using Mat3d = Eigen::Matrix3d; +using Mat3f = Eigen::Matrix3f; +using Mat4d = Eigen::Matrix4d; +using Mat4f = Eigen::Matrix4f; +using Mat5d = Eigen::Matrix; +using Mat5f = Eigen::Matrix; +using Mat6d = Eigen::Matrix; +using Mat6f = Eigen::Matrix; +using Mat15d = Eigen::Matrix; + +using Quatd = Eigen::Quaterniond; +using Quatf = Eigen::Quaternionf; + +namespace faster_lio { + +/// less of vector +template +struct less_vec { + inline bool operator()(const Eigen::Matrix& v1, const Eigen::Matrix& v2) const; +}; + +/// hash of vector +template +struct hash_vec { + inline size_t operator()(const Eigen::Matrix& v) const; +}; + +/// implementation +template <> +inline bool less_vec<2>::operator()(const Eigen::Matrix& v1, const Eigen::Matrix& v2) const { + return v1[0] < v2[0] || (v1[0] == v2[0] && v1[1] < v2[1]); +} + +template <> +inline bool less_vec<3>::operator()(const Eigen::Matrix& v1, const Eigen::Matrix& v2) const { + return v1[0] < v2[0] || (v1[0] == v2[0] && v1[1] < v2[1]) && (v1[0] == v2[0] && v1[1] == v2[1] && v1[2] < v2[2]); +} + +/// vec 2 hash +/// @see Optimized Spatial Hashing for Collision Detection of Deformable Objects, Matthias Teschner et. al., VMV 2003 +template <> +inline size_t hash_vec<2>::operator()(const Eigen::Matrix& v) const { + return size_t(((v[0]) * 73856093) ^ ((v[1]) * 471943)) % 10000000; +} + +/// vec 3 hash +template <> +inline size_t hash_vec<3>::operator()(const Eigen::Matrix& v) const { + return size_t(((v[0]) * 73856093) ^ ((v[1]) * 471943) ^ ((v[2]) * 83492791)) % 10000000; +} + +// constexpr auto less_vec2i = [](const Vec2i& v1, const Vec2i& v2) { +// return v1[0] < v2[0] || (v1[0] == v2[0] && v1[1] < v2[1]); +// }; + +} // namespace faster_lio + +#endif diff --git a/include/ivox/hilbert.hpp b/include/ivox/hilbert.hpp new file mode 100644 index 0000000..f785965 --- /dev/null +++ b/include/ivox/hilbert.hpp @@ -0,0 +1,830 @@ +// 2021-09-18, https://github.com/spectral3d/hilbert_hpp is under MIT license. + +//Copyright (c) 2019 David Beynon +// +//Permission is hereby granted, free of charge, to any person obtaining a copy +//of this software and associated documentation files (the "Software"), to deal +//in the Software without restriction, including without limitation the rights +//to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +//copies of the Software, and to permit persons to whom the Software is +//furnished to do so, subject to the following conditions: +// +//The above copyright notice and this permission notice shall be included in all +//copies or substantial portions of the Software. +// +//THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +//IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +//FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +//AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +//LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +//OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +//SOFTWARE. + +#ifndef INCLUDED_HILBERT_HPP +#define INCLUDED_HILBERT_HPP +#pragma once + +#include +#include +#include +#include +#include + +// Version 1.1 - 29 August 2019 + +// +// N dimensional hilbert curve encoding & decoding based on the paper +// "Programming the Hilbert Curve" by John Skilling. +// +// The interface assumes an std::array of some unsigned integer type. This +// contains an index in lexographic order, or a set of coordinates on the +// hilbert curve. +// +// Two implementations are included. +// +// hilbert::v1 contains a fairly straightforward implementation of the paper, +// with standard looping constructs. +// +// hilbert::v2 performs uses template metaprogramming to unroll loops and +// theoretically improve performance on some systems. +// +// v1 produces smaller code, which may be more performant on machines with +// small caches. v2 produces relatively large code that seems more efficient +// on modern systems for dimension up to about 100. +// +// v2 should be built with -O3 for best results. -O0 is extremely slow +// on most systems. +// + + +// Interface is as follows: +// +// Find the position of a point on an N dimensional Hilbert Curve. +// +// Based on the paper "Programming the Hilbert Curve" by John Skilling. +// +// Index is encoded with most significant objects first. Lexographic +// sort order. +//template +//std::array +//IndexToPosition(std::array const &in); + +// Find the index of a point on an N dimensional Hilbert Curve. +// +// Based on the paper "Programming the Hilbert Curve" by John Skilling. +// +// Index is encoded with most significant objects first. Lexographic +// sort order. +//template +//std::array +//PositionToIndex(std::array const &in); +// + + +namespace hilbert +{ + // Fairly straightforward implementation. Loops are loops and code mostly + // does what one would expect. + namespace v1 + { + namespace internal + { + // Extract bits from transposed form. + // + // e.g. + // + // a d g j a b c d + // b e h k -> e f g h + // c f i l i j k l + // + template + std::array + UntransposeBits(std::array const &in) + { + const size_t bits = std::numeric_limits::digits; + const T high_bit(T(1) << (bits - 1)); + const size_t bit_count(bits * N); + + std::array out; + + std::fill(out.begin(), out.end(), 0); + + // go through all bits in input, msb first. Shift distances are + // from msb. + for(size_t b=0;b> dst_bit); + } + + return out; + } + + // Pack bits into transposed form. + // + // e.g. + // + // a b c d a d g j + // e f g h -> b e h k + // i j k l c f i l + // + template + std::array + TransposeBits(std::array const &in) + { + const size_t bits = std::numeric_limits::digits; + const T high_bit(T(1) << (bits - 1)); + const size_t bit_count(bits * N); + + std::array out; + + std::fill(out.begin(), out.end(), 0); + + // go through all bits in input, msb first. Shift distances + // are from msb. + for(size_t b=0;b> dst_bit); + } + + return out; + } + } + + // + // Public interfaces. + // + + // Find the position of a point on an N dimensional Hilbert Curve. + // + // Based on the paper "Programming the Hilbert Curve" by John Skilling. + // + // Index is encoded with most significant objects first. Lexographic + // sort order. + template + std::array + IndexToPosition( + std::array const &in) + { + // First convert index to transpose. + std::array out(internal::TransposeBits(in)); + + // Initial gray encoding of transposed vector. + { + T tmp = out[N-1] >> 1; + + for(size_t n=N-1;n;n--) + { + out[n]^= out[n-1]; + } + + out[0]^= tmp; + } + + // Apply transforms to gray code. + { + T cur_bit(2), + low_bits; + + while(cur_bit) + { + low_bits = cur_bit - 1; + + size_t n(N); + + do + { + n--; + if(out[n] & cur_bit) + { + // flip low bits of X + out[0]^= low_bits; + } + else + { + // swap low bits with X + T t((out[n] ^ out[0]) & low_bits); + out[n]^= t; + out[0]^= t; + } + } + while(n); + + cur_bit<<= 1; + } + } + + return out; + } + + // Find the index of a point on an N dimensional Hilbert Curve. + // + // Based on the paper "Programming the Hilbert Curve" by John Skilling. + // + // Index is encoded with most significant objects first. Lexographic + // sort order. + template + std::array + PositionToIndex(std::array const &in) + { + const size_t bits = std::numeric_limits::digits; + + std::array out(in); + + // reverse transforms to convert into transposed gray code. + { + T cur_bit(T(1) << (bits - 1)), + low_bits; + + do + { + low_bits = cur_bit - 1; + + for(size_t n=0;n>= 1; + } while(low_bits > 1); + } + + // Remove gray code from transposed vector. + { + T cur_bit(T(1) << (bits - 1)), + t(0); + + for(size_t n=1;n>= 1; + } while(cur_bit > 1); + + for(auto &v : out) + { + v^= t; + } + } + + return internal::UntransposeBits(out); + } + } // namespace v1 + + // Implementation using metaprogramming to unroll most loops. + // Optimised performance should be superior to v1 provided all code remains + // in cache etc. + // + // At some value of N v1 should overtake v2. + namespace v2 + { + namespace internal + { + // Metaprogramming guts. Unrolled loops, abandon all hope etc. + namespace tmp + { + template + T + TransposeBits2( + std::array const &, + std::integral_constant, + std::integral_constant) + { + return T(0); + } + + template + T + TransposeBits2( + std::array const &in, + std::integral_constant, + std::integral_constant) + { + const size_t size = std::numeric_limits::digits; + const size_t src = ((D + ((size - B) * N)) / size); + const size_t src_bit = + size - (1+((D + ((size - B) * N)) % size)); + const T src_bit_val = T(1) << src_bit; + const size_t dst_bit = (B - 1); + const T dst_bit_val = T(1) << dst_bit; + + // Multiply rather than shift to avoid clang implicit + // conversion warning. + T bit = ((in[src] & src_bit_val) >> src_bit) * dst_bit_val; + + return bit + TransposeBits2( + in, + std::integral_constant(), + std::integral_constant()); + } + + template + void + TransposeBits( + std::array const &, + std::array &, + std::integral_constant) + { + } + + template + void + TransposeBits( + std::array const &in, + std::array &out, + std::integral_constant) + { + out[D-1] = TransposeBits2( + in, + std::integral_constant(), + std::integral_constant< + size_t, + std::numeric_limits::digits>()); + + TransposeBits( + in, + out, + std::integral_constant()); + } + + + template + T + UntransposeBits2( + std::array const &, + std::integral_constant, + std::integral_constant) + { + return T(0); + } + + template + T + UntransposeBits2( + std::array const &in, + std::integral_constant, + std::integral_constant) + { + const size_t size = std::numeric_limits::digits; + const size_t src = ((D * size) + (size - B)) % N; + const size_t src_bit = + size - (((((D * size) + (size - B))) / N) + 1); + const T src_bit_val = T(1) << src_bit; + const size_t dst_bit(B - 1); + const T dst_bit_val = T(1) << dst_bit; + + // Multiply rather than shift to avoid clang implicit + // conversion warning. + T bit = ((in[src] & src_bit_val) >> src_bit) * dst_bit_val; + + return bit + UntransposeBits2( + in, + std::integral_constant(), + std::integral_constant()); + } + + template + void + UntransposeBits( + std::array const &, + std::array &, + std::integral_constant) + { + } + + template + void + UntransposeBits( + std::array const &in, + std::array &out, + std::integral_constant) + { + out[D-1] = UntransposeBits2( + in, + std::integral_constant(), + std::integral_constant< + size_t, + std::numeric_limits::digits>()); + + UntransposeBits( + in, + out, + std::integral_constant()); + } + + template + void + ApplyGrayCode1( + std::array const &in, + std::array &out, + std::integral_constant) + { + out[0]^= in[N-1] >> 1; + } + + template + void + ApplyGrayCode1( + std::array const &in, + std::array &out, + std::integral_constant) + { + out[I]^= out[I-1]; + + ApplyGrayCode1( + in, + out, + std::integral_constant()); + } + + // Remove a gray code from a transposed vector + template + void + RemoveGrayCode1( + std::array &, + std::integral_constant) + { + } + + // xor array values with previous values. + template + void + RemoveGrayCode1( + std::array &in, + std::integral_constant) + { + const size_t src_idx = N - (D + 1); + const size_t dst_idx = N - D; + + in[dst_idx]^= in[src_idx]; + + RemoveGrayCode1(in, std::integral_constant()); + } + + template + T RemoveGrayCode2(T, std::integral_constant) + { + return T(0); + } + + template + T RemoveGrayCode2(T v, std::integral_constant) + { + const T cur_bit(T(1) << (B-1)); + const T low_bits(cur_bit - 1); + + if(v & cur_bit) + { + return low_bits ^ RemoveGrayCode2( + v, + std::integral_constant()); + } + else + { + return RemoveGrayCode2( + v, + std::integral_constant()); + } + } + + template + void + GrayToHilbert2( + std::array &, + std::integral_constant, + std::integral_constant) + { + } + + template + void + GrayToHilbert2( + std::array &out, + std::integral_constant, + std::integral_constant) + { + const size_t n(I-1); + const T cur_bit(T(1) << (std::numeric_limits::digits - B)); + const T low_bits(cur_bit - 1); + + if(out[n] & cur_bit) + { + // flip low bits of X + out[0]^= low_bits; + } + else + { + // swap low bits with X + T t((out[n] ^ out[0]) & low_bits); + out[n]^= t; + out[0]^= t; + } + + GrayToHilbert2( + out, + std::integral_constant(), + std::integral_constant()); + } + + template + void + GrayToHilbert( + std::array &, + std::integral_constant) + { + } + + template + void + GrayToHilbert( + std::array &out, + std::integral_constant) + { + GrayToHilbert2( + out, + std::integral_constant(), + std::integral_constant()); + + GrayToHilbert(out, std::integral_constant()); + } + + template + void + HilbertToGray2( + std::array &, + std::integral_constant, + std::integral_constant) + { + } + + template + void + HilbertToGray2( + std::array &out, + std::integral_constant, + std::integral_constant) + { + const size_t cur_bit(T(1) << B); + const size_t low_bits(cur_bit-1); + const size_t n(N-I); + + if(out[n] & cur_bit) + { + // flip low bits of X + out[0]^= low_bits; + } + else + { + // swap low bits with X + T t((out[n] ^ out[0]) & low_bits); + out[n]^= t; + out[0]^= t; + } + + HilbertToGray2( + out, + std::integral_constant(), + std::integral_constant()); + } + + template + void + HilbertToGray( + std::array &, + std::integral_constant) + { + } + + template + void + HilbertToGray( + std::array &out, + std::integral_constant) + { + HilbertToGray2( + out, + std::integral_constant(), + std::integral_constant()); + + HilbertToGray(out, std::integral_constant()); + } + + template + void + ApplyMaskToArray( + std::array &, + T, + std::integral_constant) + { + } + + template + void + ApplyMaskToArray( + std::array &a, + T mask, std::integral_constant) + { + a[I-1]^= mask; + + ApplyMaskToArray( + a, + mask, + std::integral_constant()); + } + } // namespace tmp + + + // Pack bits into transposed form. + // + // e.g. + // + // a b c d a d g j + // e f g h -> b e h k + // i j k l c f i l + // + template + std::array + TransposeBits(std::array const &in) + { + std::array out; + + std::fill(out.begin(), out.end(), 0); + + tmp::TransposeBits( + in, + out, + std::integral_constant()); + + return out; + } + + // Extract bits from transposed form. + // e.g. + // + // a d g j a b c d + // b e h k -> e f g h + // c f i l i j k l + // + template + std::array + UntransposeBits(std::array const &in) + { + std::array out; + + std::fill(out.begin(), out.end(), 0); + + tmp::UntransposeBits( + in, + out, + std::integral_constant()); + + return out; + } + + + // Apply a gray code to a transformed vector. + template + std::array + ApplyGrayCode(std::array const &in) + { + std::array out(in); + + tmp::ApplyGrayCode1( + in, + out, + std::integral_constant()); + + return out; + } + + template + std::array + RemoveGrayCode(std::array const &in) + { + const size_t bits = std::numeric_limits::digits; + std::array out(in); + + // Remove gray code from transposed vector. + { + // xor values with prev values. + tmp::RemoveGrayCode1( + out, + std::integral_constant()); + + // create a mask. + T t = tmp::RemoveGrayCode2( + out[N-1], + std::integral_constant()); + + // Apply mask to output. + tmp::ApplyMaskToArray( + out, + t, + std::integral_constant()); + } + + return out; + } + + // Generate code to convert from a transposed gray code to a hilbert + // code. + template + std::array + GrayToHilbert(std::array const &in) + { + std::array out(in); + + tmp::GrayToHilbert( + out, + std::integral_constant< + size_t, + std::numeric_limits::digits - 1>()); + + return out; + } + + // Generate code to convert from a hilbert code to a transposed gray + // code. + template + std::array + HilbertToGray(std::array const &in) + { + std::array out(in); + + tmp::HilbertToGray( + out, + std::integral_constant< + size_t, + std::numeric_limits::digits-1>()); + + return out; + } + } + + // + // Public interfaces. + // + + // Find the position of a point on an N dimensional Hilbert Curve. + // + // Based on the paper "Programming the Hilbert Curve" by John Skilling. + // + // Index is encoded with most significant objects first. Lexographic + // sort order. + template + std::array + IndexToPosition(std::array const &in) + { + // First convert index to transpose. + return internal::GrayToHilbert( + internal::ApplyGrayCode( + internal::TransposeBits(in))); + } + + // Find the index of a point on an N dimensional Hilbert Curve. + // + // Based on the paper "Programming the Hilbert Curve" by John Skilling. + // + // Index is encoded with most significant objects first. Lexographic + // sort order. + template + std::array + PositionToIndex(std::array const &in) + { + return internal::UntransposeBits( + internal::RemoveGrayCode( + internal::HilbertToGray(in))); + } + } // namespace v2 +} // namespace hilbert + +#endif diff --git a/include/ivox/ivox3d.h b/include/ivox/ivox3d.h new file mode 100644 index 0000000..6f37014 --- /dev/null +++ b/include/ivox/ivox3d.h @@ -0,0 +1,315 @@ +// +// Created by xiang on 2021/9/16. +// + +#ifndef FASTER_LIO_IVOX3D_H +#define FASTER_LIO_IVOX3D_H + +#include +// #include +#include +#include +#include + +#include "eigen_types.h" +#include "ivox3d_node.hpp" + +namespace faster_lio { + +enum class IVoxNodeType { + DEFAULT, // linear ivox + PHC, // phc ivox +}; + +/// traits for NodeType +template +struct IVoxNodeTypeTraits {}; + +template +struct IVoxNodeTypeTraits { + using NodeType = IVoxNode; +}; + +template +struct IVoxNodeTypeTraits { + using NodeType = IVoxNodePhc; +}; + +template +class IVox { + public: + using KeyType = Eigen::Matrix; + using PtType = Eigen::Matrix; + using NodeType = typename IVoxNodeTypeTraits::NodeType; + using PointVector = std::vector>; + using DistPoint = typename NodeType::DistPoint; + + enum class NearbyType { + CENTER, // center only + NEARBY6, + NEARBY18, + NEARBY26, + }; + + struct Options { + float resolution_ = 0.2; // ivox resolution + float inv_resolution_ = 10.0; // inverse resolution + NearbyType nearby_type_ = NearbyType::NEARBY6; // nearby range + std::size_t capacity_ = 1000000; // capacity + }; + + /** + * constructor + * @param options ivox options + */ + explicit IVox(Options options) : options_(options) { + options_.inv_resolution_ = 1.0 / options_.resolution_; + GenerateNearbyGrids(); + } + + /** + * add points + * @param points_to_add + */ + void AddPoints(const PointVector& points_to_add); + + /// get nn + bool GetClosestPoint(const PointType& pt, PointType& closest_pt); + + /// get nn with condition + bool GetClosestPoint(const PointType& pt, PointVector& closest_pt, int max_num = 5, double max_range = 5.0); + + /// get nn in cloud + bool GetClosestPoint(const PointVector& cloud, PointVector& closest_cloud); + + /// get number of points + size_t NumPoints() const; + + /// get number of valid grids + size_t NumValidGrids() const; + + /// get statistics of the points + std::vector StatGridPoints() const; + + std::unordered_map>::iterator, hash_vec> + grids_map_; + KeyType Pos2Grid(const PtType& pt) const; + KeyType Pos2Grid_(const PtType& pt, const double &defined_res) const; + + private: + /// generate the nearby grids according to the given options + void GenerateNearbyGrids(); + + /// position to grid + // KeyType Pos2Grid(const PtType& pt) const; + + Options options_; + // std::unordered_map>::iterator, hash_vec> + // grids_map_; // voxel hash map + std::list> grids_cache_; // voxel cache + std::vector nearby_grids_; // nearbys +}; + +template +bool IVox::GetClosestPoint(const PointType& pt, PointType& closest_pt) { + std::vector candidates; + auto key = Pos2Grid(ToEigen(pt)); + std::for_each(nearby_grids_.begin(), nearby_grids_.end(), [&key, &candidates, &pt, this](const KeyType& delta) { + auto dkey = key + delta; + auto iter = grids_map_.find(dkey); + if (iter != grids_map_.end()) { + DistPoint dist_point; + bool found = iter->second->second.NNPoint(pt, dist_point); + if (found) { + candidates.emplace_back(dist_point); + } + } + }); + + if (candidates.empty()) { + return false; + } + + auto iter = std::min_element(candidates.begin(), candidates.end()); + closest_pt = iter->Get(); + return true; +} + +template +bool IVox::GetClosestPoint(const PointType& pt, PointVector& closest_pt, int max_num, + double max_range) { + std::vector candidates; + candidates.reserve(max_num * nearby_grids_.size()); + + auto key = Pos2Grid(ToEigen(pt)); + +// #define INNER_TIMER +#ifdef INNER_TIMER + static std::unordered_map> stats; + if (stats.empty()) { + stats["knn"] = std::vector(); + stats["nth"] = std::vector(); + } +#endif + + for (const KeyType& delta : nearby_grids_) { + auto dkey = key + delta; + auto iter = grids_map_.find(dkey); + if (iter != grids_map_.end()) { +#ifdef INNER_TIMER + auto t1 = std::chrono::high_resolution_clock::now(); +#endif + auto tmp = iter->second->second.KNNPointByCondition(candidates, pt, max_num, max_range); +#ifdef INNER_TIMER + auto t2 = std::chrono::high_resolution_clock::now(); + auto knn = std::chrono::duration_cast(t2 - t1).count(); + stats["knn"].emplace_back(knn); +#endif + } + } + + if (candidates.empty()) { + return false; + } + +#ifdef INNER_TIMER + auto t1 = std::chrono::high_resolution_clock::now(); +#endif + + if (candidates.size() <= max_num) { + } else { + std::nth_element(candidates.begin(), candidates.begin() + max_num - 1, candidates.end()); + candidates.resize(max_num); + } + std::nth_element(candidates.begin(), candidates.begin(), candidates.end()); + +#ifdef INNER_TIMER + auto t2 = std::chrono::high_resolution_clock::now(); + auto nth = std::chrono::duration_cast(t2 - t1).count(); + stats["nth"].emplace_back(nth); + + constexpr int STAT_PERIOD = 100000; + if (!stats["nth"].empty() && stats["nth"].size() % STAT_PERIOD == 0) { + for (auto& it : stats) { + const std::string& key = it.first; + std::vector& stat = it.second; + int64_t sum_ = std::accumulate(stat.begin(), stat.end(), 0); + int64_t num_ = stat.size(); + stat.clear(); + std::cout << "inner_" << key << "(ns): sum=" << sum_ << " num=" << num_ << " ave=" << 1.0 * sum_ / num_ + << " ave*n=" << 1.0 * sum_ / STAT_PERIOD << std::endl; + } + } +#endif + + closest_pt.clear(); + for (auto& it : candidates) { + closest_pt.emplace_back(it.Get()); + } + return closest_pt.empty() == false; +} + +template +size_t IVox::NumValidGrids() const { + return grids_map_.size(); +} + +template +void IVox::GenerateNearbyGrids() { + if (options_.nearby_type_ == NearbyType::CENTER) { + nearby_grids_.emplace_back(KeyType::Zero()); + } else if (options_.nearby_type_ == NearbyType::NEARBY6) { + nearby_grids_ = {KeyType(0, 0, 0), KeyType(-1, 0, 0), KeyType(1, 0, 0), KeyType(0, 1, 0), + KeyType(0, -1, 0), KeyType(0, 0, -1), KeyType(0, 0, 1)}; + } else if (options_.nearby_type_ == NearbyType::NEARBY18) { + nearby_grids_ = {KeyType(0, 0, 0), KeyType(-1, 0, 0), KeyType(1, 0, 0), KeyType(0, 1, 0), + KeyType(0, -1, 0), KeyType(0, 0, -1), KeyType(0, 0, 1), KeyType(1, 1, 0), + KeyType(-1, 1, 0), KeyType(1, -1, 0), KeyType(-1, -1, 0), KeyType(1, 0, 1), + KeyType(-1, 0, 1), KeyType(1, 0, -1), KeyType(-1, 0, -1), KeyType(0, 1, 1), + KeyType(0, -1, 1), KeyType(0, 1, -1), KeyType(0, -1, -1)}; + } else if (options_.nearby_type_ == NearbyType::NEARBY26) { + nearby_grids_ = {KeyType(0, 0, 0), KeyType(-1, 0, 0), KeyType(1, 0, 0), KeyType(0, 1, 0), + KeyType(0, -1, 0), KeyType(0, 0, -1), KeyType(0, 0, 1), KeyType(1, 1, 0), + KeyType(-1, 1, 0), KeyType(1, -1, 0), KeyType(-1, -1, 0), KeyType(1, 0, 1), + KeyType(-1, 0, 1), KeyType(1, 0, -1), KeyType(-1, 0, -1), KeyType(0, 1, 1), + KeyType(0, -1, 1), KeyType(0, 1, -1), KeyType(0, -1, -1), KeyType(1, 1, 1), + KeyType(-1, 1, 1), KeyType(1, -1, 1), KeyType(1, 1, -1), KeyType(-1, -1, 1), + KeyType(-1, 1, -1), KeyType(1, -1, -1), KeyType(-1, -1, -1)}; + } else { + // LOG(ERROR) << "Unknown nearby_type!"; + } +} + +template +bool IVox::GetClosestPoint(const PointVector& cloud, PointVector& closest_cloud) { + std::vector index(cloud.size()); + + closest_cloud.resize(cloud.size()); + + for (int i = 0; i < cloud.size(); ++i) { + PointType pt; + if (GetClosestPoint(cloud[i], pt)) { + closest_cloud[i] = pt; + } else { + closest_cloud[i] = PointType(); + } + }; + return true; +} + +template +void IVox::AddPoints(const PointVector& points_to_add) { + for(size_t i = 0; i(points_to_add[i].x, points_to_add[i].y, points_to_add[i].z)); + auto iter = grids_map_.find(key); + if (iter == grids_map_.end()) { + PointType center; + center.getVector3fMap() = key.template cast() * options_.resolution_; + + grids_cache_.push_front({key, NodeType(center, options_.resolution_)}); + grids_map_.insert({key, grids_cache_.begin()}); + + grids_cache_.front().second.InsertPoint(points_to_add[i]); + + if (grids_map_.size() >= options_.capacity_) { + grids_map_.erase(grids_cache_.back().first); + grids_cache_.pop_back(); + } + } else { + iter->second->second.InsertPoint(points_to_add[i]); + grids_cache_.splice(grids_cache_.begin(), grids_cache_, iter->second); + grids_map_[key] = grids_cache_.begin(); + } + } +} + +template +Eigen::Matrix IVox::Pos2Grid(const IVox::PtType& pt) const { + return (pt * options_.inv_resolution_).array().floor().template cast(); +} + +template +Eigen::Matrix IVox::Pos2Grid_(const IVox::PtType& pt, const double &defined_res) const { + return (pt / defined_res).array().floor().template cast(); +} + +template +std::vector IVox::StatGridPoints() const { + int num = grids_cache_.size(), valid_num = 0, max = 0, min = 100000000; + int sum = 0, sum_square = 0; + for (auto& it : grids_cache_) { + int s = it.second.Size(); + valid_num += s > 0; + max = s > max ? s : max; + min = s < min ? s : min; + sum += s; + sum_square += s * s; + } + float ave = float(sum) / num; + float stddev = num > 1 ? sqrt((float(sum_square) - num * ave * ave) / (num - 1)) : 0; + return std::vector{valid_num, ave, max, min, stddev}; +} + +} // namespace faster_lio + +#endif diff --git a/include/ivox/ivox3d_node.hpp b/include/ivox/ivox3d_node.hpp new file mode 100644 index 0000000..d2a9e0b --- /dev/null +++ b/include/ivox/ivox3d_node.hpp @@ -0,0 +1,413 @@ +#include +#include +#include +#include +#include + +#include "hilbert.hpp" + +namespace faster_lio { + +// squared distance of two pcl points +template +inline double distance2(const PointT& pt1, const PointT& pt2) { + Eigen::Vector3f d = pt1.getVector3fMap() - pt2.getVector3fMap(); + return d.squaredNorm(); +} + +// convert from pcl point to eigen +template +inline Eigen::Matrix ToEigen(const PointType& pt) { + cout << "line 21" << endl; + return Eigen::Matrix(pt.x, pt.y, pt.z); + cout << "line 23" << endl; +} + +template <> +inline Eigen::Matrix ToEigen(const pcl::PointXYZ& pt) { + return pt.getVector3fMap(); +} + +template <> +inline Eigen::Matrix ToEigen(const pcl::PointXYZI& pt) { + return pt.getVector3fMap(); +} + +template <> +inline Eigen::Matrix ToEigen(const pcl::PointXYZINormal& pt) { + return pt.getVector3fMap(); +} + +template +class IVoxNode { + public: + EIGEN_MAKE_ALIGNED_OPERATOR_NEW; + + struct DistPoint; + + IVoxNode() = default; + IVoxNode(const PointT& center, const float& side_length) {} /// same with phc + + void InsertPoint(const PointT& pt); + + inline bool Empty() const; + + inline std::size_t Size() const; + + inline PointT GetPoint(const std::size_t idx) const; + + int KNNPointByCondition(std::vector& dis_points, const PointT& point, const int& K, + const double& max_range); + + private: + std::vector points_; +}; + +template +class IVoxNodePhc { + public: + EIGEN_MAKE_ALIGNED_OPERATOR_NEW; + + struct DistPoint; + struct PhcCube; + + IVoxNodePhc() = default; + IVoxNodePhc(const PointT& center, const float& side_length, const int& phc_order = 6); + + void InsertPoint(const PointT& pt); + + void ErasePoint(const PointT& pt, const double erase_distance_th_); + + inline bool Empty() const; + + inline std::size_t Size() const; + + PointT GetPoint(const std::size_t idx) const; + + bool NNPoint(const PointT& cur_pt, DistPoint& dist_point) const; + + int KNNPointByCondition(std::vector& dis_points, const PointT& cur_pt, const int& K = 5, + const double& max_range = 5.0); + + private: + uint32_t CalculatePhcIndex(const PointT& pt) const; + + private: + std::vector phc_cubes_; + + PointT center_; + float side_length_ = 0; + int phc_order_ = 6; + float phc_side_length_ = 0; + float phc_side_length_inv_ = 0; + Eigen::Matrix min_cube_; +}; + +template +struct IVoxNode::DistPoint { + double dist = 0; + IVoxNode* node = nullptr; + int idx = 0; + + DistPoint() = default; + DistPoint(const double d, IVoxNode* n, const int i) : dist(d), node(n), idx(i) {} + + PointT Get() { return node->GetPoint(idx); } + + inline bool operator()(const DistPoint& p1, const DistPoint& p2) { return p1.dist < p2.dist; } + + inline bool operator<(const DistPoint& rhs) { return dist < rhs.dist; } +}; + +template +void IVoxNode::InsertPoint(const PointT& pt) { + points_.template emplace_back(pt); +} + +template +bool IVoxNode::Empty() const { + return points_.empty(); +} + +template +std::size_t IVoxNode::Size() const { + return points_.size(); +} + +template +PointT IVoxNode::GetPoint(const std::size_t idx) const { + return points_[idx]; +} + +template +int IVoxNode::KNNPointByCondition(std::vector& dis_points, const PointT& point, const int& K, + const double& max_range) { + std::size_t old_size = dis_points.size(); +// #define INNER_TIMER +#ifdef INNER_TIMER + static std::unordered_map> stats; + if (stats.empty()) { + stats["dis"] = std::vector(); + stats["put"] = std::vector(); + stats["nth"] = std::vector(); + } +#endif + + for (const auto& pt : points_) { +#ifdef INNER_TIMER + auto t0 = std::chrono::high_resolution_clock::now(); +#endif + double d = distance2(pt, point); +#ifdef INNER_TIMER + auto t1 = std::chrono::high_resolution_clock::now(); +#endif + if (d < max_range * max_range) { + dis_points.template emplace_back(DistPoint(d, this, &pt - points_.data())); + } +#ifdef INNER_TIMER + auto t2 = std::chrono::high_resolution_clock::now(); + + auto dis = std::chrono::duration_cast(t1 - t0).count(); + stats["dis"].emplace_back(dis); + auto put = std::chrono::duration_cast(t2 - t1).count(); + stats["put"].emplace_back(put); +#endif + } + +#ifdef INNER_TIMER + auto t1 = std::chrono::high_resolution_clock::now(); +#endif + // sort by distance + if (old_size + K >= dis_points.size()) { + } else { + std::nth_element(dis_points.begin() + old_size, dis_points.begin() + old_size + K - 1, dis_points.end()); + dis_points.resize(old_size + K); + } + +#ifdef INNER_TIMER + auto t2 = std::chrono::high_resolution_clock::now(); + auto nth = std::chrono::duration_cast(t2 - t1).count(); + stats["nth"].emplace_back(nth); + + constexpr int STAT_PERIOD = 100000; + if (!stats["nth"].empty() && stats["nth"].size() % STAT_PERIOD == 0) { + for (auto& it : stats) { + const std::string& key = it.first; + std::vector& stat = it.second; + int64_t sum_ = std::accumulate(stat.begin(), stat.end(), 0); + int64_t num_ = stat.size(); + stat.clear(); + std::cout << "inner_" << key << "(ns): sum=" << sum_ << " num=" << num_ << " ave=" << 1.0 * sum_ / num_ + << " ave*n=" << 1.0 * sum_ / STAT_PERIOD << std::endl; + } + } +#endif + + return dis_points.size(); +} + +template +struct IVoxNodePhc::DistPoint { + double dist = 0; + IVoxNodePhc* node = nullptr; + int idx = 0; + + DistPoint() {} + DistPoint(const double d, IVoxNodePhc* n, const int i) : dist(d), node(n), idx(i) {} + + PointT Get() { return node->GetPoint(idx); } + + inline bool operator()(const DistPoint& p1, const DistPoint& p2) { return p1.dist < p2.dist; } + + inline bool operator<(const DistPoint& rhs) { return dist < rhs.dist; } +}; + +template +struct IVoxNodePhc::PhcCube { + uint32_t idx = 0; + pcl::CentroidPoint mean; + + PhcCube(uint32_t index, const PointT& pt) { mean.add(pt); } + + void AddPoint(PointT& pt) { mean.add(pt); } + + PointT GetPoint() const { + PointT pt; + mean.get(pt); + return std::move(pt); + } +}; + +template +IVoxNodePhc::IVoxNodePhc(const PointT& center, const float& side_length, const int& phc_order) + : center_(center), side_length_(side_length), phc_order_(phc_order) { + assert(phc_order <= 8); + phc_side_length_ = side_length_ / (std::pow(2, phc_order_)); + phc_side_length_inv_ = (std::pow(2, phc_order_)) / side_length_; + min_cube_ = center_.getArray3fMap() - side_length / 2.0; + phc_cubes_.reserve(64); +} + +template +void IVoxNodePhc::InsertPoint(const PointT& pt) { + uint32_t idx = CalculatePhcIndex(pt); + + PhcCube cube{idx, pt}; + auto it = std::lower_bound(phc_cubes_.begin(), phc_cubes_.end(), cube, + [](const PhcCube& a, const PhcCube& b) { return a.idx < b.idx; }); + + if (it == phc_cubes_.end()) { + phc_cubes_.emplace_back(cube); + } else { + if (it->idx == idx) { + it->AddPoint(pt); + } else { + phc_cubes_.insert(it, cube); + } + } +} + +template +void IVoxNodePhc::ErasePoint(const PointT& pt, const double erase_distance_th_) { + uint32_t idx = CalculatePhcIndex(pt); + + PhcCube cube{idx, pt}; + auto it = std::lower_bound(phc_cubes_.begin(), phc_cubes_.end(), cube, + [](const PhcCube& a, const PhcCube& b) { return a.idx < b.idx; }); + + if (erase_distance_th_ > 0) { + } + if (it != phc_cubes_.end() && it->idx == idx) { + phc_cubes_.erase(it); + } +} + +template +bool IVoxNodePhc::Empty() const { + return phc_cubes_.empty(); +} + +template +std::size_t IVoxNodePhc::Size() const { + return phc_cubes_.size(); +} + +template +PointT IVoxNodePhc::GetPoint(const std::size_t idx) const { + return phc_cubes_[idx].GetPoint(); +} + +template +bool IVoxNodePhc::NNPoint(const PointT& cur_pt, DistPoint& dist_point) const { + if (phc_cubes_.empty()) { + return false; + } + uint32_t cur_idx = CalculatePhcIndex(cur_pt); + PhcCube cube{cur_idx, cur_pt}; + auto it = std::lower_bound(phc_cubes_.begin(), phc_cubes_.end(), cube, + [](const PhcCube& a, const PhcCube& b) { return a.idx < b.idx; }); + + if (it == phc_cubes_.end()) { + it--; + dist_point = DistPoint(distance2(cur_pt, it->GetPoint()), this, it - phc_cubes_.begin()); + } else if (it == phc_cubes_.begin()) { + dist_point = DistPoint(distance2(cur_pt, it->GetPoint()), this, it - phc_cubes_.begin()); + } else { + auto last_it = it; + last_it--; + double d1 = distance2(cur_pt, it->GetPoint()); + double d2 = distance2(cur_pt, last_it->GetPoint()); + if (d1 > d2) { + dist_point = DistPoint(d2, this, it - phc_cubes_.begin()); + } else { + dist_point = DistPoint(d1, this, it - phc_cubes_.begin()); + } + } + + return true; +} + +template +int IVoxNodePhc::KNNPointByCondition(std::vector& dis_points, const PointT& cur_pt, + const int& K, const double& max_range) { + uint32_t cur_idx = CalculatePhcIndex(cur_pt); + PhcCube cube{cur_idx, cur_pt}; + auto it = std::lower_bound(phc_cubes_.begin(), phc_cubes_.end(), cube, + [](const PhcCube& a, const PhcCube& b) { return a.idx < b.idx; }); + + const int max_search_cube_side_length = std::pow(2, std::ceil(std::log2(max_range * phc_side_length_inv_))); + const int max_search_idx_th = + 8 * max_search_cube_side_length * max_search_cube_side_length * max_search_cube_side_length; + + auto create_dist_point = [&cur_pt, this](typename std::vector::const_iterator forward_it) { + double d = distance2(forward_it->GetPoint(), cur_pt); + return DistPoint(d, this, forward_it - phc_cubes_.begin()); + }; + + typename std::vector::const_iterator forward_it(it); + typename std::vector::const_reverse_iterator backward_it(it); + if (it != phc_cubes_.end()) { + dis_points.emplace_back(create_dist_point(it)); + forward_it++; + } + if (backward_it != phc_cubes_.rend()) { + backward_it++; + } + + auto forward_reach_boundary = [&]() { + return forward_it == phc_cubes_.end() || forward_it->idx - cur_idx > max_search_idx_th; + }; + auto backward_reach_boundary = [&]() { + return backward_it == phc_cubes_.rend() || cur_idx - backward_it->idx > max_search_idx_th; + }; + + while (!forward_reach_boundary() && !backward_reach_boundary()) { + if (forward_it->idx - cur_idx > cur_idx - backward_it->idx) { + dis_points.emplace_back(create_dist_point(forward_it)); + forward_it++; + } else { + dis_points.emplace_back(create_dist_point(backward_it.base())); + backward_it++; + } + if (dis_points.size() > K) { + break; + } + } + + if (forward_reach_boundary()) { + while (!backward_reach_boundary() && dis_points.size() < K) { + dis_points.emplace_back(create_dist_point(backward_it.base())); + backward_it++; + } + } + + if (backward_reach_boundary()) { + while (!forward_reach_boundary() && dis_points.size() < K) { + dis_points.emplace_back(create_dist_point(forward_it)); + forward_it++; + } + } + + return dis_points.size(); +} + +template +uint32_t IVoxNodePhc::CalculatePhcIndex(const PointT& pt) const { + Eigen::Matrix eposf = (pt.getVector3fMap() - min_cube_) * phc_side_length_inv_; + Eigen::Matrix eposi = eposf.template cast(); + for (int i = 0; i < dim; ++i) { + if (eposi(i, 0) < 0) { + eposi(i, 0) = 0; + } + if (eposi(i, 0) > std::pow(2, phc_order_)) { + eposi(i, 0) = std::pow(2, phc_order_) - 1; + } + } + std::array apos{eposi(0), eposi(1), eposi(2)}; + std::array tmp = hilbert::v2::PositionToIndex(apos); + + uint32_t idx = (uint32_t(tmp[0]) << 16) + (uint32_t(tmp[1]) << 8) + (uint32_t(tmp[2])); + return idx; +} + +} // namespace faster_lio diff --git a/include/matplotlibcpp.h b/include/matplotlibcpp.h new file mode 100644 index 0000000..a6afa34 --- /dev/null +++ b/include/matplotlibcpp.h @@ -0,0 +1,2500 @@ +#pragma once + +// Python headers must be included before any system headers, since +// they define _POSIX_C_SOURCE +#include + +#include +#include +#include +#include +#include +#include +#include +#include // requires c++11 support +#include + +#ifndef WITHOUT_NUMPY +# define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION +# include + +# ifdef WITH_OPENCV +# include +# include +# endif // WITH_OPENCV + +/* + * A bunch of constants were removed in OpenCV 4 in favour of enum classes, so + * define the ones we need here. + */ +# if CV_MAJOR_VERSION > 3 +# define CV_BGR2RGB cv::COLOR_BGR2RGB +# define CV_BGRA2RGBA cv::COLOR_BGRA2RGBA +# endif +#endif // WITHOUT_NUMPY + +#if PY_MAJOR_VERSION >= 3 +# define PyString_FromString PyUnicode_FromString +# define PyInt_FromLong PyLong_FromLong +# define PyString_FromString PyUnicode_FromString +#endif + + +namespace matplotlibcpp { +namespace detail { + +static std::string s_backend; + +struct _interpreter { + PyObject* s_python_function_arrow; + PyObject *s_python_function_show; + PyObject *s_python_function_close; + PyObject *s_python_function_draw; + PyObject *s_python_function_pause; + PyObject *s_python_function_save; + PyObject *s_python_function_figure; + PyObject *s_python_function_fignum_exists; + PyObject *s_python_function_plot; + PyObject *s_python_function_quiver; + PyObject* s_python_function_contour; + PyObject *s_python_function_semilogx; + PyObject *s_python_function_semilogy; + PyObject *s_python_function_loglog; + PyObject *s_python_function_fill; + PyObject *s_python_function_fill_between; + PyObject *s_python_function_hist; + PyObject *s_python_function_imshow; + PyObject *s_python_function_scatter; + PyObject *s_python_function_boxplot; + PyObject *s_python_function_subplot; + PyObject *s_python_function_subplot2grid; + PyObject *s_python_function_legend; + PyObject *s_python_function_xlim; + PyObject *s_python_function_ion; + PyObject *s_python_function_ginput; + PyObject *s_python_function_ylim; + PyObject *s_python_function_title; + PyObject *s_python_function_axis; + PyObject *s_python_function_axvline; + PyObject *s_python_function_axvspan; + PyObject *s_python_function_xlabel; + PyObject *s_python_function_ylabel; + PyObject *s_python_function_gca; + PyObject *s_python_function_xticks; + PyObject *s_python_function_yticks; + PyObject* s_python_function_margins; + PyObject *s_python_function_tick_params; + PyObject *s_python_function_grid; + PyObject* s_python_function_cla; + PyObject *s_python_function_clf; + PyObject *s_python_function_errorbar; + PyObject *s_python_function_annotate; + PyObject *s_python_function_tight_layout; + PyObject *s_python_colormap; + PyObject *s_python_empty_tuple; + PyObject *s_python_function_stem; + PyObject *s_python_function_xkcd; + PyObject *s_python_function_text; + PyObject *s_python_function_suptitle; + PyObject *s_python_function_bar; + PyObject *s_python_function_colorbar; + PyObject *s_python_function_subplots_adjust; + + + /* For now, _interpreter is implemented as a singleton since its currently not possible to have + multiple independent embedded python interpreters without patching the python source code + or starting a separate process for each. + http://bytes.com/topic/python/answers/793370-multiple-independent-python-interpreters-c-c-program + */ + + static _interpreter& get() { + static _interpreter ctx; + return ctx; + } + + PyObject* safe_import(PyObject* module, std::string fname) { + PyObject* fn = PyObject_GetAttrString(module, fname.c_str()); + + if (!fn) + throw std::runtime_error(std::string("Couldn't find required function: ") + fname); + + if (!PyFunction_Check(fn)) + throw std::runtime_error(fname + std::string(" is unexpectedly not a PyFunction.")); + + return fn; + } + +private: + +#ifndef WITHOUT_NUMPY +# if PY_MAJOR_VERSION >= 3 + + void *import_numpy() { + import_array(); // initialize C-API + return NULL; + } + +# else + + void import_numpy() { + import_array(); // initialize C-API + } + +# endif +#endif + + _interpreter() { + + // optional but recommended +#if PY_MAJOR_VERSION >= 3 + wchar_t name[] = L"plotting"; +#else + char name[] = "plotting"; +#endif + Py_SetProgramName(name); + Py_Initialize(); + +#ifndef WITHOUT_NUMPY + import_numpy(); // initialize numpy C-API +#endif + + PyObject* matplotlibname = PyString_FromString("matplotlib"); + PyObject* pyplotname = PyString_FromString("matplotlib.pyplot"); + PyObject* cmname = PyString_FromString("matplotlib.cm"); + PyObject* pylabname = PyString_FromString("pylab"); + if (!pyplotname || !pylabname || !matplotlibname || !cmname) { + throw std::runtime_error("couldnt create string"); + } + + PyObject* matplotlib = PyImport_Import(matplotlibname); + Py_DECREF(matplotlibname); + if (!matplotlib) { + PyErr_Print(); + throw std::runtime_error("Error loading module matplotlib!"); + } + + // matplotlib.use() must be called *before* pylab, matplotlib.pyplot, + // or matplotlib.backends is imported for the first time + if (!s_backend.empty()) { + PyObject_CallMethod(matplotlib, const_cast("use"), const_cast("s"), s_backend.c_str()); + } + + PyObject* pymod = PyImport_Import(pyplotname); + Py_DECREF(pyplotname); + if (!pymod) { throw std::runtime_error("Error loading module matplotlib.pyplot!"); } + + s_python_colormap = PyImport_Import(cmname); + Py_DECREF(cmname); + if (!s_python_colormap) { throw std::runtime_error("Error loading module matplotlib.cm!"); } + + PyObject* pylabmod = PyImport_Import(pylabname); + Py_DECREF(pylabname); + if (!pylabmod) { throw std::runtime_error("Error loading module pylab!"); } + + s_python_function_arrow = safe_import(pymod, "arrow"); + s_python_function_show = safe_import(pymod, "show"); + s_python_function_close = safe_import(pymod, "close"); + s_python_function_draw = safe_import(pymod, "draw"); + s_python_function_pause = safe_import(pymod, "pause"); + s_python_function_figure = safe_import(pymod, "figure"); + s_python_function_fignum_exists = safe_import(pymod, "fignum_exists"); + s_python_function_plot = safe_import(pymod, "plot"); + s_python_function_quiver = safe_import(pymod, "quiver"); + s_python_function_contour = safe_import(pymod, "contour"); + s_python_function_semilogx = safe_import(pymod, "semilogx"); + s_python_function_semilogy = safe_import(pymod, "semilogy"); + s_python_function_loglog = safe_import(pymod, "loglog"); + s_python_function_fill = safe_import(pymod, "fill"); + s_python_function_fill_between = safe_import(pymod, "fill_between"); + s_python_function_hist = safe_import(pymod,"hist"); + s_python_function_scatter = safe_import(pymod,"scatter"); + s_python_function_boxplot = safe_import(pymod,"boxplot"); + s_python_function_subplot = safe_import(pymod, "subplot"); + s_python_function_subplot2grid = safe_import(pymod, "subplot2grid"); + s_python_function_legend = safe_import(pymod, "legend"); + s_python_function_ylim = safe_import(pymod, "ylim"); + s_python_function_title = safe_import(pymod, "title"); + s_python_function_axis = safe_import(pymod, "axis"); + s_python_function_axvline = safe_import(pymod, "axvline"); + s_python_function_axvspan = safe_import(pymod, "axvspan"); + s_python_function_xlabel = safe_import(pymod, "xlabel"); + s_python_function_ylabel = safe_import(pymod, "ylabel"); + s_python_function_gca = safe_import(pymod, "gca"); + s_python_function_xticks = safe_import(pymod, "xticks"); + s_python_function_yticks = safe_import(pymod, "yticks"); + s_python_function_margins = safe_import(pymod, "margins"); + s_python_function_tick_params = safe_import(pymod, "tick_params"); + s_python_function_grid = safe_import(pymod, "grid"); + s_python_function_xlim = safe_import(pymod, "xlim"); + s_python_function_ion = safe_import(pymod, "ion"); + s_python_function_ginput = safe_import(pymod, "ginput"); + s_python_function_save = safe_import(pylabmod, "savefig"); + s_python_function_annotate = safe_import(pymod,"annotate"); + s_python_function_cla = safe_import(pymod, "cla"); + s_python_function_clf = safe_import(pymod, "clf"); + s_python_function_errorbar = safe_import(pymod, "errorbar"); + s_python_function_tight_layout = safe_import(pymod, "tight_layout"); + s_python_function_stem = safe_import(pymod, "stem"); + s_python_function_xkcd = safe_import(pymod, "xkcd"); + s_python_function_text = safe_import(pymod, "text"); + s_python_function_suptitle = safe_import(pymod, "suptitle"); + s_python_function_bar = safe_import(pymod,"bar"); + s_python_function_colorbar = PyObject_GetAttrString(pymod, "colorbar"); + s_python_function_subplots_adjust = safe_import(pymod,"subplots_adjust"); +#ifndef WITHOUT_NUMPY + s_python_function_imshow = safe_import(pymod, "imshow"); +#endif + s_python_empty_tuple = PyTuple_New(0); + } + + ~_interpreter() { + Py_Finalize(); + } +}; + +} // end namespace detail + +/// Select the backend +/// +/// **NOTE:** This must be called before the first plot command to have +/// any effect. +/// +/// Mainly useful to select the non-interactive 'Agg' backend when running +/// matplotlibcpp in headless mode, for example on a machine with no display. +/// +/// See also: https://matplotlib.org/2.0.2/api/matplotlib_configuration_api.html#matplotlib.use +inline void backend(const std::string& name) +{ + detail::s_backend = name; +} + +inline bool annotate(std::string annotation, double x, double y) +{ + detail::_interpreter::get(); + + PyObject * xy = PyTuple_New(2); + PyObject * str = PyString_FromString(annotation.c_str()); + + PyTuple_SetItem(xy,0,PyFloat_FromDouble(x)); + PyTuple_SetItem(xy,1,PyFloat_FromDouble(y)); + + PyObject* kwargs = PyDict_New(); + PyDict_SetItemString(kwargs, "xy", xy); + + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, str); + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_annotate, args, kwargs); + + Py_DECREF(args); + Py_DECREF(kwargs); + + if(res) Py_DECREF(res); + + return res; +} + +namespace detail { + +#ifndef WITHOUT_NUMPY +// Type selector for numpy array conversion +template struct select_npy_type { const static NPY_TYPES type = NPY_NOTYPE; }; //Default +template <> struct select_npy_type { const static NPY_TYPES type = NPY_DOUBLE; }; +template <> struct select_npy_type { const static NPY_TYPES type = NPY_FLOAT; }; +template <> struct select_npy_type { const static NPY_TYPES type = NPY_BOOL; }; +template <> struct select_npy_type { const static NPY_TYPES type = NPY_INT8; }; +template <> struct select_npy_type { const static NPY_TYPES type = NPY_SHORT; }; +template <> struct select_npy_type { const static NPY_TYPES type = NPY_INT; }; +template <> struct select_npy_type { const static NPY_TYPES type = NPY_INT64; }; +template <> struct select_npy_type { const static NPY_TYPES type = NPY_UINT8; }; +template <> struct select_npy_type { const static NPY_TYPES type = NPY_USHORT; }; +template <> struct select_npy_type { const static NPY_TYPES type = NPY_ULONG; }; +template <> struct select_npy_type { const static NPY_TYPES type = NPY_UINT64; }; + +// Sanity checks; comment them out or change the numpy type below if you're compiling on +// a platform where they don't apply +static_assert(sizeof(long long) == 8); +template <> struct select_npy_type { const static NPY_TYPES type = NPY_INT64; }; +static_assert(sizeof(unsigned long long) == 8); +template <> struct select_npy_type { const static NPY_TYPES type = NPY_UINT64; }; +// TODO: add int, long, etc. + +template +PyObject* get_array(const std::vector& v) +{ + npy_intp vsize = v.size(); + NPY_TYPES type = select_npy_type::type; + if (type == NPY_NOTYPE) { + size_t memsize = v.size()*sizeof(double); + double* dp = static_cast(::malloc(memsize)); + for (size_t i=0; i(varray), NPY_ARRAY_OWNDATA); + return varray; + } + + PyObject* varray = PyArray_SimpleNewFromData(1, &vsize, type, (void*)(v.data())); + return varray; +} + + +template +PyObject* get_2darray(const std::vector<::std::vector>& v) +{ + if (v.size() < 1) throw std::runtime_error("get_2d_array v too small"); + + npy_intp vsize[2] = {static_cast(v.size()), + static_cast(v[0].size())}; + + PyArrayObject *varray = + (PyArrayObject *)PyArray_SimpleNew(2, vsize, NPY_DOUBLE); + + double *vd_begin = static_cast(PyArray_DATA(varray)); + + for (const ::std::vector &v_row : v) { + if (v_row.size() != static_cast(vsize[1])) + throw std::runtime_error("Missmatched array size"); + std::copy(v_row.begin(), v_row.end(), vd_begin); + vd_begin += vsize[1]; + } + + return reinterpret_cast(varray); +} + +#else // fallback if we don't have numpy: copy every element of the given vector + +template +PyObject* get_array(const std::vector& v) +{ + PyObject* list = PyList_New(v.size()); + for(size_t i = 0; i < v.size(); ++i) { + PyList_SetItem(list, i, PyFloat_FromDouble(v.at(i))); + } + return list; +} + +#endif // WITHOUT_NUMPY + +// sometimes, for labels and such, we need string arrays +inline PyObject * get_array(const std::vector& strings) +{ + PyObject* list = PyList_New(strings.size()); + for (std::size_t i = 0; i < strings.size(); ++i) { + PyList_SetItem(list, i, PyString_FromString(strings[i].c_str())); + } + return list; +} + +// not all matplotlib need 2d arrays, some prefer lists of lists +template +PyObject* get_listlist(const std::vector>& ll) +{ + PyObject* listlist = PyList_New(ll.size()); + for (std::size_t i = 0; i < ll.size(); ++i) { + PyList_SetItem(listlist, i, get_array(ll[i])); + } + return listlist; +} + +} // namespace detail + +/// Plot a line through the given x and y data points.. +/// +/// See: https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.plot.html +template +bool plot(const std::vector &x, const std::vector &y, const std::map& keywords) +{ + assert(x.size() == y.size()); + + detail::_interpreter::get(); + + // using numpy arrays + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + // construct positional args + PyObject* args = PyTuple_New(2); + PyTuple_SetItem(args, 0, xarray); + PyTuple_SetItem(args, 1, yarray); + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) + { + PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_plot, args, kwargs); + + Py_DECREF(args); + Py_DECREF(kwargs); + if(res) Py_DECREF(res); + + return res; +} + +// TODO - it should be possible to make this work by implementing +// a non-numpy alternative for `detail::get_2darray()`. +#ifndef WITHOUT_NUMPY +template +void plot_surface(const std::vector<::std::vector> &x, + const std::vector<::std::vector> &y, + const std::vector<::std::vector> &z, + const std::map &keywords = + std::map()) +{ + detail::_interpreter::get(); + + // We lazily load the modules here the first time this function is called + // because I'm not sure that we can assume "matplotlib installed" implies + // "mpl_toolkits installed" on all platforms, and we don't want to require + // it for people who don't need 3d plots. + static PyObject *mpl_toolkitsmod = nullptr, *axis3dmod = nullptr; + if (!mpl_toolkitsmod) { + detail::_interpreter::get(); + + PyObject* mpl_toolkits = PyString_FromString("mpl_toolkits"); + PyObject* axis3d = PyString_FromString("mpl_toolkits.mplot3d"); + if (!mpl_toolkits || !axis3d) { throw std::runtime_error("couldnt create string"); } + + mpl_toolkitsmod = PyImport_Import(mpl_toolkits); + Py_DECREF(mpl_toolkits); + if (!mpl_toolkitsmod) { throw std::runtime_error("Error loading module mpl_toolkits!"); } + + axis3dmod = PyImport_Import(axis3d); + Py_DECREF(axis3d); + if (!axis3dmod) { throw std::runtime_error("Error loading module mpl_toolkits.mplot3d!"); } + } + + assert(x.size() == y.size()); + assert(y.size() == z.size()); + + // using numpy arrays + PyObject *xarray = detail::get_2darray(x); + PyObject *yarray = detail::get_2darray(y); + PyObject *zarray = detail::get_2darray(z); + + // construct positional args + PyObject *args = PyTuple_New(3); + PyTuple_SetItem(args, 0, xarray); + PyTuple_SetItem(args, 1, yarray); + PyTuple_SetItem(args, 2, zarray); + + // Build up the kw args. + PyObject *kwargs = PyDict_New(); + PyDict_SetItemString(kwargs, "rstride", PyInt_FromLong(1)); + PyDict_SetItemString(kwargs, "cstride", PyInt_FromLong(1)); + + PyObject *python_colormap_coolwarm = PyObject_GetAttrString( + detail::_interpreter::get().s_python_colormap, "coolwarm"); + + PyDict_SetItemString(kwargs, "cmap", python_colormap_coolwarm); + + for (std::map::const_iterator it = keywords.begin(); + it != keywords.end(); ++it) { + PyDict_SetItemString(kwargs, it->first.c_str(), + PyString_FromString(it->second.c_str())); + } + + + PyObject *fig = + PyObject_CallObject(detail::_interpreter::get().s_python_function_figure, + detail::_interpreter::get().s_python_empty_tuple); + if (!fig) throw std::runtime_error("Call to figure() failed."); + + PyObject *gca_kwargs = PyDict_New(); + PyDict_SetItemString(gca_kwargs, "projection", PyString_FromString("3d")); + + PyObject *gca = PyObject_GetAttrString(fig, "gca"); + if (!gca) throw std::runtime_error("No gca"); + Py_INCREF(gca); + PyObject *axis = PyObject_Call( + gca, detail::_interpreter::get().s_python_empty_tuple, gca_kwargs); + + if (!axis) throw std::runtime_error("No axis"); + Py_INCREF(axis); + + Py_DECREF(gca); + Py_DECREF(gca_kwargs); + + PyObject *plot_surface = PyObject_GetAttrString(axis, "plot_surface"); + if (!plot_surface) throw std::runtime_error("No surface"); + Py_INCREF(plot_surface); + PyObject *res = PyObject_Call(plot_surface, args, kwargs); + if (!res) throw std::runtime_error("failed surface"); + Py_DECREF(plot_surface); + + Py_DECREF(axis); + Py_DECREF(args); + Py_DECREF(kwargs); + if (res) Py_DECREF(res); +} +#endif // WITHOUT_NUMPY + +template +void plot3(const std::vector &x, + const std::vector &y, + const std::vector &z, + const std::map &keywords = + std::map()) +{ + detail::_interpreter::get(); + + // Same as with plot_surface: We lazily load the modules here the first time + // this function is called because I'm not sure that we can assume "matplotlib + // installed" implies "mpl_toolkits installed" on all platforms, and we don't + // want to require it for people who don't need 3d plots. + static PyObject *mpl_toolkitsmod = nullptr, *axis3dmod = nullptr; + if (!mpl_toolkitsmod) { + detail::_interpreter::get(); + + PyObject* mpl_toolkits = PyString_FromString("mpl_toolkits"); + PyObject* axis3d = PyString_FromString("mpl_toolkits.mplot3d"); + if (!mpl_toolkits || !axis3d) { throw std::runtime_error("couldnt create string"); } + + mpl_toolkitsmod = PyImport_Import(mpl_toolkits); + Py_DECREF(mpl_toolkits); + if (!mpl_toolkitsmod) { throw std::runtime_error("Error loading module mpl_toolkits!"); } + + axis3dmod = PyImport_Import(axis3d); + Py_DECREF(axis3d); + if (!axis3dmod) { throw std::runtime_error("Error loading module mpl_toolkits.mplot3d!"); } + } + + assert(x.size() == y.size()); + assert(y.size() == z.size()); + + PyObject *xarray = detail::get_array(x); + PyObject *yarray = detail::get_array(y); + PyObject *zarray = detail::get_array(z); + + // construct positional args + PyObject *args = PyTuple_New(3); + PyTuple_SetItem(args, 0, xarray); + PyTuple_SetItem(args, 1, yarray); + PyTuple_SetItem(args, 2, zarray); + + // Build up the kw args. + PyObject *kwargs = PyDict_New(); + + for (std::map::const_iterator it = keywords.begin(); + it != keywords.end(); ++it) { + PyDict_SetItemString(kwargs, it->first.c_str(), + PyString_FromString(it->second.c_str())); + } + + PyObject *fig = + PyObject_CallObject(detail::_interpreter::get().s_python_function_figure, + detail::_interpreter::get().s_python_empty_tuple); + if (!fig) throw std::runtime_error("Call to figure() failed."); + + PyObject *gca_kwargs = PyDict_New(); + PyDict_SetItemString(gca_kwargs, "projection", PyString_FromString("3d")); + + PyObject *gca = PyObject_GetAttrString(fig, "gca"); + if (!gca) throw std::runtime_error("No gca"); + Py_INCREF(gca); + PyObject *axis = PyObject_Call( + gca, detail::_interpreter::get().s_python_empty_tuple, gca_kwargs); + + if (!axis) throw std::runtime_error("No axis"); + Py_INCREF(axis); + + Py_DECREF(gca); + Py_DECREF(gca_kwargs); + + PyObject *plot3 = PyObject_GetAttrString(axis, "plot"); + if (!plot3) throw std::runtime_error("No 3D line plot"); + Py_INCREF(plot3); + PyObject *res = PyObject_Call(plot3, args, kwargs); + if (!res) throw std::runtime_error("Failed 3D line plot"); + Py_DECREF(plot3); + + Py_DECREF(axis); + Py_DECREF(args); + Py_DECREF(kwargs); + if (res) Py_DECREF(res); +} + +template +bool stem(const std::vector &x, const std::vector &y, const std::map& keywords) +{ + assert(x.size() == y.size()); + + detail::_interpreter::get(); + + // using numpy arrays + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + // construct positional args + PyObject* args = PyTuple_New(2); + PyTuple_SetItem(args, 0, xarray); + PyTuple_SetItem(args, 1, yarray); + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for (std::map::const_iterator it = + keywords.begin(); it != keywords.end(); ++it) { + PyDict_SetItemString(kwargs, it->first.c_str(), + PyString_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call( + detail::_interpreter::get().s_python_function_stem, args, kwargs); + + Py_DECREF(args); + Py_DECREF(kwargs); + if (res) + Py_DECREF(res); + + return res; +} + +template< typename Numeric > +bool fill(const std::vector& x, const std::vector& y, const std::map& keywords) +{ + assert(x.size() == y.size()); + + detail::_interpreter::get(); + + // using numpy arrays + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + // construct positional args + PyObject* args = PyTuple_New(2); + PyTuple_SetItem(args, 0, xarray); + PyTuple_SetItem(args, 1, yarray); + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for (auto it = keywords.begin(); it != keywords.end(); ++it) { + PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_fill, args, kwargs); + + Py_DECREF(args); + Py_DECREF(kwargs); + + if (res) Py_DECREF(res); + + return res; +} + +template< typename Numeric > +bool fill_between(const std::vector& x, const std::vector& y1, const std::vector& y2, const std::map& keywords) +{ + assert(x.size() == y1.size()); + assert(x.size() == y2.size()); + + detail::_interpreter::get(); + + // using numpy arrays + PyObject* xarray = detail::get_array(x); + PyObject* y1array = detail::get_array(y1); + PyObject* y2array = detail::get_array(y2); + + // construct positional args + PyObject* args = PyTuple_New(3); + PyTuple_SetItem(args, 0, xarray); + PyTuple_SetItem(args, 1, y1array); + PyTuple_SetItem(args, 2, y2array); + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) { + PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_fill_between, args, kwargs); + + Py_DECREF(args); + Py_DECREF(kwargs); + if(res) Py_DECREF(res); + + return res; +} + +template +bool arrow(Numeric x, Numeric y, Numeric end_x, Numeric end_y, const std::string& fc = "r", + const std::string ec = "k", Numeric head_length = 0.25, Numeric head_width = 0.1625) { + PyObject* obj_x = PyFloat_FromDouble(x); + PyObject* obj_y = PyFloat_FromDouble(y); + PyObject* obj_end_x = PyFloat_FromDouble(end_x); + PyObject* obj_end_y = PyFloat_FromDouble(end_y); + + PyObject* kwargs = PyDict_New(); + PyDict_SetItemString(kwargs, "fc", PyString_FromString(fc.c_str())); + PyDict_SetItemString(kwargs, "ec", PyString_FromString(ec.c_str())); + PyDict_SetItemString(kwargs, "head_width", PyFloat_FromDouble(head_width)); + PyDict_SetItemString(kwargs, "head_length", PyFloat_FromDouble(head_length)); + + PyObject* plot_args = PyTuple_New(4); + PyTuple_SetItem(plot_args, 0, obj_x); + PyTuple_SetItem(plot_args, 1, obj_y); + PyTuple_SetItem(plot_args, 2, obj_end_x); + PyTuple_SetItem(plot_args, 3, obj_end_y); + + PyObject* res = + PyObject_Call(detail::_interpreter::get().s_python_function_arrow, plot_args, kwargs); + + Py_DECREF(plot_args); + Py_DECREF(kwargs); + if (res) + Py_DECREF(res); + + return res; +} + +template< typename Numeric> +bool hist(const std::vector& y, long bins=10,std::string color="b", + double alpha=1.0, bool cumulative=false) +{ + detail::_interpreter::get(); + + PyObject* yarray = detail::get_array(y); + + PyObject* kwargs = PyDict_New(); + PyDict_SetItemString(kwargs, "bins", PyLong_FromLong(bins)); + PyDict_SetItemString(kwargs, "color", PyString_FromString(color.c_str())); + PyDict_SetItemString(kwargs, "alpha", PyFloat_FromDouble(alpha)); + PyDict_SetItemString(kwargs, "cumulative", cumulative ? Py_True : Py_False); + + PyObject* plot_args = PyTuple_New(1); + + PyTuple_SetItem(plot_args, 0, yarray); + + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_hist, plot_args, kwargs); + + + Py_DECREF(plot_args); + Py_DECREF(kwargs); + if(res) Py_DECREF(res); + + return res; +} + +#ifndef WITHOUT_NUMPY +namespace detail { + +inline void imshow(void *ptr, const NPY_TYPES type, const int rows, const int columns, const int colors, const std::map &keywords, PyObject** out) +{ + assert(type == NPY_UINT8 || type == NPY_FLOAT); + assert(colors == 1 || colors == 3 || colors == 4); + + detail::_interpreter::get(); + + // construct args + npy_intp dims[3] = { rows, columns, colors }; + PyObject *args = PyTuple_New(1); + PyTuple_SetItem(args, 0, PyArray_SimpleNewFromData(colors == 1 ? 2 : 3, dims, type, ptr)); + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) + { + PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); + } + + PyObject *res = PyObject_Call(detail::_interpreter::get().s_python_function_imshow, args, kwargs); + Py_DECREF(args); + Py_DECREF(kwargs); + if (!res) + throw std::runtime_error("Call to imshow() failed"); + if (out) + *out = res; + else + Py_DECREF(res); +} + +} // namespace detail + +inline void imshow(const unsigned char *ptr, const int rows, const int columns, const int colors, const std::map &keywords = {}, PyObject** out = nullptr) +{ + detail::imshow((void *) ptr, NPY_UINT8, rows, columns, colors, keywords, out); +} + +inline void imshow(const float *ptr, const int rows, const int columns, const int colors, const std::map &keywords = {}, PyObject** out = nullptr) +{ + detail::imshow((void *) ptr, NPY_FLOAT, rows, columns, colors, keywords, out); +} + +#ifdef WITH_OPENCV +void imshow(const cv::Mat &image, const std::map &keywords = {}) +{ + // Convert underlying type of matrix, if needed + cv::Mat image2; + NPY_TYPES npy_type = NPY_UINT8; + switch (image.type() & CV_MAT_DEPTH_MASK) { + case CV_8U: + image2 = image; + break; + case CV_32F: + image2 = image; + npy_type = NPY_FLOAT; + break; + default: + image.convertTo(image2, CV_MAKETYPE(CV_8U, image.channels())); + } + + // If color image, convert from BGR to RGB + switch (image2.channels()) { + case 3: + cv::cvtColor(image2, image2, CV_BGR2RGB); + break; + case 4: + cv::cvtColor(image2, image2, CV_BGRA2RGBA); + } + + detail::imshow(image2.data, npy_type, image2.rows, image2.cols, image2.channels(), keywords); +} +#endif // WITH_OPENCV +#endif // WITHOUT_NUMPY + +template +bool scatter(const std::vector& x, + const std::vector& y, + const double s=1.0, // The marker size in points**2 + const std::map & keywords = {}) +{ + detail::_interpreter::get(); + + assert(x.size() == y.size()); + + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + PyObject* kwargs = PyDict_New(); + PyDict_SetItemString(kwargs, "s", PyLong_FromLong(s)); + for (const auto& it : keywords) + { + PyDict_SetItemString(kwargs, it.first.c_str(), PyString_FromString(it.second.c_str())); + } + + PyObject* plot_args = PyTuple_New(2); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_scatter, plot_args, kwargs); + + Py_DECREF(plot_args); + Py_DECREF(kwargs); + if(res) Py_DECREF(res); + + return res; +} + +template +bool boxplot(const std::vector>& data, + const std::vector& labels = {}, + const std::map & keywords = {}) +{ + detail::_interpreter::get(); + + PyObject* listlist = detail::get_listlist(data); + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, listlist); + + PyObject* kwargs = PyDict_New(); + + // kwargs needs the labels, if there are (the correct number of) labels + if (!labels.empty() && labels.size() == data.size()) { + PyDict_SetItemString(kwargs, "labels", detail::get_array(labels)); + } + + // take care of the remaining keywords + for (const auto& it : keywords) + { + PyDict_SetItemString(kwargs, it.first.c_str(), PyString_FromString(it.second.c_str())); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_boxplot, args, kwargs); + + Py_DECREF(args); + Py_DECREF(kwargs); + + if(res) Py_DECREF(res); + + return res; +} + +template +bool boxplot(const std::vector& data, + const std::map & keywords = {}) +{ + detail::_interpreter::get(); + + PyObject* vector = detail::get_array(data); + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, vector); + + PyObject* kwargs = PyDict_New(); + for (const auto& it : keywords) + { + PyDict_SetItemString(kwargs, it.first.c_str(), PyString_FromString(it.second.c_str())); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_boxplot, args, kwargs); + + Py_DECREF(args); + Py_DECREF(kwargs); + + if(res) Py_DECREF(res); + + return res; +} + +template +bool bar(const std::vector & x, + const std::vector & y, + std::string ec = "black", + std::string ls = "-", + double lw = 1.0, + const std::map & keywords = {}) +{ + detail::_interpreter::get(); + + PyObject * xarray = detail::get_array(x); + PyObject * yarray = detail::get_array(y); + + PyObject * kwargs = PyDict_New(); + + PyDict_SetItemString(kwargs, "ec", PyString_FromString(ec.c_str())); + PyDict_SetItemString(kwargs, "ls", PyString_FromString(ls.c_str())); + PyDict_SetItemString(kwargs, "lw", PyFloat_FromDouble(lw)); + + for (std::map::const_iterator it = + keywords.begin(); + it != keywords.end(); + ++it) { + PyDict_SetItemString( + kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); + } + + PyObject * plot_args = PyTuple_New(2); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + + PyObject * res = PyObject_Call( + detail::_interpreter::get().s_python_function_bar, plot_args, kwargs); + + Py_DECREF(plot_args); + Py_DECREF(kwargs); + if (res) Py_DECREF(res); + + return res; +} + +template +bool bar(const std::vector & y, + std::string ec = "black", + std::string ls = "-", + double lw = 1.0, + const std::map & keywords = {}) +{ + using T = typename std::remove_reference::type::value_type; + + detail::_interpreter::get(); + + std::vector x; + for (std::size_t i = 0; i < y.size(); i++) { x.push_back(i); } + + return bar(x, y, ec, ls, lw, keywords); +} + +inline bool subplots_adjust(const std::map& keywords = {}) +{ + detail::_interpreter::get(); + + PyObject* kwargs = PyDict_New(); + for (std::map::const_iterator it = + keywords.begin(); it != keywords.end(); ++it) { + PyDict_SetItemString(kwargs, it->first.c_str(), + PyFloat_FromDouble(it->second)); + } + + + PyObject* plot_args = PyTuple_New(0); + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_subplots_adjust, plot_args, kwargs); + + Py_DECREF(plot_args); + Py_DECREF(kwargs); + if(res) Py_DECREF(res); + + return res; +} + +template< typename Numeric> +bool named_hist(std::string label,const std::vector& y, long bins=10, std::string color="b", double alpha=1.0) +{ + detail::_interpreter::get(); + + PyObject* yarray = detail::get_array(y); + + PyObject* kwargs = PyDict_New(); + PyDict_SetItemString(kwargs, "label", PyString_FromString(label.c_str())); + PyDict_SetItemString(kwargs, "bins", PyLong_FromLong(bins)); + PyDict_SetItemString(kwargs, "color", PyString_FromString(color.c_str())); + PyDict_SetItemString(kwargs, "alpha", PyFloat_FromDouble(alpha)); + + + PyObject* plot_args = PyTuple_New(1); + PyTuple_SetItem(plot_args, 0, yarray); + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_hist, plot_args, kwargs); + + Py_DECREF(plot_args); + Py_DECREF(kwargs); + if(res) Py_DECREF(res); + + return res; +} + +template +bool plot(const std::vector& x, const std::vector& y, const std::string& s = "") +{ + assert(x.size() == y.size()); + + detail::_interpreter::get(); + + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + PyObject* pystring = PyString_FromString(s.c_str()); + + PyObject* plot_args = PyTuple_New(3); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + PyTuple_SetItem(plot_args, 2, pystring); + + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_plot, plot_args); + + Py_DECREF(plot_args); + if(res) Py_DECREF(res); + + return res; +} + +template +bool contour(const std::vector& x, const std::vector& y, + const std::vector& z, + const std::map& keywords = {}) { + assert(x.size() == y.size() && x.size() == z.size()); + + PyObject* xarray = get_array(x); + PyObject* yarray = get_array(y); + PyObject* zarray = get_array(z); + + PyObject* plot_args = PyTuple_New(3); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + PyTuple_SetItem(plot_args, 2, zarray); + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for (std::map::const_iterator it = keywords.begin(); + it != keywords.end(); ++it) { + PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); + } + + PyObject* res = + PyObject_Call(detail::_interpreter::get().s_python_function_contour, plot_args, kwargs); + + Py_DECREF(kwargs); + Py_DECREF(plot_args); + if (res) + Py_DECREF(res); + + return res; +} + +template +bool quiver(const std::vector& x, const std::vector& y, const std::vector& u, const std::vector& w, const std::map& keywords = {}) +{ + assert(x.size() == y.size() && x.size() == u.size() && u.size() == w.size()); + + detail::_interpreter::get(); + + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + PyObject* uarray = detail::get_array(u); + PyObject* warray = detail::get_array(w); + + PyObject* plot_args = PyTuple_New(4); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + PyTuple_SetItem(plot_args, 2, uarray); + PyTuple_SetItem(plot_args, 3, warray); + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) + { + PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call( + detail::_interpreter::get().s_python_function_quiver, plot_args, kwargs); + + Py_DECREF(kwargs); + Py_DECREF(plot_args); + if (res) + Py_DECREF(res); + + return res; +} + +template +bool stem(const std::vector& x, const std::vector& y, const std::string& s = "") +{ + assert(x.size() == y.size()); + + detail::_interpreter::get(); + + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + PyObject* pystring = PyString_FromString(s.c_str()); + + PyObject* plot_args = PyTuple_New(3); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + PyTuple_SetItem(plot_args, 2, pystring); + + PyObject* res = PyObject_CallObject( + detail::_interpreter::get().s_python_function_stem, plot_args); + + Py_DECREF(plot_args); + if (res) + Py_DECREF(res); + + return res; +} + +template +bool semilogx(const std::vector& x, const std::vector& y, const std::string& s = "") +{ + assert(x.size() == y.size()); + + detail::_interpreter::get(); + + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + PyObject* pystring = PyString_FromString(s.c_str()); + + PyObject* plot_args = PyTuple_New(3); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + PyTuple_SetItem(plot_args, 2, pystring); + + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_semilogx, plot_args); + + Py_DECREF(plot_args); + if(res) Py_DECREF(res); + + return res; +} + +template +bool semilogy(const std::vector& x, const std::vector& y, const std::string& s = "") +{ + assert(x.size() == y.size()); + + detail::_interpreter::get(); + + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + PyObject* pystring = PyString_FromString(s.c_str()); + + PyObject* plot_args = PyTuple_New(3); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + PyTuple_SetItem(plot_args, 2, pystring); + + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_semilogy, plot_args); + + Py_DECREF(plot_args); + if(res) Py_DECREF(res); + + return res; +} + +template +bool loglog(const std::vector& x, const std::vector& y, const std::string& s = "") +{ + assert(x.size() == y.size()); + + detail::_interpreter::get(); + + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + PyObject* pystring = PyString_FromString(s.c_str()); + + PyObject* plot_args = PyTuple_New(3); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + PyTuple_SetItem(plot_args, 2, pystring); + + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_loglog, plot_args); + + Py_DECREF(plot_args); + if(res) Py_DECREF(res); + + return res; +} + +template +bool errorbar(const std::vector &x, const std::vector &y, const std::vector &yerr, const std::map &keywords = {}) +{ + assert(x.size() == y.size()); + + detail::_interpreter::get(); + + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + PyObject* yerrarray = detail::get_array(yerr); + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) + { + PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); + } + + PyDict_SetItemString(kwargs, "yerr", yerrarray); + + PyObject *plot_args = PyTuple_New(2); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + + PyObject *res = PyObject_Call(detail::_interpreter::get().s_python_function_errorbar, plot_args, kwargs); + + Py_DECREF(kwargs); + Py_DECREF(plot_args); + + if (res) + Py_DECREF(res); + else + throw std::runtime_error("Call to errorbar() failed."); + + return res; +} + +template +bool named_plot(const std::string& name, const std::vector& y, const std::string& format = "") +{ + detail::_interpreter::get(); + + PyObject* kwargs = PyDict_New(); + PyDict_SetItemString(kwargs, "label", PyString_FromString(name.c_str())); + + PyObject* yarray = detail::get_array(y); + + PyObject* pystring = PyString_FromString(format.c_str()); + + PyObject* plot_args = PyTuple_New(2); + + PyTuple_SetItem(plot_args, 0, yarray); + PyTuple_SetItem(plot_args, 1, pystring); + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_plot, plot_args, kwargs); + + Py_DECREF(kwargs); + Py_DECREF(plot_args); + if (res) Py_DECREF(res); + + return res; +} + +template +bool named_plot(const std::string& name, const std::vector& x, const std::vector& y, const std::string& format = "") +{ + detail::_interpreter::get(); + + PyObject* kwargs = PyDict_New(); + PyDict_SetItemString(kwargs, "label", PyString_FromString(name.c_str())); + + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + PyObject* pystring = PyString_FromString(format.c_str()); + + PyObject* plot_args = PyTuple_New(3); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + PyTuple_SetItem(plot_args, 2, pystring); + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_plot, plot_args, kwargs); + + Py_DECREF(kwargs); + Py_DECREF(plot_args); + if (res) Py_DECREF(res); + + return res; +} + +template +bool named_semilogx(const std::string& name, const std::vector& x, const std::vector& y, const std::string& format = "") +{ + detail::_interpreter::get(); + + PyObject* kwargs = PyDict_New(); + PyDict_SetItemString(kwargs, "label", PyString_FromString(name.c_str())); + + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + PyObject* pystring = PyString_FromString(format.c_str()); + + PyObject* plot_args = PyTuple_New(3); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + PyTuple_SetItem(plot_args, 2, pystring); + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_semilogx, plot_args, kwargs); + + Py_DECREF(kwargs); + Py_DECREF(plot_args); + if (res) Py_DECREF(res); + + return res; +} + +template +bool named_semilogy(const std::string& name, const std::vector& x, const std::vector& y, const std::string& format = "") +{ + detail::_interpreter::get(); + + PyObject* kwargs = PyDict_New(); + PyDict_SetItemString(kwargs, "label", PyString_FromString(name.c_str())); + + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + PyObject* pystring = PyString_FromString(format.c_str()); + + PyObject* plot_args = PyTuple_New(3); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + PyTuple_SetItem(plot_args, 2, pystring); + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_semilogy, plot_args, kwargs); + + Py_DECREF(kwargs); + Py_DECREF(plot_args); + if (res) Py_DECREF(res); + + return res; +} + +template +bool named_loglog(const std::string& name, const std::vector& x, const std::vector& y, const std::string& format = "") +{ + detail::_interpreter::get(); + + PyObject* kwargs = PyDict_New(); + PyDict_SetItemString(kwargs, "label", PyString_FromString(name.c_str())); + + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + PyObject* pystring = PyString_FromString(format.c_str()); + + PyObject* plot_args = PyTuple_New(3); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + PyTuple_SetItem(plot_args, 2, pystring); + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_loglog, plot_args, kwargs); + + Py_DECREF(kwargs); + Py_DECREF(plot_args); + if (res) Py_DECREF(res); + + return res; +} + +template +bool plot(const std::vector& y, const std::string& format = "") +{ + std::vector x(y.size()); + for(size_t i=0; i +bool plot(const std::vector& y, const std::map& keywords) +{ + std::vector x(y.size()); + for(size_t i=0; i +bool stem(const std::vector& y, const std::string& format = "") +{ + std::vector x(y.size()); + for (size_t i = 0; i < x.size(); ++i) x.at(i) = i; + return stem(x, y, format); +} + +template +void text(Numeric x, Numeric y, const std::string& s = "") +{ + detail::_interpreter::get(); + + PyObject* args = PyTuple_New(3); + PyTuple_SetItem(args, 0, PyFloat_FromDouble(x)); + PyTuple_SetItem(args, 1, PyFloat_FromDouble(y)); + PyTuple_SetItem(args, 2, PyString_FromString(s.c_str())); + + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_text, args); + if(!res) throw std::runtime_error("Call to text() failed."); + + Py_DECREF(args); + Py_DECREF(res); +} + +inline void colorbar(PyObject* mappable = NULL, const std::map& keywords = {}) +{ + if (mappable == NULL) + throw std::runtime_error("Must call colorbar with PyObject* returned from an image, contour, surface, etc."); + + detail::_interpreter::get(); + + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, mappable); + + PyObject* kwargs = PyDict_New(); + for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) + { + PyDict_SetItemString(kwargs, it->first.c_str(), PyFloat_FromDouble(it->second)); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_colorbar, args, kwargs); + if(!res) throw std::runtime_error("Call to colorbar() failed."); + + Py_DECREF(args); + Py_DECREF(kwargs); + Py_DECREF(res); +} + + +inline long figure(long number = -1) +{ + detail::_interpreter::get(); + + PyObject *res; + if (number == -1) + res = PyObject_CallObject(detail::_interpreter::get().s_python_function_figure, detail::_interpreter::get().s_python_empty_tuple); + else { + assert(number > 0); + + // Make sure interpreter is initialised + detail::_interpreter::get(); + + PyObject *args = PyTuple_New(1); + PyTuple_SetItem(args, 0, PyLong_FromLong(number)); + res = PyObject_CallObject(detail::_interpreter::get().s_python_function_figure, args); + Py_DECREF(args); + } + + if(!res) throw std::runtime_error("Call to figure() failed."); + + PyObject* num = PyObject_GetAttrString(res, "number"); + if (!num) throw std::runtime_error("Could not get number attribute of figure object"); + const long figureNumber = PyLong_AsLong(num); + + Py_DECREF(num); + Py_DECREF(res); + + return figureNumber; +} + +inline bool fignum_exists(long number) +{ + detail::_interpreter::get(); + + PyObject *args = PyTuple_New(1); + PyTuple_SetItem(args, 0, PyLong_FromLong(number)); + PyObject *res = PyObject_CallObject(detail::_interpreter::get().s_python_function_fignum_exists, args); + if(!res) throw std::runtime_error("Call to fignum_exists() failed."); + + bool ret = PyObject_IsTrue(res); + Py_DECREF(res); + Py_DECREF(args); + + return ret; +} + +inline void figure_size(size_t w, size_t h) +{ + detail::_interpreter::get(); + + const size_t dpi = 100; + PyObject* size = PyTuple_New(2); + PyTuple_SetItem(size, 0, PyFloat_FromDouble((double)w / dpi)); + PyTuple_SetItem(size, 1, PyFloat_FromDouble((double)h / dpi)); + + PyObject* kwargs = PyDict_New(); + PyDict_SetItemString(kwargs, "figsize", size); + PyDict_SetItemString(kwargs, "dpi", PyLong_FromSize_t(dpi)); + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_figure, + detail::_interpreter::get().s_python_empty_tuple, kwargs); + + Py_DECREF(kwargs); + + if(!res) throw std::runtime_error("Call to figure_size() failed."); + Py_DECREF(res); +} + +inline void legend() +{ + detail::_interpreter::get(); + + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_legend, detail::_interpreter::get().s_python_empty_tuple); + if(!res) throw std::runtime_error("Call to legend() failed."); + + Py_DECREF(res); +} + +inline void legend(const std::map& keywords) +{ + detail::_interpreter::get(); + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) + { + PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_legend, detail::_interpreter::get().s_python_empty_tuple, kwargs); + if(!res) throw std::runtime_error("Call to legend() failed."); + + Py_DECREF(kwargs); + Py_DECREF(res); +} + +template +void ylim(Numeric left, Numeric right) +{ + detail::_interpreter::get(); + + PyObject* list = PyList_New(2); + PyList_SetItem(list, 0, PyFloat_FromDouble(left)); + PyList_SetItem(list, 1, PyFloat_FromDouble(right)); + + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, list); + + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_ylim, args); + if(!res) throw std::runtime_error("Call to ylim() failed."); + + Py_DECREF(args); + Py_DECREF(res); +} + +template +void xlim(Numeric left, Numeric right) +{ + detail::_interpreter::get(); + + PyObject* list = PyList_New(2); + PyList_SetItem(list, 0, PyFloat_FromDouble(left)); + PyList_SetItem(list, 1, PyFloat_FromDouble(right)); + + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, list); + + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_xlim, args); + if(!res) throw std::runtime_error("Call to xlim() failed."); + + Py_DECREF(args); + Py_DECREF(res); +} + + +inline double* xlim() +{ + detail::_interpreter::get(); + + PyObject* args = PyTuple_New(0); + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_xlim, args); + PyObject* left = PyTuple_GetItem(res,0); + PyObject* right = PyTuple_GetItem(res,1); + + double* arr = new double[2]; + arr[0] = PyFloat_AsDouble(left); + arr[1] = PyFloat_AsDouble(right); + + if(!res) throw std::runtime_error("Call to xlim() failed."); + + Py_DECREF(res); + return arr; +} + + +inline double* ylim() +{ + detail::_interpreter::get(); + + PyObject* args = PyTuple_New(0); + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_ylim, args); + PyObject* left = PyTuple_GetItem(res,0); + PyObject* right = PyTuple_GetItem(res,1); + + double* arr = new double[2]; + arr[0] = PyFloat_AsDouble(left); + arr[1] = PyFloat_AsDouble(right); + + if(!res) throw std::runtime_error("Call to ylim() failed."); + + Py_DECREF(res); + return arr; +} + +template +inline void xticks(const std::vector &ticks, const std::vector &labels = {}, const std::map& keywords = {}) +{ + assert(labels.size() == 0 || ticks.size() == labels.size()); + + detail::_interpreter::get(); + + // using numpy array + PyObject* ticksarray = detail::get_array(ticks); + + PyObject* args; + if(labels.size() == 0) { + // construct positional args + args = PyTuple_New(1); + PyTuple_SetItem(args, 0, ticksarray); + } else { + // make tuple of tick labels + PyObject* labelstuple = PyTuple_New(labels.size()); + for (size_t i = 0; i < labels.size(); i++) + PyTuple_SetItem(labelstuple, i, PyUnicode_FromString(labels[i].c_str())); + + // construct positional args + args = PyTuple_New(2); + PyTuple_SetItem(args, 0, ticksarray); + PyTuple_SetItem(args, 1, labelstuple); + } + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) + { + PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_xticks, args, kwargs); + + Py_DECREF(args); + Py_DECREF(kwargs); + if(!res) throw std::runtime_error("Call to xticks() failed"); + + Py_DECREF(res); +} + +template +inline void xticks(const std::vector &ticks, const std::map& keywords) +{ + xticks(ticks, {}, keywords); +} + +template +inline void yticks(const std::vector &ticks, const std::vector &labels = {}, const std::map& keywords = {}) +{ + assert(labels.size() == 0 || ticks.size() == labels.size()); + + detail::_interpreter::get(); + + // using numpy array + PyObject* ticksarray = detail::get_array(ticks); + + PyObject* args; + if(labels.size() == 0) { + // construct positional args + args = PyTuple_New(1); + PyTuple_SetItem(args, 0, ticksarray); + } else { + // make tuple of tick labels + PyObject* labelstuple = PyTuple_New(labels.size()); + for (size_t i = 0; i < labels.size(); i++) + PyTuple_SetItem(labelstuple, i, PyUnicode_FromString(labels[i].c_str())); + + // construct positional args + args = PyTuple_New(2); + PyTuple_SetItem(args, 0, ticksarray); + PyTuple_SetItem(args, 1, labelstuple); + } + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) + { + PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_yticks, args, kwargs); + + Py_DECREF(args); + Py_DECREF(kwargs); + if(!res) throw std::runtime_error("Call to yticks() failed"); + + Py_DECREF(res); +} + +template +inline void yticks(const std::vector &ticks, const std::map& keywords) +{ + yticks(ticks, {}, keywords); +} + +template inline void margins(Numeric margin) +{ + // construct positional args + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, PyFloat_FromDouble(margin)); + + PyObject* res = + PyObject_CallObject(detail::_interpreter::get().s_python_function_margins, args); + if (!res) + throw std::runtime_error("Call to margins() failed."); + + Py_DECREF(args); + Py_DECREF(res); +} + +template inline void margins(Numeric margin_x, Numeric margin_y) +{ + // construct positional args + PyObject* args = PyTuple_New(2); + PyTuple_SetItem(args, 0, PyFloat_FromDouble(margin_x)); + PyTuple_SetItem(args, 1, PyFloat_FromDouble(margin_y)); + + PyObject* res = + PyObject_CallObject(detail::_interpreter::get().s_python_function_margins, args); + if (!res) + throw std::runtime_error("Call to margins() failed."); + + Py_DECREF(args); + Py_DECREF(res); +} + + +inline void tick_params(const std::map& keywords, const std::string axis = "both") +{ + detail::_interpreter::get(); + + // construct positional args + PyObject* args; + args = PyTuple_New(1); + PyTuple_SetItem(args, 0, PyString_FromString(axis.c_str())); + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for (std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) + { + PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); + } + + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_tick_params, args, kwargs); + + Py_DECREF(args); + Py_DECREF(kwargs); + if (!res) throw std::runtime_error("Call to tick_params() failed"); + + Py_DECREF(res); +} + +inline void subplot(long nrows, long ncols, long plot_number) +{ + detail::_interpreter::get(); + + // construct positional args + PyObject* args = PyTuple_New(3); + PyTuple_SetItem(args, 0, PyFloat_FromDouble(nrows)); + PyTuple_SetItem(args, 1, PyFloat_FromDouble(ncols)); + PyTuple_SetItem(args, 2, PyFloat_FromDouble(plot_number)); + + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_subplot, args); + if(!res) throw std::runtime_error("Call to subplot() failed."); + + Py_DECREF(args); + Py_DECREF(res); +} + +inline void subplot2grid(long nrows, long ncols, long rowid=0, long colid=0, long rowspan=1, long colspan=1) +{ + detail::_interpreter::get(); + + PyObject* shape = PyTuple_New(2); + PyTuple_SetItem(shape, 0, PyLong_FromLong(nrows)); + PyTuple_SetItem(shape, 1, PyLong_FromLong(ncols)); + + PyObject* loc = PyTuple_New(2); + PyTuple_SetItem(loc, 0, PyLong_FromLong(rowid)); + PyTuple_SetItem(loc, 1, PyLong_FromLong(colid)); + + PyObject* args = PyTuple_New(4); + PyTuple_SetItem(args, 0, shape); + PyTuple_SetItem(args, 1, loc); + PyTuple_SetItem(args, 2, PyLong_FromLong(rowspan)); + PyTuple_SetItem(args, 3, PyLong_FromLong(colspan)); + + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_subplot2grid, args); + if(!res) throw std::runtime_error("Call to subplot2grid() failed."); + + Py_DECREF(shape); + Py_DECREF(loc); + Py_DECREF(args); + Py_DECREF(res); +} + +inline void title(const std::string &titlestr, const std::map &keywords = {}) +{ + detail::_interpreter::get(); + + PyObject* pytitlestr = PyString_FromString(titlestr.c_str()); + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, pytitlestr); + + PyObject* kwargs = PyDict_New(); + for (auto it = keywords.begin(); it != keywords.end(); ++it) { + PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_title, args, kwargs); + if(!res) throw std::runtime_error("Call to title() failed."); + + Py_DECREF(args); + Py_DECREF(kwargs); + Py_DECREF(res); +} + +inline void suptitle(const std::string &suptitlestr, const std::map &keywords = {}) +{ + detail::_interpreter::get(); + + PyObject* pysuptitlestr = PyString_FromString(suptitlestr.c_str()); + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, pysuptitlestr); + + PyObject* kwargs = PyDict_New(); + for (auto it = keywords.begin(); it != keywords.end(); ++it) { + PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_suptitle, args, kwargs); + if(!res) throw std::runtime_error("Call to suptitle() failed."); + + Py_DECREF(args); + Py_DECREF(kwargs); + Py_DECREF(res); +} + +inline void axis(const std::string &axisstr) +{ + detail::_interpreter::get(); + + PyObject* str = PyString_FromString(axisstr.c_str()); + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, str); + + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_axis, args); + if(!res) throw std::runtime_error("Call to title() failed."); + + Py_DECREF(args); + Py_DECREF(res); +} + +inline void axvline(double x, double ymin = 0., double ymax = 1., const std::map& keywords = std::map()) +{ + detail::_interpreter::get(); + + // construct positional args + PyObject* args = PyTuple_New(3); + PyTuple_SetItem(args, 0, PyFloat_FromDouble(x)); + PyTuple_SetItem(args, 1, PyFloat_FromDouble(ymin)); + PyTuple_SetItem(args, 2, PyFloat_FromDouble(ymax)); + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) + { + PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_axvline, args, kwargs); + + Py_DECREF(args); + Py_DECREF(kwargs); + + if(res) Py_DECREF(res); +} + +inline void axvspan(double xmin, double xmax, double ymin = 0., double ymax = 1., const std::map& keywords = std::map()) +{ + // construct positional args + PyObject* args = PyTuple_New(4); + PyTuple_SetItem(args, 0, PyFloat_FromDouble(xmin)); + PyTuple_SetItem(args, 1, PyFloat_FromDouble(xmax)); + PyTuple_SetItem(args, 2, PyFloat_FromDouble(ymin)); + PyTuple_SetItem(args, 3, PyFloat_FromDouble(ymax)); + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) + { + if (it->first == "linewidth" || it->first == "alpha") + PyDict_SetItemString(kwargs, it->first.c_str(), PyFloat_FromDouble(std::stod(it->second))); + else + PyDict_SetItemString(kwargs, it->first.c_str(), PyString_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_axvspan, args, kwargs); + Py_DECREF(args); + Py_DECREF(kwargs); + + if(res) Py_DECREF(res); +} + +inline void xlabel(const std::string &str, const std::map &keywords = {}) +{ + detail::_interpreter::get(); + + PyObject* pystr = PyString_FromString(str.c_str()); + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, pystr); + + PyObject* kwargs = PyDict_New(); + for (auto it = keywords.begin(); it != keywords.end(); ++it) { + PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_xlabel, args, kwargs); + if(!res) throw std::runtime_error("Call to xlabel() failed."); + + Py_DECREF(args); + Py_DECREF(kwargs); + Py_DECREF(res); +} + +inline void ylabel(const std::string &str, const std::map& keywords = {}) +{ + detail::_interpreter::get(); + + PyObject* pystr = PyString_FromString(str.c_str()); + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, pystr); + + PyObject* kwargs = PyDict_New(); + for (auto it = keywords.begin(); it != keywords.end(); ++it) { + PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_ylabel, args, kwargs); + if(!res) throw std::runtime_error("Call to ylabel() failed."); + + Py_DECREF(args); + Py_DECREF(kwargs); + Py_DECREF(res); +} + +inline void set_zlabel(const std::string &str, const std::map& keywords = {}) +{ + detail::_interpreter::get(); + + // Same as with plot_surface: We lazily load the modules here the first time + // this function is called because I'm not sure that we can assume "matplotlib + // installed" implies "mpl_toolkits installed" on all platforms, and we don't + // want to require it for people who don't need 3d plots. + static PyObject *mpl_toolkitsmod = nullptr, *axis3dmod = nullptr; + if (!mpl_toolkitsmod) { + PyObject* mpl_toolkits = PyString_FromString("mpl_toolkits"); + PyObject* axis3d = PyString_FromString("mpl_toolkits.mplot3d"); + if (!mpl_toolkits || !axis3d) { throw std::runtime_error("couldnt create string"); } + + mpl_toolkitsmod = PyImport_Import(mpl_toolkits); + Py_DECREF(mpl_toolkits); + if (!mpl_toolkitsmod) { throw std::runtime_error("Error loading module mpl_toolkits!"); } + + axis3dmod = PyImport_Import(axis3d); + Py_DECREF(axis3d); + if (!axis3dmod) { throw std::runtime_error("Error loading module mpl_toolkits.mplot3d!"); } + } + + PyObject* pystr = PyString_FromString(str.c_str()); + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, pystr); + + PyObject* kwargs = PyDict_New(); + for (auto it = keywords.begin(); it != keywords.end(); ++it) { + PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); + } + + PyObject *ax = + PyObject_CallObject(detail::_interpreter::get().s_python_function_gca, + detail::_interpreter::get().s_python_empty_tuple); + if (!ax) throw std::runtime_error("Call to gca() failed."); + Py_INCREF(ax); + + PyObject *zlabel = PyObject_GetAttrString(ax, "set_zlabel"); + if (!zlabel) throw std::runtime_error("Attribute set_zlabel not found."); + Py_INCREF(zlabel); + + PyObject *res = PyObject_Call(zlabel, args, kwargs); + if (!res) throw std::runtime_error("Call to set_zlabel() failed."); + Py_DECREF(zlabel); + + Py_DECREF(ax); + Py_DECREF(args); + Py_DECREF(kwargs); + if (res) Py_DECREF(res); +} + +inline void grid(bool flag) +{ + detail::_interpreter::get(); + + PyObject* pyflag = flag ? Py_True : Py_False; + Py_INCREF(pyflag); + + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, pyflag); + + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_grid, args); + if(!res) throw std::runtime_error("Call to grid() failed."); + + Py_DECREF(args); + Py_DECREF(res); +} + +inline void show(const bool block = true) +{ + detail::_interpreter::get(); + + PyObject* res; + if(block) + { + res = PyObject_CallObject( + detail::_interpreter::get().s_python_function_show, + detail::_interpreter::get().s_python_empty_tuple); + } + else + { + PyObject *kwargs = PyDict_New(); + PyDict_SetItemString(kwargs, "block", Py_False); + res = PyObject_Call( detail::_interpreter::get().s_python_function_show, detail::_interpreter::get().s_python_empty_tuple, kwargs); + Py_DECREF(kwargs); + } + + + if (!res) throw std::runtime_error("Call to show() failed."); + + Py_DECREF(res); +} + +inline void close() +{ + detail::_interpreter::get(); + + PyObject* res = PyObject_CallObject( + detail::_interpreter::get().s_python_function_close, + detail::_interpreter::get().s_python_empty_tuple); + + if (!res) throw std::runtime_error("Call to close() failed."); + + Py_DECREF(res); +} + +inline void xkcd() { + detail::_interpreter::get(); + + PyObject* res; + PyObject *kwargs = PyDict_New(); + + res = PyObject_Call(detail::_interpreter::get().s_python_function_xkcd, + detail::_interpreter::get().s_python_empty_tuple, kwargs); + + Py_DECREF(kwargs); + + if (!res) + throw std::runtime_error("Call to show() failed."); + + Py_DECREF(res); +} + +inline void draw() +{ + detail::_interpreter::get(); + + PyObject* res = PyObject_CallObject( + detail::_interpreter::get().s_python_function_draw, + detail::_interpreter::get().s_python_empty_tuple); + + if (!res) throw std::runtime_error("Call to draw() failed."); + + Py_DECREF(res); +} + +template +inline void pause(Numeric interval) +{ + detail::_interpreter::get(); + + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, PyFloat_FromDouble(interval)); + + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_pause, args); + if(!res) throw std::runtime_error("Call to pause() failed."); + + Py_DECREF(args); + Py_DECREF(res); +} + +inline void save(const std::string& filename) +{ + detail::_interpreter::get(); + + PyObject* pyfilename = PyString_FromString(filename.c_str()); + + PyObject* args = PyTuple_New(1); + PyTuple_SetItem(args, 0, pyfilename); + std::cout<<"args:"<> ginput(const int numClicks = 1, const std::map& keywords = {}) +{ + detail::_interpreter::get(); + + PyObject *args = PyTuple_New(1); + PyTuple_SetItem(args, 0, PyLong_FromLong(numClicks)); + + // construct keyword args + PyObject* kwargs = PyDict_New(); + for(std::map::const_iterator it = keywords.begin(); it != keywords.end(); ++it) + { + PyDict_SetItemString(kwargs, it->first.c_str(), PyUnicode_FromString(it->second.c_str())); + } + + PyObject* res = PyObject_Call( + detail::_interpreter::get().s_python_function_ginput, args, kwargs); + + Py_DECREF(kwargs); + Py_DECREF(args); + if (!res) throw std::runtime_error("Call to ginput() failed."); + + const size_t len = PyList_Size(res); + std::vector> out; + out.reserve(len); + for (size_t i = 0; i < len; i++) { + PyObject *current = PyList_GetItem(res, i); + std::array position; + position[0] = PyFloat_AsDouble(PyTuple_GetItem(current, 0)); + position[1] = PyFloat_AsDouble(PyTuple_GetItem(current, 1)); + out.push_back(position); + } + Py_DECREF(res); + + return out; +} + +// Actually, is there any reason not to call this automatically for every plot? +inline void tight_layout() { + detail::_interpreter::get(); + + PyObject *res = PyObject_CallObject( + detail::_interpreter::get().s_python_function_tight_layout, + detail::_interpreter::get().s_python_empty_tuple); + + if (!res) throw std::runtime_error("Call to tight_layout() failed."); + + Py_DECREF(res); +} + +// Support for variadic plot() and initializer lists: + +namespace detail { + +template +using is_function = typename std::is_function>>::type; + +template +struct is_callable_impl; + +template +struct is_callable_impl +{ + typedef is_function type; +}; // a non-object is callable iff it is a function + +template +struct is_callable_impl +{ + struct Fallback { void operator()(); }; + struct Derived : T, Fallback { }; + + template struct Check; + + template + static std::true_type test( ... ); // use a variadic function to make sure (1) it accepts everything and (2) its always the worst match + + template + static std::false_type test( Check* ); + +public: + typedef decltype(test(nullptr)) type; + typedef decltype(&Fallback::operator()) dtype; + static constexpr bool value = type::value; +}; // an object is callable iff it defines operator() + +template +struct is_callable +{ + // dispatch to is_callable_impl or is_callable_impl depending on whether T is of class type or not + typedef typename is_callable_impl::value, T>::type type; +}; + +template +struct plot_impl { }; + +template<> +struct plot_impl +{ + template + bool operator()(const IterableX& x, const IterableY& y, const std::string& format) + { + // 2-phase lookup for distance, begin, end + using std::distance; + using std::begin; + using std::end; + + auto xs = distance(begin(x), end(x)); + auto ys = distance(begin(y), end(y)); + assert(xs == ys && "x and y data must have the same number of elements!"); + + PyObject* xlist = PyList_New(xs); + PyObject* ylist = PyList_New(ys); + PyObject* pystring = PyString_FromString(format.c_str()); + + auto itx = begin(x), ity = begin(y); + for(size_t i = 0; i < xs; ++i) { + PyList_SetItem(xlist, i, PyFloat_FromDouble(*itx++)); + PyList_SetItem(ylist, i, PyFloat_FromDouble(*ity++)); + } + + PyObject* plot_args = PyTuple_New(3); + PyTuple_SetItem(plot_args, 0, xlist); + PyTuple_SetItem(plot_args, 1, ylist); + PyTuple_SetItem(plot_args, 2, pystring); + + PyObject* res = PyObject_CallObject(detail::_interpreter::get().s_python_function_plot, plot_args); + + Py_DECREF(plot_args); + if(res) Py_DECREF(res); + + return res; + } +}; + +template<> +struct plot_impl +{ + template + bool operator()(const Iterable& ticks, const Callable& f, const std::string& format) + { + if(begin(ticks) == end(ticks)) return true; + + // We could use additional meta-programming to deduce the correct element type of y, + // but all values have to be convertible to double anyways + std::vector y; + for(auto x : ticks) y.push_back(f(x)); + return plot_impl()(ticks,y,format); + } +}; + +} // end namespace detail + +// recursion stop for the above +template +bool plot() { return true; } + +template +bool plot(const A& a, const B& b, const std::string& format, Args... args) +{ + return detail::plot_impl::type>()(a,b,format) && plot(args...); +} + +/* + * This group of plot() functions is needed to support initializer lists, i.e. calling + * plot( {1,2,3,4} ) + */ +inline bool plot(const std::vector& x, const std::vector& y, const std::string& format = "") { + return plot(x,y,format); +} + +inline bool plot(const std::vector& y, const std::string& format = "") { + return plot(y,format); +} + +inline bool plot(const std::vector& x, const std::vector& y, const std::map& keywords) { + return plot(x,y,keywords); +} + +/* + * This class allows dynamic plots, ie changing the plotted data without clearing and re-plotting + */ +class Plot +{ +public: + // default initialization with plot label, some data and format + template + Plot(const std::string& name, const std::vector& x, const std::vector& y, const std::string& format = "") { + detail::_interpreter::get(); + + assert(x.size() == y.size()); + + PyObject* kwargs = PyDict_New(); + if(name != "") + PyDict_SetItemString(kwargs, "label", PyString_FromString(name.c_str())); + + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + PyObject* pystring = PyString_FromString(format.c_str()); + + PyObject* plot_args = PyTuple_New(3); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + PyTuple_SetItem(plot_args, 2, pystring); + + PyObject* res = PyObject_Call(detail::_interpreter::get().s_python_function_plot, plot_args, kwargs); + + Py_DECREF(kwargs); + Py_DECREF(plot_args); + + if(res) + { + line= PyList_GetItem(res, 0); + + if(line) + set_data_fct = PyObject_GetAttrString(line,"set_data"); + else + Py_DECREF(line); + Py_DECREF(res); + } + } + + // shorter initialization with name or format only + // basically calls line, = plot([], []) + Plot(const std::string& name = "", const std::string& format = "") + : Plot(name, std::vector(), std::vector(), format) {} + + template + bool update(const std::vector& x, const std::vector& y) { + assert(x.size() == y.size()); + if(set_data_fct) + { + PyObject* xarray = detail::get_array(x); + PyObject* yarray = detail::get_array(y); + + PyObject* plot_args = PyTuple_New(2); + PyTuple_SetItem(plot_args, 0, xarray); + PyTuple_SetItem(plot_args, 1, yarray); + + PyObject* res = PyObject_CallObject(set_data_fct, plot_args); + if (res) Py_DECREF(res); + return res; + } + return false; + } + + // clears the plot but keep it available + bool clear() { + return update(std::vector(), std::vector()); + } + + // definitely remove this line + void remove() { + if(line) + { + auto remove_fct = PyObject_GetAttrString(line,"remove"); + PyObject* args = PyTuple_New(0); + PyObject* res = PyObject_CallObject(remove_fct, args); + if (res) Py_DECREF(res); + } + decref(); + } + + ~Plot() { + decref(); + } +private: + + void decref() { + if(line) + Py_DECREF(line); + if(set_data_fct) + Py_DECREF(set_data_fct); + } + + + PyObject* line = nullptr; + PyObject* set_data_fct = nullptr; +}; + +} // end namespace matplotlibcpp diff --git a/include/so3_math.h b/include/so3_math.h new file mode 100755 index 0000000..f91d8a6 --- /dev/null +++ b/include/so3_math.h @@ -0,0 +1,128 @@ +#ifndef SO3_MATH_H +#define SO3_MATH_H + +#include +#include + +// #include + +#define SKEW_SYM_MATRX(v) 0.0,-v[2],v[1],v[2],0.0,-v[0],-v[1],v[0],0.0 + +template +Eigen::Matrix skew_sym_mat(const Eigen::Matrix &v) +{ + Eigen::Matrix skew_sym_mat; + skew_sym_mat<<0.0,-v[2],v[1],v[2],0.0,-v[0],-v[1],v[0],0.0; + return skew_sym_mat; +} + +template +Eigen::Matrix Exp(const Eigen::Matrix &ang) +{ + T ang_norm = ang.norm(); + Eigen::Matrix Eye3 = Eigen::Matrix::Identity(); + if (ang_norm > 0.0000001) + { + Eigen::Matrix r_axis = ang / ang_norm; + Eigen::Matrix K; + K << SKEW_SYM_MATRX(r_axis); + /// Roderigous Tranformation + return Eye3 + std::sin(ang_norm) * K + (1.0 - std::cos(ang_norm)) * K * K; + } + else + { + return Eye3; + } +} + +template +Eigen::Matrix Exp(const Eigen::Matrix &ang_vel, const Ts &dt) +{ + T ang_vel_norm = ang_vel.norm(); + Eigen::Matrix Eye3 = Eigen::Matrix::Identity(); + + if (ang_vel_norm > 0.0000001) + { + Eigen::Matrix r_axis = ang_vel / ang_vel_norm; + Eigen::Matrix K; + + K << SKEW_SYM_MATRX(r_axis); + + T r_ang = ang_vel_norm * dt; + + /// Roderigous Tranformation + return Eye3 + std::sin(r_ang) * K + (1.0 - std::cos(r_ang)) * K * K; + } + else + { + return Eye3; + } +} + +template +Eigen::Matrix Exp(const T &v1, const T &v2, const T &v3) +{ + T &&norm = sqrt(v1 * v1 + v2 * v2 + v3 * v3); + Eigen::Matrix Eye3 = Eigen::Matrix::Identity(); + if (norm > 0.00001) + { + T r_ang[3] = {v1 / norm, v2 / norm, v3 / norm}; + Eigen::Matrix K; + K << SKEW_SYM_MATRX(r_ang); + + /// Roderigous Tranformation + return Eye3 + std::sin(norm) * K + (1.0 - std::cos(norm)) * K * K; + } + else + { + return Eye3; + } +} + +/* Logrithm of a Rotation Matrix */ +template +Eigen::Matrix Log(const Eigen::Matrix R) +{ + T theta = (R.trace() > 3.0 - 1e-6) ? 0.0 : std::acos(0.5 * (R.trace() - 1)); + Eigen::Matrix K(R(2,1) - R(1,2), R(0,2) - R(2,0), R(1,0) - R(0,1)); + return (std::abs(theta) < 0.001) ? (0.5 * K) : (0.5 * theta / std::sin(theta) * K); +} + +template +Eigen::Matrix RotMtoEuler(const Eigen::Matrix &rot) +{ + T sy = sqrt(rot(0,0)*rot(0,0) + rot(1,0)*rot(1,0)); + bool singular = sy < 1e-6; + T x, y, z; + if(!singular) + { + x = atan2(rot(2, 1), rot(2, 2)); + y = atan2(-rot(2, 0), sy); + z = atan2(rot(1, 0), rot(0, 0)); + } + else + { + x = atan2(-rot(1, 2), rot(1, 1)); + y = atan2(-rot(2, 0), sy); + z = 0; + } + Eigen::Matrix ang(x, y, z); + return ang; +} + +template +Eigen::Matrix3d Jacob_right_inv(Eigen::Vector3d &vec){ + Eigen::Matrix3d hat_v, res; + hat_v << SKEW_SYM_MATRX(vec); + if(vec.norm() > 1e-6) + { + res = Eigen::Matrix::Identity() + 0.5 * hat_v + (1 - vec.norm() * std::cos(vec.norm() / 2) / 2 / std::sin(vec.norm() / 2)) * hat_v * hat_v / vec.squaredNorm(); + } + else + { + res = Eigen::Matrix::Identity(); + } + return res; +} + +#endif diff --git a/launch/gdb_debug_example.launch b/launch/gdb_debug_example.launch new file mode 100755 index 0000000..38a5724 --- /dev/null +++ b/launch/gdb_debug_example.launch @@ -0,0 +1,25 @@ + + + + + + + + + + + + + + + + + + + + + + + launch-prefix="gdb -ex run --args" + + \ No newline at end of file diff --git a/launch/mapping_avia.launch b/launch/mapping_avia.launch new file mode 100755 index 0000000..da647d0 --- /dev/null +++ b/launch/mapping_avia.launch @@ -0,0 +1,25 @@ + + + + + + + + + + + + + + + + + + + + + + + launch-prefix="valgrind" + + \ No newline at end of file diff --git a/launch/mapping_horizon.launch b/launch/mapping_horizon.launch new file mode 100755 index 0000000..5f7091f --- /dev/null +++ b/launch/mapping_horizon.launch @@ -0,0 +1,25 @@ + + + + + + + + + + + + + + + + + + + + + + + launch-prefix="gdb -ex run --args" + + \ No newline at end of file diff --git a/launch/mapping_ouster64.launch b/launch/mapping_ouster64.launch new file mode 100755 index 0000000..c2d6d18 --- /dev/null +++ b/launch/mapping_ouster64.launch @@ -0,0 +1,25 @@ + + + + + + + + + + + + + + + + + + + + + + + launch-prefix="gdb -ex run --args" + + diff --git a/launch/mapping_velody16.launch b/launch/mapping_velody16.launch new file mode 100755 index 0000000..f9caa53 --- /dev/null +++ b/launch/mapping_velody16.launch @@ -0,0 +1,25 @@ + + + + + + + + + + + + + + + + + + + + + + + launch-prefix="gdb -ex run --args" + + \ No newline at end of file diff --git a/msg/LocalSensorExternalTrigger.msg b/msg/LocalSensorExternalTrigger.msg new file mode 100644 index 0000000..d70dabe --- /dev/null +++ b/msg/LocalSensorExternalTrigger.msg @@ -0,0 +1,6 @@ +# This message contains essential time information of the +# local sensor (lidar/inertial sensor) when get triggered +Header header # local timestamp of the trigger +uint32 trigger_id +uint32 event_id +time timestamp_host # corresponding timestamp of the Host PC, not used \ No newline at end of file diff --git a/package.xml b/package.xml new file mode 100755 index 0000000..15ba3fd --- /dev/null +++ b/package.xml @@ -0,0 +1,47 @@ + + + point_lio + 0.0.0 + + + This is a modified version of LOAM which is original algorithm + is described in the following paper: + J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time. + Robotics: Science and Systems Conference (RSS). Berkeley, CA, July 2014. + + + claydergc + + BSD + + Dongjiao He + + catkin + geometry_msgs + nav_msgs + roscpp + rospy + std_msgs + sensor_msgs + tf + pcl_ros + livox_ros_driver + message_generation + + geometry_msgs + nav_msgs + sensor_msgs + roscpp + rospy + std_msgs + tf + pcl_ros + livox_ros_driver + message_runtime + + rostest + rosbag + + + + diff --git a/rviz_cfg/.gitignore b/rviz_cfg/.gitignore new file mode 100755 index 0000000..e69de29 diff --git a/rviz_cfg/loam_livox.rviz b/rviz_cfg/loam_livox.rviz new file mode 100755 index 0000000..c99ca7e --- /dev/null +++ b/rviz_cfg/loam_livox.rviz @@ -0,0 +1,365 @@ +Panels: + - Class: rviz/Displays + Help Height: 0 + Name: Displays + Property Tree Widget: + Expanded: + - /Global Options1 + - /Axes1 + - /mapping1 + - /mapping1/surround1 + - /mapping1/currPoints1 + - /mapping1/currPoints1/Autocompute Value Bounds1 + - /Odometry1/Odometry1 + - /Odometry1/Odometry1/Shape1 + - /Odometry1/Odometry1/Covariance1 + - /Odometry1/Odometry1/Covariance1/Position1 + - /Odometry1/Odometry1/Covariance1/Orientation1 + - /MarkerArray1/Namespaces1 + Splitter Ratio: 0.6432291865348816 + Tree Height: 777 + - Class: rviz/Selection + Name: Selection + - Class: rviz/Tool Properties + Expanded: + - /2D Pose Estimate1 + - /2D Nav Goal1 + - /Publish Point1 + Name: Tool Properties + Splitter Ratio: 0.5886790156364441 + - Class: rviz/Views + Expanded: + - /Current View1 + Name: Views + Splitter Ratio: 0.5 + - Class: rviz/Time + Experimental: false + Name: Time + SyncMode: 0 + SyncSource: surround +Preferences: + PromptSaveOnExit: true +Toolbars: + toolButtonStyle: 2 +Visualization Manager: + Class: "" + Displays: + - Alpha: 1 + Cell Size: 1000 + Class: rviz/Grid + Color: 160; 160; 164 + Enabled: false + Line Style: + Line Width: 0.029999999329447746 + Value: Lines + Name: Grid + Normal Cell Count: 0 + Offset: + X: 0 + Y: 0 + Z: 0 + Plane: XY + Plane Cell Count: 40 + Reference Frame: + Value: false + - Alpha: 1 + Class: rviz/Axes + Enabled: true + Length: 0.699999988079071 + Name: Axes + Radius: 0.05999999865889549 + Reference Frame: aft_mapped + Show Trail: false + Value: true + - Class: rviz/Group + Displays: + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 10 + Min Value: -10 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 238; 238; 236 + Color Transformer: Intensity + Decay Time: 0 + Enabled: true + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 238; 238; 236 + Name: surround + Position Transformer: XYZ + Queue Size: 1 + Selectable: false + Size (Pixels): 3 + Size (m): 0.05000000074505806 + Style: Points + Topic: /cloud_registered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: true + - Alpha: 0.10000000149011612 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 15 + Min Value: -5 + Value: false + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 255; 255; 255 + Color Transformer: Intensity + Decay Time: 1000 + Enabled: true + Invert Rainbow: true + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: currPoints + Position Transformer: XYZ + Queue Size: 100000 + Selectable: true + Size (Pixels): 1 + Size (m): 0.009999999776482582 + Style: Points + Topic: /cloud_registered + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 10 + Min Value: -10 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 255; 0; 0 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: false + Invert Rainbow: false + Max Color: 255; 255; 255 + Min Color: 0; 0; 0 + Name: PointCloud2 + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.10000000149011612 + Style: Flat Squares + Topic: /Laser_map + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: false + Enabled: true + Name: mapping + - Class: rviz/Group + Displays: + - Angle Tolerance: 0.009999999776482582 + Class: rviz/Odometry + Covariance: + Orientation: + Alpha: 0.5 + Color: 255; 255; 127 + Color Style: Unique + Frame: Local + Offset: 1 + Scale: 1 + Value: true + Position: + Alpha: 0.30000001192092896 + Color: 204; 51; 204 + Scale: 1 + Value: true + Value: true + Enabled: true + Keep: 1 + Name: Odometry + Position Tolerance: 0.0010000000474974513 + Queue Size: 10 + Shape: + Alpha: 1 + Axes Length: 1 + Axes Radius: 0.20000000298023224 + Color: 255; 85; 0 + Head Length: 0 + Head Radius: 0 + Shaft Length: 0.05000000074505806 + Shaft Radius: 0.05000000074505806 + Value: Axes + Topic: /Odometry + Unreliable: false + Value: true + Enabled: true + Name: Odometry + - Alpha: 1 + Class: rviz/Axes + Enabled: true + Length: 0.699999988079071 + Name: Axes + Radius: 0.10000000149011612 + Reference Frame: + Show Trail: false + Value: true + - Alpha: 0 + Buffer Length: 2 + Class: rviz/Path + Color: 25; 255; 255 + Enabled: true + Head Diameter: 0 + Head Length: 0 + Length: 0.30000001192092896 + Line Style: Billboards + Line Width: 0.20000000298023224 + Name: Path + Offset: + X: 0 + Y: 0 + Z: 0 + Pose Color: 25; 255; 255 + Pose Style: None + Queue Size: 10 + Radius: 0.029999999329447746 + Shaft Diameter: 0.4000000059604645 + Shaft Length: 0.4000000059604645 + Topic: /path + Unreliable: false + Value: true + - Alpha: 1 + Autocompute Intensity Bounds: false + Autocompute Value Bounds: + Max Value: 10 + Min Value: -10 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 255; 255; 255 + Color Transformer: Intensity + Decay Time: 0 + Enabled: false + Invert Rainbow: false + Max Color: 239; 41; 41 + Max Intensity: 0 + Min Color: 239; 41; 41 + Min Intensity: 0 + Name: PointCloud2 + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 4 + Size (m): 0.30000001192092896 + Style: Spheres + Topic: /cloud_effected + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: false + - Alpha: 1 + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 13.139549255371094 + Min Value: -32.08251953125 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz/PointCloud2 + Color: 138; 226; 52 + Color Transformer: FlatColor + Decay Time: 0 + Enabled: false + Invert Rainbow: false + Max Color: 138; 226; 52 + Min Color: 138; 226; 52 + Name: PointCloud2 + Position Transformer: XYZ + Queue Size: 10 + Selectable: true + Size (Pixels): 3 + Size (m): 0.10000000149011612 + Style: Flat Squares + Topic: /Laser_map + Unreliable: false + Use Fixed Frame: true + Use rainbow: true + Value: false + - Class: rviz/MarkerArray + Enabled: false + Marker Topic: /MarkerArray + Name: MarkerArray + Namespaces: + {} + Queue Size: 100 + Value: false + Enabled: true + Global Options: + Background Color: 0; 0; 0 + Default Light: true + Fixed Frame: camera_init + Frame Rate: 10 + Name: root + Tools: + - Class: rviz/Interact + Hide Inactive Objects: true + - Class: rviz/MoveCamera + - Class: rviz/Select + - Class: rviz/FocusCamera + - Class: rviz/Measure + - Class: rviz/SetInitialPose + Theta std deviation: 0.2617993950843811 + Topic: /initialpose + X std deviation: 0.5 + Y std deviation: 0.5 + - Class: rviz/SetGoal + Topic: /move_base_simple/goal + - Class: rviz/PublishPoint + Single click: true + Topic: /clicked_point + Value: true + Views: + Current: + Class: rviz/Orbit + Distance: 100.72154235839844 + Enable Stereo Rendering: + Stereo Eye Separation: 0.05999999865889549 + Stereo Focal Distance: 1 + Swap Stereo Eyes: false + Value: false + Field of View: 0.7853981852531433 + Focal Point: + X: 34.07209014892578 + Y: -2.53304386138916 + Z: -8.43538761138916 + Focal Shape Fixed Size: true + Focal Shape Size: 0.05000000074505806 + Invert Z Axis: false + Name: Current View + Near Clip Distance: 0.009999999776482582 + Pitch: 0.06979652494192123 + Target Frame: global + Yaw: 4.245370388031006 + Saved: ~ +Window Geometry: + Displays: + collapsed: true + Height: 1016 + Hide Left Dock: true + Hide Right Dock: true + QMainWindow State: 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 + Selection: + collapsed: false + Time: + collapsed: false + Tool Properties: + collapsed: false + Views: + collapsed: true + Width: 1848 + X: 72 + Y: 27 diff --git a/src/Estimator.cpp b/src/Estimator.cpp new file mode 100755 index 0000000..cda4f19 --- /dev/null +++ b/src/Estimator.cpp @@ -0,0 +1,401 @@ +// #include <../include/IKFoM/IKFoM_toolkit/esekfom/esekfom.hpp> +#include "Estimator.h" + +PointCloudXYZI::Ptr normvec(new PointCloudXYZI(100000, 1)); +std::vector time_seq; +PointCloudXYZI::Ptr feats_down_body(new PointCloudXYZI(10000, 1)); +PointCloudXYZI::Ptr feats_down_world(new PointCloudXYZI(10000, 1)); +std::vector pbody_list; +std::vector Nearest_Points; +std::shared_ptr ivox_ = nullptr; // localmap in ivox +std::vector pointSearchSqDis(NUM_MATCH_POINTS); +bool point_selected_surf[100000] = {0}; +std::vector crossmat_list; +int effct_feat_num = 0; +int k = 0; +int idx = -1; +esekfom::esekf kf_input; +esekfom::esekf kf_output; +input_ikfom input_in; +V3D angvel_avr, acc_avr, acc_avr_norm; +int feats_down_size = 0; +V3D Lidar_T_wrt_IMU(Zero3d); +M3D Lidar_R_wrt_IMU(Eye3d); + +Eigen::Matrix process_noise_cov_input() +{ + Eigen::Matrix cov; + cov.setZero(); + cov.block<3, 3>(3, 3).diagonal() << gyr_cov_input, gyr_cov_input, gyr_cov_input; + cov.block<3, 3>(12, 12).diagonal() << acc_cov_input, acc_cov_input, acc_cov_input; + cov.block<3, 3>(15, 15).diagonal() << b_gyr_cov, b_gyr_cov, b_gyr_cov; + cov.block<3, 3>(18, 18).diagonal() << b_acc_cov, b_acc_cov, b_acc_cov; + // MTK::get_cov::type cov = MTK::get_cov::type::Zero(); + // MTK::setDiagonal(cov, &process_noise_input::ng, gyr_cov_input);// 0.03 + // MTK::setDiagonal(cov, &process_noise_input::na, acc_cov_input); // *dt 0.01 0.01 * dt * dt 0.05 + // MTK::setDiagonal(cov, &process_noise_input::nbg, b_gyr_cov); // *dt 0.00001 0.00001 * dt *dt 0.3 //0.001 0.0001 0.01 + // MTK::setDiagonal(cov, &process_noise_input::nba, b_acc_cov); //0.001 0.05 0.0001/out 0.01 + return cov; +} + +Eigen::Matrix process_noise_cov_output() +{ + Eigen::Matrix cov; + cov.setZero(); + cov.block<3, 3>(12, 12).diagonal() << vel_cov, vel_cov, vel_cov; + cov.block<3, 3>(15, 15).diagonal() << gyr_cov_output, gyr_cov_output, gyr_cov_output; + cov.block<3, 3>(18, 18).diagonal() << acc_cov_output, acc_cov_output, acc_cov_output; + cov.block<3, 3>(24, 24).diagonal() << b_gyr_cov, b_gyr_cov, b_gyr_cov; + cov.block<3, 3>(27, 27).diagonal() << b_acc_cov, b_acc_cov, b_acc_cov; + return cov; +} + +Eigen::Matrix get_f_input(state_input &s, const input_ikfom &in) +{ + Eigen::Matrix res = Eigen::Matrix::Zero(); + vect3 omega; + in.gyro.boxminus(omega, s.bg); + vect3 a_inertial = s.rot * (in.acc-s.ba); // .normalized() + for(int i = 0; i < 3; i++ ){ + res(i) = s.vel[i]; + res(i + 3) = omega[i]; + res(i + 12) = a_inertial[i] + s.gravity[i]; + } + return res; +} + +Eigen::Matrix get_f_output(state_output &s, const input_ikfom &in) +{ + Eigen::Matrix res = Eigen::Matrix::Zero(); + vect3 a_inertial = s.rot * s.acc; // .normalized() + for(int i = 0; i < 3; i++ ){ + res(i) = s.vel[i]; + res(i + 3) = s.omg[i]; + res(i + 12) = a_inertial[i] + s.gravity[i]; + } + return res; +} + +Eigen::Matrix df_dx_input(state_input &s, const input_ikfom &in) +{ + Eigen::Matrix cov = Eigen::Matrix::Zero(); + cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity(); + vect3 acc_; + in.acc.boxminus(acc_, s.ba); + vect3 omega; + in.gyro.boxminus(omega, s.bg); + cov.template block<3, 3>(12, 3) = -s.rot*MTK::hat(acc_); // .normalized().toRotationMatrix() + cov.template block<3, 3>(12, 18) = -s.rot; //.normalized().toRotationMatrix(); + // Eigen::Matrix vec = Eigen::Matrix::Zero(); + // Eigen::Matrix grav_matrix; + // s.S2_Mx(grav_matrix, vec, 21); + cov.template block<3, 3>(12, 21) = Eigen::Matrix3d::Identity(); // grav_matrix; + cov.template block<3, 3>(3, 15) = -Eigen::Matrix3d::Identity(); + return cov; +} + +Eigen::Matrix df_dx_output(state_output &s, const input_ikfom &in) +{ + Eigen::Matrix cov = Eigen::Matrix::Zero(); + cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity(); + cov.template block<3, 3>(12, 3) = -s.rot*MTK::hat(s.acc); // .normalized().toRotationMatrix() + cov.template block<3, 3>(12, 18) = s.rot; //.normalized().toRotationMatrix(); + // Eigen::Matrix vec = Eigen::Matrix::Zero(); + // Eigen::Matrix grav_matrix; + // s.S2_Mx(grav_matrix, vec, 21); + cov.template block<3, 3>(12, 21) = Eigen::Matrix3d::Identity(); // grav_matrix; + cov.template block<3, 3>(3, 15) = Eigen::Matrix3d::Identity(); + return cov; +} + +void h_model_input(state_input &s, Eigen::Matrix3d cov_p, Eigen::Matrix3d cov_R, esekfom::dyn_share_modified &ekfom_data) +{ + bool match_in_map = false; + VF(4) pabcd; + pabcd.setZero(); + normvec->resize(time_seq[k]); + int effect_num_k = 0; + for (int j = 0; j < time_seq[k]; j++) + { + PointType &point_body_j = feats_down_body->points[idx+j+1]; + PointType &point_world_j = feats_down_world->points[idx+j+1]; + pointBodyToWorld(&point_body_j, &point_world_j); + V3D p_body = pbody_list[idx+j+1]; + double p_norm = p_body.norm(); + V3D p_world; + p_world << point_world_j.x, point_world_j.y, point_world_j.z; + { + auto &points_near = Nearest_Points[idx+j+1]; + ivox_->GetClosestPoint(point_world_j, points_near, NUM_MATCH_POINTS); // + if ((points_near.size() < NUM_MATCH_POINTS)) // || pointSearchSqDis[NUM_MATCH_POINTS - 1] > 5) // 5) + { + point_selected_surf[idx+j+1] = false; + } + else + { + point_selected_surf[idx+j+1] = false; + if (esti_plane(pabcd, points_near, plane_thr)) //(planeValid) + { + float pd2 = fabs(pabcd(0) * point_world_j.x + pabcd(1) * point_world_j.y + pabcd(2) * point_world_j.z + pabcd(3)); + // V3D norm_vec; + // M3D Rpf, pf; + // pf = crossmat_list[idx+j+1]; + // // pf << SKEW_SYM_MATRX(p_body); + // Rpf = s.rot * pf; + // norm_vec << pabcd(0), pabcd(1), pabcd(2); + // double noise_state = norm_vec.transpose() * (cov_p+Rpf*cov_R*Rpf.transpose()) * norm_vec + sqrt(p_norm) * 0.001; + // // if (p_norm > match_s * pd2 * pd2) + // double epsilon = pd2 / sqrt(noise_state); + // // cout << "check epsilon:" << epsilon << endl; + // double weight = 1.0; // epsilon / sqrt(epsilon * epsilon+1); + // if (epsilon > 1.0) + // { + // weight = sqrt(2 * epsilon - 1) / epsilon; + // pabcd(0) = weight * pabcd(0); + // pabcd(1) = weight * pabcd(1); + // pabcd(2) = weight * pabcd(2); + // pabcd(3) = weight * pabcd(3); + // } + if (p_norm > match_s * pd2 * pd2) + { + point_selected_surf[idx+j+1] = true; + normvec->points[j].x = pabcd(0); + normvec->points[j].y = pabcd(1); + normvec->points[j].z = pabcd(2); + normvec->points[j].intensity = pabcd(3); + effect_num_k ++; + } + } + } + } + } + if (effect_num_k == 0) + { + ekfom_data.valid = false; + return; + } + ekfom_data.M_Noise = laser_point_cov; + ekfom_data.h_x.resize(effect_num_k, 12); + ekfom_data.h_x = Eigen::MatrixXd::Zero(effect_num_k, 12); + ekfom_data.z.resize(effect_num_k); + int m = 0; + + for (int j = 0; j < time_seq[k]; j++) + { + // ekfom_data.converge = false; + if(point_selected_surf[idx+j+1]) + { + V3D norm_vec(normvec->points[j].x, normvec->points[j].y, normvec->points[j].z); + + if (extrinsic_est_en) + { + V3D p_body = pbody_list[idx+j+1]; + M3D p_crossmat, p_imu_crossmat; + p_crossmat << SKEW_SYM_MATRX(p_body); + V3D point_imu = s.offset_R_L_I * p_body + s.offset_T_L_I; + p_imu_crossmat << SKEW_SYM_MATRX(point_imu); + V3D C(s.rot.transpose() * norm_vec); + V3D A(p_imu_crossmat * C); + V3D B(p_crossmat * s.offset_R_L_I.transpose() * C); + ekfom_data.h_x.block<1, 12>(m, 0) << norm_vec(0), norm_vec(1), norm_vec(2), VEC_FROM_ARRAY(A), VEC_FROM_ARRAY(B), VEC_FROM_ARRAY(C); + } + else + { + M3D point_crossmat = crossmat_list[idx+j+1]; + V3D C(s.rot.transpose() * norm_vec); // conjugate().normalized() + V3D A(point_crossmat * C); + ekfom_data.h_x.block<1, 6>(m, 0) << norm_vec(0), norm_vec(1), norm_vec(2), VEC_FROM_ARRAY(A), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0; + } + ekfom_data.z(m) = -norm_vec(0) * feats_down_world->points[idx+j+1].x -norm_vec(1) * feats_down_world->points[idx+j+1].y -norm_vec(2) * feats_down_world->points[idx+j+1].z-normvec->points[j].intensity; + + m++; + } + } + effct_feat_num += effect_num_k; +} + +void h_model_output(state_output &s, Eigen::Matrix3d cov_p, Eigen::Matrix3d cov_R, esekfom::dyn_share_modified &ekfom_data) +{ + bool match_in_map = false; + VF(4) pabcd; + pabcd.setZero(); + normvec->resize(time_seq[k]); + int effect_num_k = 0; + for (int j = 0; j < time_seq[k]; j++) + { + PointType &point_body_j = feats_down_body->points[idx+j+1]; + PointType &point_world_j = feats_down_world->points[idx+j+1]; + pointBodyToWorld(&point_body_j, &point_world_j); + V3D p_body = pbody_list[idx+j+1]; + double p_norm = p_body.norm(); + V3D p_world; + p_world << point_world_j.x, point_world_j.y, point_world_j.z; + { + auto &points_near = Nearest_Points[idx+j+1]; + + ivox_->GetClosestPoint(point_world_j, points_near, NUM_MATCH_POINTS); // + + if ((points_near.size() < NUM_MATCH_POINTS)) // || pointSearchSqDis[NUM_MATCH_POINTS - 1] > 5) + { + point_selected_surf[idx+j+1] = false; + } + else + { + point_selected_surf[idx+j+1] = false; + if (esti_plane(pabcd, points_near, plane_thr)) //(planeValid) + { + float pd2 = fabs(pabcd(0) * point_world_j.x + pabcd(1) * point_world_j.y + pabcd(2) * point_world_j.z + pabcd(3)); + // V3D norm_vec; + // M3D Rpf, pf; + // pf = crossmat_list[idx+j+1]; + // // pf << SKEW_SYM_MATRX(p_body); + // Rpf = s.rot * pf; + // norm_vec << pabcd(0), pabcd(1), pabcd(2); + // double noise_state = norm_vec.transpose() * (cov_p+Rpf*cov_R*Rpf.transpose()) * norm_vec + sqrt(p_norm) * 0.001; + // // if (p_norm > match_s * pd2 * pd2) + // double epsilon = pd2 / sqrt(noise_state); + // double weight = 1.0; // epsilon / sqrt(epsilon * epsilon+1); + // if (epsilon > 1.0) + // { + // weight = sqrt(2 * epsilon - 1) / epsilon; + // pabcd(0) = weight * pabcd(0); + // pabcd(1) = weight * pabcd(1); + // pabcd(2) = weight * pabcd(2); + // pabcd(3) = weight * pabcd(3); + // } + if (p_norm > match_s * pd2 * pd2) + { + // point_selected_surf[i] = true; + point_selected_surf[idx+j+1] = true; + normvec->points[j].x = pabcd(0); + normvec->points[j].y = pabcd(1); + normvec->points[j].z = pabcd(2); + normvec->points[j].intensity = pabcd(3); + effect_num_k ++; + } + } + } + } + } + if (effect_num_k == 0) + { + ekfom_data.valid = false; + return; + } + ekfom_data.M_Noise = laser_point_cov; + ekfom_data.h_x.resize(effect_num_k, 12); + ekfom_data.h_x = Eigen::MatrixXd::Zero(effect_num_k, 12); + ekfom_data.z.resize(effect_num_k); + int m = 0; + for (int j = 0; j < time_seq[k]; j++) + { + // ekfom_data.converge = false; + if(point_selected_surf[idx+j+1]) + { + V3D norm_vec(normvec->points[j].x, normvec->points[j].y, normvec->points[j].z); + if (extrinsic_est_en) + { + V3D p_body = pbody_list[idx+j+1]; + M3D p_crossmat, p_imu_crossmat; + p_crossmat << SKEW_SYM_MATRX(p_body); + V3D point_imu = s.offset_R_L_I * p_body + s.offset_T_L_I; + p_imu_crossmat << SKEW_SYM_MATRX(point_imu); + V3D C(s.rot.transpose() * norm_vec); + V3D A(p_imu_crossmat * C); + V3D B(p_crossmat * s.offset_R_L_I.transpose() * C); + ekfom_data.h_x.block<1, 12>(m, 0) << norm_vec(0), norm_vec(1), norm_vec(2), VEC_FROM_ARRAY(A), VEC_FROM_ARRAY(B), VEC_FROM_ARRAY(C); + } + else + { + M3D point_crossmat = crossmat_list[idx+j+1]; + V3D C(s.rot.transpose() * norm_vec); // conjugate().normalized() + V3D A(point_crossmat * C); + ekfom_data.h_x.block<1, 6>(m, 0) << norm_vec(0), norm_vec(1), norm_vec(2), VEC_FROM_ARRAY(A), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0; + } + ekfom_data.z(m) = -norm_vec(0) * feats_down_world->points[idx+j+1].x -norm_vec(1) * feats_down_world->points[idx+j+1].y -norm_vec(2) * feats_down_world->points[idx+j+1].z-normvec->points[j].intensity; + + m++; + } + } + effct_feat_num += effect_num_k; +} + +void h_model_IMU_output(state_output &s, esekfom::dyn_share_modified &ekfom_data) +{ + std::memset(ekfom_data.satu_check, false, 6); + ekfom_data.z_IMU.block<3,1>(0, 0) = angvel_avr - s.omg - s.bg; + ekfom_data.z_IMU.block<3,1>(3, 0) = acc_avr * G_m_s2 / acc_norm - s.acc - s.ba; + ekfom_data.R_IMU << imu_meas_omg_cov, imu_meas_omg_cov, imu_meas_omg_cov, imu_meas_acc_cov, imu_meas_acc_cov, imu_meas_acc_cov; + if(check_satu) + { + if(fabs(angvel_avr(0)) >= 0.99 * satu_gyro) + { + ekfom_data.satu_check[0] = true; + ekfom_data.z_IMU(0) = 0.0; + } + + if(fabs(angvel_avr(1)) >= 0.99 * satu_gyro) + { + ekfom_data.satu_check[1] = true; + ekfom_data.z_IMU(1) = 0.0; + } + + if(fabs(angvel_avr(2)) >= 0.99 * satu_gyro) + { + ekfom_data.satu_check[2] = true; + ekfom_data.z_IMU(2) = 0.0; + } + + if(fabs(acc_avr(0)) >= 0.99 * satu_acc) + { + ekfom_data.satu_check[3] = true; + ekfom_data.z_IMU(3) = 0.0; + } + + if(fabs(acc_avr(1)) >= 0.99 * satu_acc) + { + ekfom_data.satu_check[4] = true; + ekfom_data.z_IMU(4) = 0.0; + } + + if(fabs(acc_avr(2)) >= 0.99 * satu_acc) + { + ekfom_data.satu_check[5] = true; + ekfom_data.z_IMU(5) = 0.0; + } + } +} + +void pointBodyToWorld(PointType const * const pi, PointType * const po) +{ + V3D p_body(pi->x, pi->y, pi->z); + + V3D p_global; + if (extrinsic_est_en) + { + if (!use_imu_as_input) + { + p_global = kf_output.x_.rot * (kf_output.x_.offset_R_L_I * p_body + kf_output.x_.offset_T_L_I) + kf_output.x_.pos; + } + else + { + p_global = kf_input.x_.rot * (kf_input.x_.offset_R_L_I * p_body + kf_input.x_.offset_T_L_I) + kf_input.x_.pos; + } + } + else + { + if (!use_imu_as_input) + { + p_global = kf_output.x_.rot * (Lidar_R_wrt_IMU * p_body + Lidar_T_wrt_IMU) + kf_output.x_.pos; // .normalized() + } + else + { + p_global = kf_input.x_.rot * (Lidar_R_wrt_IMU * p_body + Lidar_T_wrt_IMU) + kf_input.x_.pos; // .normalized() + } + } + + po->x = p_global(0); + po->y = p_global(1); + po->z = p_global(2); + po->intensity = pi->intensity; +} \ No newline at end of file diff --git a/src/Estimator.h b/src/Estimator.h new file mode 100755 index 0000000..9dc8ce6 --- /dev/null +++ b/src/Estimator.h @@ -0,0 +1,61 @@ +#ifndef Estimator_H +#define Estimator_H + +#include "common_lib.h" +#include "parameters.h" +#include +#include +#include +#include +// #include +#include +#include + +extern PointCloudXYZI::Ptr normvec; //(new PointCloudXYZI(100000, 1)); +extern std::vector time_seq; +extern PointCloudXYZI::Ptr feats_down_body; //(new PointCloudXYZI()); +extern PointCloudXYZI::Ptr feats_down_world; //(new PointCloudXYZI()); +extern std::vector pbody_list; +extern std::vector Nearest_Points; +extern std::shared_ptr ivox_; // localmap in ivox +extern std::vector pointSearchSqDis; +extern bool point_selected_surf[100000]; // = {0}; +extern std::vector crossmat_list; +extern int effct_feat_num; +extern int k; +extern int idx; +extern V3D angvel_avr, acc_avr, acc_avr_norm; +extern int feats_down_size; +// extern std::vector normvec_holder; +extern V3D Lidar_T_wrt_IMU; //(Zero3d); +extern M3D Lidar_R_wrt_IMU; //(Eye3d); + +extern input_ikfom input_in; + +Eigen::Matrix process_noise_cov_input(); + +Eigen::Matrix process_noise_cov_output(); + +//double L_offset_to_I[3] = {0.04165, 0.02326, -0.0284}; // Avia +//vect3 Lidar_offset_to_IMU(L_offset_to_I, 3); +Eigen::Matrix get_f_input(state_input &s, const input_ikfom &in); + +Eigen::Matrix get_f_output(state_output &s, const input_ikfom &in); + +Eigen::Matrix df_dx_input(state_input &s, const input_ikfom &in); + +// Eigen::Matrix df_dw_input(state_input &s, const input_ikfom &in); + +Eigen::Matrix df_dx_output(state_output &s, const input_ikfom &in); + +// Eigen::Matrix df_dw_output(state_output &s); + +void h_model_input(state_input &s, Eigen::Matrix3d cov_p, Eigen::Matrix3d cov_R, esekfom::dyn_share_modified &ekfom_data); + +void h_model_output(state_output &s, Eigen::Matrix3d cov_p, Eigen::Matrix3d cov_R, esekfom::dyn_share_modified &ekfom_data); + +void h_model_IMU_output(state_output &s, esekfom::dyn_share_modified &ekfom_data); + +void pointBodyToWorld(PointType const * const pi, PointType * const po); + +#endif \ No newline at end of file diff --git a/src/IMU_Processing.cpp b/src/IMU_Processing.cpp new file mode 100755 index 0000000..0f51d2e --- /dev/null +++ b/src/IMU_Processing.cpp @@ -0,0 +1,136 @@ +#include "IMU_Processing.h" + +const bool time_list(PointType &x, PointType &y) {return (x.curvature < y.curvature);}; + +void ImuProcess::set_gyr_cov(const V3D &scaler) +{ + cov_gyr_scale = scaler; +} + +void ImuProcess::set_acc_cov(const V3D &scaler) +{ + cov_vel_scale = scaler; +} + +ImuProcess::ImuProcess() + : b_first_frame_(true), imu_need_init_(true) +{ + imu_en = true; + init_iter_num = 1; + mean_acc = V3D(0, 0, 0.0); + mean_gyr = V3D(0, 0, 0); + after_imu_init_ = false; + state_cov.setIdentity(); +} + +ImuProcess::~ImuProcess() {} + +void ImuProcess::Reset() +{ + ROS_WARN("Reset ImuProcess"); + mean_acc = V3D(0, 0, 0.0); + mean_gyr = V3D(0, 0, 0); + imu_need_init_ = true; + init_iter_num = 1; + after_imu_init_ = false; + + time_last_scan = 0.0; +} + +void ImuProcess::Set_init(Eigen::Vector3d &tmp_gravity, Eigen::Matrix3d &rot) +{ + /** 1. initializing the gravity, gyro bias, acc and gyro covariance + ** 2. normalize the acceleration measurenments to unit gravity **/ + // V3D tmp_gravity = - mean_acc / mean_acc.norm() * G_m_s2; // state_gravity; + M3D hat_grav; + hat_grav << 0.0, gravity_(2), -gravity_(1), + -gravity_(2), 0.0, gravity_(0), + gravity_(1), -gravity_(0), 0.0; + double align_norm = (hat_grav * tmp_gravity).norm() / gravity_.norm() / tmp_gravity.norm(); + double align_cos = gravity_.transpose() * tmp_gravity; + align_cos = align_cos / gravity_.norm() / tmp_gravity.norm(); + if (align_norm < 1e-6) + { + if (align_cos > 1e-6) + { + rot = Eye3d; + } + else + { + rot = -Eye3d; + } + } + else + { + V3D align_angle = hat_grav * tmp_gravity / (hat_grav * tmp_gravity).norm() * acos(align_cos); + rot = Exp(align_angle(0), align_angle(1), align_angle(2)); + } +} + +void ImuProcess::IMU_init(const MeasureGroup &meas, int &N) +{ + /** 1. initializing the gravity, gyro bias, acc and gyro covariance + ** 2. normalize the acceleration measurenments to unit gravity **/ + ROS_INFO("IMU Initializing: %.1f %%", double(N) / MAX_INI_COUNT * 100); + V3D cur_acc, cur_gyr; + + if (b_first_frame_) + { + Reset(); + N = 1; + b_first_frame_ = false; + const auto &imu_acc = meas.imu.front()->linear_acceleration; + const auto &gyr_acc = meas.imu.front()->angular_velocity; + mean_acc << imu_acc.x, imu_acc.y, imu_acc.z; + mean_gyr << gyr_acc.x, gyr_acc.y, gyr_acc.z; + } + + for (const auto &imu : meas.imu) + { + const auto &imu_acc = imu->linear_acceleration; + const auto &gyr_acc = imu->angular_velocity; + cur_acc << imu_acc.x, imu_acc.y, imu_acc.z; + cur_gyr << gyr_acc.x, gyr_acc.y, gyr_acc.z; + + mean_acc += (cur_acc - mean_acc) / N; + mean_gyr += (cur_gyr - mean_gyr) / N; + + N ++; + } +} + +void ImuProcess::Process(const MeasureGroup &meas, PointCloudXYZI::Ptr cur_pcl_un_) +{ + if (imu_en) + { + if(meas.imu.empty()) return; + + if (imu_need_init_) + { + + { + /// The very first lidar frame + IMU_init(meas, init_iter_num); + + imu_need_init_ = true; + + if (init_iter_num > MAX_INI_COUNT) + { + ROS_INFO("IMU Initializing: %.1f %%", 100.0); + imu_need_init_ = false; + *cur_pcl_un_ = *(meas.lidar); + } + // *cur_pcl_un_ = *(meas.lidar); + } + return; + } + if (!after_imu_init_) after_imu_init_ = true; + *cur_pcl_un_ = *(meas.lidar); + return; + } + else + { + *cur_pcl_un_ = *(meas.lidar); + return; + } +} \ No newline at end of file diff --git a/src/IMU_Processing.h b/src/IMU_Processing.h new file mode 100755 index 0000000..a7f888e --- /dev/null +++ b/src/IMU_Processing.h @@ -0,0 +1,59 @@ +#pragma once +#include +#include +// #include +// #include +// #include +#include +#include +// #include +#include +// #include "Estimator.h" +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +/// *************Preconfiguration + +#define MAX_INI_COUNT (100) +const bool time_list(PointType &x, PointType &y); // {return (x.curvature < y.curvature);}; + +/// *************IMU Process and undistortion +class ImuProcess +{ + public: + EIGEN_MAKE_ALIGNED_OPERATOR_NEW + + ImuProcess(); + ~ImuProcess(); + + void Reset(); + void Process(const MeasureGroup &meas, PointCloudXYZI::Ptr pcl_un_); + void set_gyr_cov(const V3D &scaler); + void set_acc_cov(const V3D &scaler); + void Set_init(Eigen::Vector3d &tmp_gravity, Eigen::Matrix3d &rot); + + MD(12, 12) state_cov = MD(12, 12)::Identity(); + int lidar_type; + V3D gravity_; + bool imu_en; + V3D mean_acc; + bool imu_need_init_ = true; + bool after_imu_init_ = false; + bool b_first_frame_ = true; + double time_last_scan = 0.0; + V3D cov_gyr_scale = V3D(0.0001, 0.0001, 0.0001); + V3D cov_vel_scale = V3D(0.0001, 0.0001, 0.0001); + + private: + void IMU_init(const MeasureGroup &meas, int &N); + V3D mean_gyr; + int init_iter_num = 1; +}; diff --git a/src/laserMapping.cpp b/src/laserMapping.cpp new file mode 100755 index 0000000..c966403 --- /dev/null +++ b/src/laserMapping.cpp @@ -0,0 +1,1070 @@ +// #include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include "li_initialization.h" +#include +// #include +// #include "matplotlibcpp.h" +// #include + +using namespace std; + +#define PUBFRAME_PERIOD (20) + +const float MOV_THRESHOLD = 1.5f; + +string root_dir = ROOT_DIR; + +int time_log_counter = 0; //, publish_count = 0; + +bool init_map = false, flg_first_scan = true; + +// Time Log Variables +double match_time = 0, solve_time = 0, propag_time = 0, update_time = 0; + +bool flg_reset = false, flg_exit = false; + +//surf feature in map +PointCloudXYZI::Ptr feats_undistort(new PointCloudXYZI()); +PointCloudXYZI::Ptr feats_down_body_space(new PointCloudXYZI()); +PointCloudXYZI::Ptr init_feats_world(new PointCloudXYZI()); +std::deque depth_feats_world; +pcl::VoxelGrid downSizeFilterSurf; +pcl::VoxelGrid downSizeFilterMap; + +V3D euler_cur; + +nav_msgs::Path path; +nav_msgs::Odometry odomAftMapped; +geometry_msgs::PoseStamped msg_body_pose; + +void SigHandle(int sig) +{ + flg_exit = true; + ROS_WARN("catch sig %d", sig); + sig_buffer.notify_all(); +} + +inline void dump_lio_state_to_log(FILE *fp) +{ + V3D rot_ang; + if (!use_imu_as_input) + { + rot_ang = SO3ToEuler(kf_output.x_.rot); + } + else + { + rot_ang = SO3ToEuler(kf_input.x_.rot); + } + + fprintf(fp, "%lf ", Measures.lidar_beg_time - first_lidar_time); + fprintf(fp, "%lf %lf %lf ", rot_ang(0), rot_ang(1), rot_ang(2)); // Angle + if (use_imu_as_input) + { + fprintf(fp, "%lf %lf %lf ", kf_input.x_.pos(0), kf_input.x_.pos(1), kf_input.x_.pos(2)); // Pos + fprintf(fp, "%lf %lf %lf ", 0.0, 0.0, 0.0); // omega + fprintf(fp, "%lf %lf %lf ", kf_input.x_.vel(0), kf_input.x_.vel(1), kf_input.x_.vel(2)); // Vel + fprintf(fp, "%lf %lf %lf ", 0.0, 0.0, 0.0); // Acc + fprintf(fp, "%lf %lf %lf ", kf_input.x_.bg(0), kf_input.x_.bg(1), kf_input.x_.bg(2)); // Bias_g + fprintf(fp, "%lf %lf %lf ", kf_input.x_.ba(0), kf_input.x_.ba(1), kf_input.x_.ba(2)); // Bias_a + fprintf(fp, "%lf %lf %lf ", kf_input.x_.gravity(0), kf_input.x_.gravity(1), kf_input.x_.gravity(2)); // Bias_a + } + else + { + fprintf(fp, "%lf %lf %lf ", kf_output.x_.pos(0), kf_output.x_.pos(1), kf_output.x_.pos(2)); // Pos + fprintf(fp, "%lf %lf %lf ", 0.0, 0.0, 0.0); // omega + fprintf(fp, "%lf %lf %lf ", kf_output.x_.vel(0), kf_output.x_.vel(1), kf_output.x_.vel(2)); // Vel + fprintf(fp, "%lf %lf %lf ", 0.0, 0.0, 0.0); // Acc + fprintf(fp, "%lf %lf %lf ", kf_output.x_.bg(0), kf_output.x_.bg(1), kf_output.x_.bg(2)); // Bias_g + fprintf(fp, "%lf %lf %lf ", kf_output.x_.ba(0), kf_output.x_.ba(1), kf_output.x_.ba(2)); // Bias_a + fprintf(fp, "%lf %lf %lf ", kf_output.x_.gravity(0), kf_output.x_.gravity(1), kf_output.x_.gravity(2)); // Bias_a + } + fprintf(fp, "\r\n"); + fflush(fp); +} + +void pointBodyLidarToIMU(PointType const * const pi, PointType * const po) +{ + V3D p_body_lidar(pi->x, pi->y, pi->z); + V3D p_body_imu; + if (extrinsic_est_en) + { + if (!use_imu_as_input) + { + p_body_imu = kf_output.x_.offset_R_L_I * p_body_lidar + kf_output.x_.offset_T_L_I; + } + else + { + p_body_imu = kf_input.x_.offset_R_L_I * p_body_lidar + kf_input.x_.offset_T_L_I; + } + } + else + { + p_body_imu = Lidar_R_wrt_IMU * p_body_lidar + Lidar_T_wrt_IMU; + } + po->x = p_body_imu(0); + po->y = p_body_imu(1); + po->z = p_body_imu(2); + po->intensity = pi->intensity; +} + +void MapIncremental() { + PointVector points_to_add; + int cur_pts = feats_down_world->size(); + points_to_add.reserve(cur_pts); + + for (size_t i = 0; i < cur_pts; ++i) { + /* decide if need add to map */ + PointType &point_world = feats_down_world->points[i]; + if (!Nearest_Points[i].empty()) { + const PointVector &points_near = Nearest_Points[i]; + + Eigen::Vector3f center = + ((point_world.getVector3fMap() / filter_size_map_min).array().floor() + 0.5) * filter_size_map_min; + bool need_add = true; + for (int readd_i = 0; readd_i < points_near.size(); readd_i++) { + Eigen::Vector3f dis_2_center = points_near[readd_i].getVector3fMap() - center; + if (fabs(dis_2_center.x()) < 0.5 * filter_size_map_min && + fabs(dis_2_center.y()) < 0.5 * filter_size_map_min && + fabs(dis_2_center.z()) < 0.5 * filter_size_map_min) { + need_add = false; + break; + } + } + if (need_add) { + points_to_add.emplace_back(point_world); + } + } else { + points_to_add.emplace_back(point_world); + } + } + ivox_->AddPoints(points_to_add); +} + +void publish_init_map(const ros::Publisher & pubLaserCloudFullRes) +{ + int size_init_map = init_feats_world->size(); + + sensor_msgs::PointCloud2 laserCloudmsg; + + pcl::toROSMsg(*init_feats_world, laserCloudmsg); + + laserCloudmsg.header.stamp = ros::Time().fromSec(lidar_end_time); + laserCloudmsg.header.frame_id = "camera_init"; + pubLaserCloudFullRes.publish(laserCloudmsg); +} + +PointCloudXYZI::Ptr pcl_wait_pub(new PointCloudXYZI(500000, 1)); +PointCloudXYZI::Ptr pcl_wait_save(new PointCloudXYZI()); +void publish_frame_world(const ros::Publisher & pubLaserCloudFullRes) +{ + if (scan_pub_en) + { + PointCloudXYZI::Ptr laserCloudFullRes(feats_down_body); + int size = laserCloudFullRes->points.size(); + + PointCloudXYZI::Ptr laserCloudWorld(new PointCloudXYZI(size, 1)); + + for (int i = 0; i < size; i++) + { + laserCloudWorld->points[i].x = feats_down_world->points[i].x; + laserCloudWorld->points[i].y = feats_down_world->points[i].y; + laserCloudWorld->points[i].z = feats_down_world->points[i].z; + laserCloudWorld->points[i].intensity = feats_down_world->points[i].intensity; // feats_down_world->points[i].y; // + } + sensor_msgs::PointCloud2 laserCloudmsg; + pcl::toROSMsg(*laserCloudWorld, laserCloudmsg); + + laserCloudmsg.header.stamp = ros::Time().fromSec(lidar_end_time); + laserCloudmsg.header.frame_id = "camera_init"; + pubLaserCloudFullRes.publish(laserCloudmsg); + // publish_count -= PUBFRAME_PERIOD; + } + + /**************** save map ****************/ + /* 1. make sure you have enough memories + /* 2. noted that pcd save will influence the real-time performences **/ + if (pcd_save_en) + { + int size = feats_down_world->points.size(); + PointCloudXYZI::Ptr laserCloudWorld(new PointCloudXYZI(size, 1)); + + for (int i = 0; i < size; i++) + { + laserCloudWorld->points[i].x = feats_down_world->points[i].x; + laserCloudWorld->points[i].y = feats_down_world->points[i].y; + laserCloudWorld->points[i].z = feats_down_world->points[i].z; + laserCloudWorld->points[i].intensity = feats_down_world->points[i].intensity; + } + + *pcl_wait_save += *laserCloudWorld; + + static int scan_wait_num = 0; + scan_wait_num ++; + if (pcl_wait_save->size() > 0 && pcd_save_interval > 0 && scan_wait_num >= pcd_save_interval) + { + pcd_index ++; + string all_points_dir(string(string(ROOT_DIR) + "PCD/scans_") + to_string(pcd_index) + string(".pcd")); + pcl::PCDWriter pcd_writer; + cout << "current scan saved to /PCD/" << all_points_dir << endl; + pcd_writer.writeBinary(all_points_dir, *pcl_wait_save); + pcl_wait_save->clear(); + scan_wait_num = 0; + } + } +} + +void publish_frame_body(const ros::Publisher & pubLaserCloudFull_body) +{ + int size = feats_undistort->points.size(); + PointCloudXYZI::Ptr laserCloudIMUBody(new PointCloudXYZI(size, 1)); + + for (int i = 0; i < size; i++) + { + pointBodyLidarToIMU(&feats_undistort->points[i], \ + &laserCloudIMUBody->points[i]); + } + + sensor_msgs::PointCloud2 laserCloudmsg; + pcl::toROSMsg(*laserCloudIMUBody, laserCloudmsg); + laserCloudmsg.header.stamp = ros::Time().fromSec(lidar_end_time); + laserCloudmsg.header.frame_id = "body"; + pubLaserCloudFull_body.publish(laserCloudmsg); + // publish_count -= PUBFRAME_PERIOD; +} + +template +void set_posestamp(T & out) +{ + if (!use_imu_as_input) + { + out.position.x = kf_output.x_.pos(0); + out.position.y = kf_output.x_.pos(1); + out.position.z = kf_output.x_.pos(2); + Eigen::Quaterniond q(kf_output.x_.rot); + out.orientation.x = q.coeffs()[0]; + out.orientation.y = q.coeffs()[1]; + out.orientation.z = q.coeffs()[2]; + out.orientation.w = q.coeffs()[3]; + } + else + { + out.position.x = kf_input.x_.pos(0); + out.position.y = kf_input.x_.pos(1); + out.position.z = kf_input.x_.pos(2); + Eigen::Quaterniond q(kf_input.x_.rot); + out.orientation.x = q.coeffs()[0]; + out.orientation.y = q.coeffs()[1]; + out.orientation.z = q.coeffs()[2]; + out.orientation.w = q.coeffs()[3]; + } +} + +void publish_odometry(const ros::Publisher & pubOdomAftMapped) +{ + odomAftMapped.header.frame_id = "camera_init"; + odomAftMapped.child_frame_id = "aft_mapped"; + if (publish_odometry_without_downsample) + { + odomAftMapped.header.stamp = ros::Time().fromSec(time_current); + } + else + { + odomAftMapped.header.stamp = ros::Time().fromSec(lidar_end_time); + } + set_posestamp(odomAftMapped.pose.pose); + + pubOdomAftMapped.publish(odomAftMapped); + + static tf::TransformBroadcaster br; + tf::Transform transform; + tf::Quaternion q; + transform.setOrigin(tf::Vector3(odomAftMapped.pose.pose.position.x, \ + odomAftMapped.pose.pose.position.y, \ + odomAftMapped.pose.pose.position.z)); + q.setW(odomAftMapped.pose.pose.orientation.w); + q.setX(odomAftMapped.pose.pose.orientation.x); + q.setY(odomAftMapped.pose.pose.orientation.y); + q.setZ(odomAftMapped.pose.pose.orientation.z); + transform.setRotation( q ); + br.sendTransform( tf::StampedTransform( transform, odomAftMapped.header.stamp, "camera_init", "aft_mapped" ) ); +} + +void publish_path(const ros::Publisher pubPath) +{ + set_posestamp(msg_body_pose.pose); + // msg_body_pose.header.stamp = ros::Time::now(); + msg_body_pose.header.stamp = ros::Time().fromSec(lidar_end_time); + msg_body_pose.header.frame_id = "camera_init"; + static int jjj = 0; + jjj++; + // if (jjj % 2 == 0) // if path is too large, the rvis will crash + { + path.poses.emplace_back(msg_body_pose); + pubPath.publish(path); + } +} + +int main(int argc, char** argv) +{ + ros::init(argc, argv, "laserMapping"); + ros::NodeHandle nh("~"); + ros::AsyncSpinner spinner(0); + spinner.start(); + readParameters(nh); + cout<<"lidar_type: "<(ivox_options_); + + path.header.stamp = ros::Time().fromSec(lidar_end_time); + path.header.frame_id ="camera_init"; + + /*** variables definition for counting ***/ + int frame_num = 0; + double aver_time_consu = 0, aver_time_icp = 0, aver_time_match = 0, aver_time_incre = 0, aver_time_solve = 0, aver_time_propag = 0; + + memset(point_selected_surf, true, sizeof(point_selected_surf)); + downSizeFilterSurf.setLeafSize(filter_size_surf_min, filter_size_surf_min, filter_size_surf_min); + downSizeFilterMap.setLeafSize(filter_size_map_min, filter_size_map_min, filter_size_map_min); + + Lidar_T_wrt_IMU<lidar_type = p_pre->lidar_type = lidar_type; + p_imu->imu_en = imu_en; + + kf_input.init_dyn_share_modified_2h(get_f_input, df_dx_input, h_model_input); + kf_output.init_dyn_share_modified_3h(get_f_output, df_dx_output, h_model_output, h_model_IMU_output); + Eigen::Matrix P_init; // = MD(18, 18)::Identity() * 0.1; + reset_cov(P_init); + kf_input.change_P(P_init); + Eigen::Matrix P_init_output; // = MD(24, 24)::Identity() * 0.01; + reset_cov_output(P_init_output); + kf_output.change_P(P_init_output); + Eigen::Matrix Q_input = process_noise_cov_input(); + Eigen::Matrix Q_output = process_noise_cov_output(); + /*** debug record ***/ + FILE *fp; + string pos_log_dir = root_dir + "/Log/pos_log.txt"; + fp = fopen(pos_log_dir.c_str(),"w"); + open_file(); + + /*** ROS subscribe initialization ***/ + ros::Subscriber sub_pcl = p_pre->lidar_type == AVIA ? \ + nh.subscribe(lid_topic, 200000, livox_pcl_cbk) : \ + nh.subscribe(lid_topic, 200000, standard_pcl_cbk); + ros::Subscriber sub_imu = nh.subscribe(imu_topic, 200000, imu_cbk); + + ros::Publisher pubLaserCloudFullRes = nh.advertise + ("/cloud_registered", 1000); + ros::Publisher pubLaserCloudFullRes_body = nh.advertise + ("/cloud_registered_body", 1000); + ros::Publisher pubLaserCloudEffect = nh.advertise + ("/cloud_effected", 1000); + ros::Publisher pubLaserCloudMap = nh.advertise + ("/Laser_map", 1000); + ros::Publisher pubOdomAftMapped = nh.advertise + ("/aft_mapped_to_init", 1000); + ros::Publisher pubPath = nh.advertise + ("/path", 1000); + ros::Publisher plane_pub = nh.advertise + ("/planner_normal", 1000); +//------------------------------------------------------------------------------------------------------ + signal(SIGINT, SigHandle); + ros::Rate loop_rate(500); + bool status = ros::ok(); + while (status) + { + if (flg_exit) break; + ros::spinOnce(); + if(sync_packages(Measures)) + { + if (flg_reset) + { + ROS_WARN("reset when rosbag play back"); + p_imu->Reset(); + feats_undistort.reset(new PointCloudXYZI()); + if (use_imu_as_input) + { + // state_in = kf_input.get_x(); + state_in = state_input(); + kf_input.change_P(P_init); + } + else + { + // state_out = kf_output.get_x(); + state_out = state_output(); + kf_output.change_P(P_init_output); + } + flg_first_scan = true; + is_first_frame = true; + flg_reset = false; + init_map = false; + + { + ivox_.reset(new IVoxType(ivox_options_)); + } + } + + if (flg_first_scan) + { + first_lidar_time = Measures.lidar_beg_time; + flg_first_scan = false; + if (first_imu_time < 1) + { + first_imu_time = imu_next.header.stamp.toSec(); + printf("first imu time: %f\n", first_imu_time); + } + time_current = 0.0; + if(imu_en) + { + // imu_next = *(imu_deque.front()); + kf_input.x_.gravity << VEC_FROM_ARRAY(gravity); + kf_output.x_.gravity << VEC_FROM_ARRAY(gravity); + // kf_output.x_.acc << VEC_FROM_ARRAY(gravity); + // kf_output.x_.acc *= -1; + + { + while (Measures.lidar_beg_time > imu_next.header.stamp.toSec()) // if it is needed for the new map? + { + imu_deque.pop_front(); + if (imu_deque.empty()) + { + break; + } + imu_last = imu_next; + imu_next = *(imu_deque.front()); + // imu_deque.pop(); + } + } + } + else + { + kf_input.x_.gravity << VEC_FROM_ARRAY(gravity_init); + kf_output.x_.gravity << VEC_FROM_ARRAY(gravity_init); + kf_output.x_.acc << VEC_FROM_ARRAY(gravity_init); + kf_output.x_.acc *= -1; + p_imu->imu_need_init_ = false; + // p_imu->after_imu_init_ = true; + } + } + + double t0,t1,t2,t3,t4,t5,match_start, solve_start; + match_time = 0; + solve_time = 0; + propag_time = 0; + update_time = 0; + t0 = omp_get_wtime(); + + /*** downsample the feature points in a scan ***/ + t1 = omp_get_wtime(); + p_imu->Process(Measures, feats_undistort); + if(space_down_sample) + { + downSizeFilterSurf.setInputCloud(feats_undistort); + downSizeFilterSurf.filter(*feats_down_body); + sort(feats_down_body->points.begin(), feats_down_body->points.end(), time_list); + } + else + { + feats_down_body = Measures.lidar; + sort(feats_down_body->points.begin(), feats_down_body->points.end(), time_list); + } + { + time_seq = time_compressing(feats_down_body); + feats_down_size = feats_down_body->points.size(); + } + + if (!p_imu->after_imu_init_) + { + if (!p_imu->imu_need_init_) + { + V3D tmp_gravity; + if (imu_en) + {tmp_gravity = - p_imu->mean_acc / acc_norm * G_m_s2;} + else + {tmp_gravity << VEC_FROM_ARRAY(gravity_init); + p_imu->after_imu_init_ = true; + } + // V3D tmp_gravity << VEC_FROM_ARRAY(gravity_init); + M3D rot_init; + p_imu->Set_init(tmp_gravity, rot_init); + kf_input.x_.rot = rot_init; + kf_output.x_.rot = rot_init; + // kf_input.x_.rot; //.normalize(); + // kf_output.x_.rot; //.normalize(); + kf_output.x_.acc = - rot_init.transpose() * kf_output.x_.gravity; + } + else{ + continue;} + } + /*** initialize the map ***/ + if(!init_map) + { + feats_down_world->resize(feats_undistort->size()); + for(int i = 0; i < feats_undistort->size(); i++) + { + { + pointBodyToWorld(&(feats_undistort->points[i]), &(feats_down_world->points[i])); + } + } + for (size_t i = 0; i < feats_down_world->size(); i++) + { + init_feats_world->points.emplace_back(feats_down_world->points[i]); + } + if(init_feats_world->size() < init_map_size) + {init_map = false;} + else + { + ivox_->AddPoints(init_feats_world->points); + publish_init_map(pubLaserCloudMap); //(pubLaserCloudFullRes); + + init_feats_world.reset(new PointCloudXYZI()); + init_map = true; + } + continue; + } + + /*** ICP and Kalman filter update ***/ + normvec->resize(feats_down_size); + feats_down_world->resize(feats_down_size); + + Nearest_Points.resize(feats_down_size); + + t2 = omp_get_wtime(); + + /*** iterated state estimation ***/ + crossmat_list.reserve(feats_down_size); + pbody_list.reserve(feats_down_size); + // pbody_ext_list.reserve(feats_down_size); + + for (size_t i = 0; i < feats_down_body->size(); i++) + { + V3D point_this(feats_down_body->points[i].x, + feats_down_body->points[i].y, + feats_down_body->points[i].z); + pbody_list[i]=point_this; + if (!extrinsic_est_en) + // { + // if (!use_imu_as_input) + // { + // point_this = kf_output.x_.offset_R_L_I * point_this + kf_output.x_.offset_T_L_I; + // } + // else + // { + // point_this = kf_input.x_.offset_R_L_I * point_this + kf_input.x_.offset_T_L_I; + // } + // } + // else + { + point_this = Lidar_R_wrt_IMU * point_this + Lidar_T_wrt_IMU; + M3D point_crossmat; + point_crossmat << SKEW_SYM_MATRX(point_this); + crossmat_list[i]=point_crossmat; + } + } + if (!use_imu_as_input) + { + bool imu_upda_cov = false; + effct_feat_num = 0; + /**** point by point update ****/ + if (time_seq.size() > 0) + { + double pcl_beg_time = Measures.lidar_beg_time; + idx = -1; + for (k = 0; k < time_seq.size(); k++) + { + PointType &point_body = feats_down_body->points[idx+time_seq[k]]; + + time_current = point_body.curvature / 1000.0 + pcl_beg_time; + + if (is_first_frame) + { + if(imu_en) + { + while (time_current > imu_next.header.stamp.toSec()) + { + imu_deque.pop_front(); + if (imu_deque.empty()) break; + imu_last = imu_next; + imu_next = *(imu_deque.front()); + } + angvel_avr<header.stamp.toSec(); + while (imu_next.header.stamp.toSec() < time_predict_last_const && !imu_deque.empty()) + { + if (!last_imu) + { + imu_last = imu_next; + imu_next = *(imu_deque.front()); + break; + } + else + { + imu_deque.pop_front(); + if (imu_deque.empty()) break; + imu_last = imu_next; + imu_next = *(imu_deque.front()); + } + } + bool imu_comes = time_current > imu_next.header.stamp.toSec(); + while (imu_comes) + { + imu_upda_cov = true; + angvel_avr< 0.0) + { + time_update_last = imu_next.header.stamp.toSec(); + double propag_imu_start = omp_get_wtime(); + + kf_output.predict(dt_cov, Q_output, input_in, false, true); + + propag_time += omp_get_wtime() - propag_imu_start; + double solve_imu_start = omp_get_wtime(); + kf_output.update_iterated_dyn_share_IMU(); + solve_time += omp_get_wtime() - solve_imu_start; + } + } + imu_deque.pop_front(); + if (imu_deque.empty()) break; + imu_last = imu_next; + imu_next = *(imu_deque.front()); + imu_comes = time_current > imu_next.header.stamp.toSec(); + } + } + if (flg_reset) + { + break; + } + + double dt = time_current - time_predict_last_const; + double propag_state_start = omp_get_wtime(); + if(!prop_at_freq_of_imu) + { + double dt_cov = time_current - time_update_last; + if (dt_cov > 0.0) + { + kf_output.predict(dt_cov, Q_output, input_in, false, true); + time_update_last = time_current; + } + } + kf_output.predict(dt, Q_output, input_in, true, false); + propag_time += omp_get_wtime() - propag_state_start; + time_predict_last_const = time_current; + double t_update_start = omp_get_wtime(); + + if (feats_down_size < 1) + { + ROS_WARN("No point, skip this scan!\n"); + idx += time_seq[k]; + continue; + } + if (!kf_output.update_iterated_dyn_share_modified()) + { + idx = idx+time_seq[k]; + continue; + } + solve_start = omp_get_wtime(); + + if (publish_odometry_without_downsample) + { + /******* Publish odometry *******/ + + publish_odometry(pubOdomAftMapped); + if (runtime_pos_log) + { + euler_cur = SO3ToEuler(kf_output.x_.rot); + fout_out << setw(20) << Measures.lidar_beg_time - first_lidar_time << " " << euler_cur.transpose() << " " << kf_output.x_.pos.transpose() << " " << kf_output.x_.vel.transpose() \ + <<" "<points.size()<points[idx+j+1]; + PointType &point_world_j = feats_down_world->points[idx+j+1]; + pointBodyToWorld(&point_body_j, &point_world_j); + } + + solve_time += omp_get_wtime() - solve_start; + + update_time += omp_get_wtime() - t_update_start; + idx += time_seq[k]; + // cout << "pbp output effect feat num:" << effct_feat_num << endl; + } + } + else + { + if (!imu_deque.empty()) + { + imu_last = imu_next; + imu_next = *(imu_deque.front()); + + while (imu_next.header.stamp.toSec() > time_current && ((imu_next.header.stamp.toSec() < Measures.lidar_beg_time + lidar_time_inte ))) + { // >= ? + if (is_first_frame) + { + { + { + while (imu_next.header.stamp.toSec() < Measures.lidar_beg_time + lidar_time_inte) + { + // meas.imu.emplace_back(imu_deque.front()); should add to initialization + imu_deque.pop_front(); + if(imu_deque.empty()) break; + imu_last = imu_next; + imu_next = *(imu_deque.front()); + } + } + break; + } + angvel_avr< 0.0) + { + kf_output.predict(dt_cov, Q_output, input_in, false, true); + time_update_last = time_current; + } + kf_output.predict(dt, Q_output, input_in, true, false); + } + + time_predict_last_const = time_current; + + angvel_avr< 0) + { + double pcl_beg_time = Measures.lidar_beg_time; + idx = -1; + for (k = 0; k < time_seq.size(); k++) + { + PointType &point_body = feats_down_body->points[idx+time_seq[k]]; + time_current = point_body.curvature / 1000.0 + pcl_beg_time; + if (is_first_frame) + { + while (time_current > imu_next.header.stamp.toSec()) + { + imu_deque.pop_front(); + if (imu_deque.empty()) break; + imu_last = imu_next; + imu_next = *(imu_deque.front()); + } + imu_prop_cov = true; + + is_first_frame = false; + t_last = time_current; + time_update_last = time_current; + { + input_in.gyro< imu_next.header.stamp.toSec()) // && !imu_deque.empty()) + { + imu_deque.pop_front(); + + input_in.gyro< 0.0) + { + kf_input.predict(dt_cov, Q_input, input_in, false, true); + time_update_last = imu_last.header.stamp.toSec(); //time_current; + } + kf_input.predict(dt, Q_input, input_in, true, false); + t_last = imu_last.header.stamp.toSec(); + imu_prop_cov = true; + + if (imu_deque.empty()) break; + imu_last = imu_next; + imu_next = *(imu_deque.front()); + // imu_upda_cov = true; + } + if (flg_reset) + { + break; + } + double dt = time_current - t_last; + t_last = time_current; + double propag_start = omp_get_wtime(); + + if(!prop_at_freq_of_imu) + { + double dt_cov = time_current - time_update_last; + if (dt_cov > 0.0) + { + kf_input.predict(dt_cov, Q_input, input_in, false, true); + time_update_last = time_current; + } + } + kf_input.predict(dt, Q_input, input_in, true, false); + + propag_time += omp_get_wtime() - propag_start; + + double t_update_start = omp_get_wtime(); + + if (feats_down_size < 1) + { + ROS_WARN("No point, skip this scan!\n"); + + idx += time_seq[k]; + continue; + } + if (!kf_input.update_iterated_dyn_share_modified()) + { + idx = idx+time_seq[k]; + continue; + } + + solve_start = omp_get_wtime(); + + if (publish_odometry_without_downsample) + { + /******* Publish odometry *******/ + + publish_odometry(pubOdomAftMapped); + if (runtime_pos_log) + { + euler_cur = SO3ToEuler(kf_input.x_.rot); + fout_out << setw(20) << Measures.lidar_beg_time - first_lidar_time << " " << euler_cur.transpose() << " " << kf_input.x_.pos.transpose() << " " << kf_input.x_.vel.transpose() \ + <<" "<points.size()<points[idx+j+1]; + PointType &point_world_j = feats_down_world->points[idx+j+1]; + pointBodyToWorld(&point_body_j, &point_world_j); + } + solve_time += omp_get_wtime() - solve_start; + + update_time += omp_get_wtime() - t_update_start; + idx = idx + time_seq[k]; + } + } + else + { + if (!imu_deque.empty()) + { + imu_last = imu_next; + imu_next = *(imu_deque.front()); + while (imu_next.header.stamp.toSec() > time_current && ((imu_next.header.stamp.toSec() < Measures.lidar_beg_time + lidar_time_inte))) + { // >= ? + if (is_first_frame) + { + { + { + while (imu_next.header.stamp.toSec() < Measures.lidar_beg_time + lidar_time_inte) + { + imu_deque.pop_front(); + if(imu_deque.empty()) break; + imu_last = imu_next; + imu_next = *(imu_deque.front()); + } + } + + break; + } + imu_prop_cov = true; + + t_last = time_current; + time_update_last = time_current; + input_in.gyro< 0.0) + { + // kf_input.predict(dt_cov, Q_input, input_in, false, true); + time_update_last = imu_next.header.stamp.toSec(); //time_current; + } + // kf_input.predict(dt, Q_input, input_in, true, false); + + t_last = imu_next.header.stamp.toSec(); + + input_in.gyro< 4) + { + MapIncremental(); + } + + t5 = omp_get_wtime(); + /******* Publish points *******/ + if (path_en) publish_path(pubPath); + if (scan_pub_en || pcd_save_en) publish_frame_world(pubLaserCloudFullRes); + if (scan_pub_en && scan_body_pub_en) publish_frame_body(pubLaserCloudFullRes_body); + + /*** Debug variables Logging ***/ + if (runtime_pos_log) + { + frame_num ++; + aver_time_consu = aver_time_consu * (frame_num - 1) / frame_num + (t5 - t0) / frame_num; + {aver_time_icp = aver_time_icp * (frame_num - 1)/frame_num + update_time/frame_num;} + aver_time_match = aver_time_match * (frame_num - 1)/frame_num + (match_time)/frame_num; + aver_time_solve = aver_time_solve * (frame_num - 1)/frame_num + solve_time/frame_num; + aver_time_propag = aver_time_propag * (frame_num - 1)/frame_num + propag_time / frame_num; + T1[time_log_counter] = Measures.lidar_beg_time; + s_plot[time_log_counter] = t5 - t0; + s_plot2[time_log_counter] = feats_undistort->points.size(); + s_plot3[time_log_counter] = aver_time_consu; + time_log_counter ++; + printf("[ mapping ]: time: IMU + Map + Input Downsample: %0.6f ave match: %0.6f ave solve: %0.6f ave ICP: %0.6f map incre: %0.6f ave total: %0.6f icp: %0.6f propogate: %0.6f \n",t1-t0,aver_time_match,aver_time_solve,t3-t1,t5-t3,aver_time_consu, aver_time_icp, aver_time_propag); + if (!publish_odometry_without_downsample) + { + if (!use_imu_as_input) + { + fout_out << setw(20) << Measures.lidar_beg_time - first_lidar_time << " " << euler_cur.transpose() << " " << kf_output.x_.pos.transpose() << " " << kf_output.x_.vel.transpose() \ + <<" "<points.size()<points.size()<size() > 0 && pcd_save_en) + { + string file_name = string("scans.pcd"); + string all_points_dir(string(string(ROOT_DIR) + "PCD/") + file_name); + pcl::PCDWriter pcd_writer; + pcd_writer.writeBinary(all_points_dir, *pcl_wait_save); + } + fout_out.close(); + fout_imu_pbp.close(); + return 0; +} diff --git a/src/li_initialization.cpp b/src/li_initialization.cpp new file mode 100755 index 0000000..1152bec --- /dev/null +++ b/src/li_initialization.cpp @@ -0,0 +1,348 @@ +#include "li_initialization.h" +bool data_accum_finished = false, data_accum_start = false, online_calib_finish = false, refine_print = false; +int frame_num_init = 0; +double time_lag_IMU_wtr_lidar = 0.0, move_start_time = 0.0, online_calib_starts_time = 0.0; //, mean_acc_norm = 9.81; +double imu_first_time = 0.0; +bool lose_lid = false; +double timediff_imu_wrt_lidar = 0.0; +bool timediff_set_flg = false; +V3D gravity_lio = V3D::Zero(); +mutex mtx_buffer; +sensor_msgs::Imu imu_last, imu_next; +// sensor_msgs::Imu::ConstPtr imu_last_ptr; +PointCloudXYZI::Ptr ptr_con(new PointCloudXYZI()); +double T1[MAXN], s_plot[MAXN], s_plot2[MAXN], s_plot3[MAXN], s_plot11[MAXN]; + +condition_variable sig_buffer; +int scan_count = 0; +int frame_ct = 0, wait_num = 0; +std::mutex m_time; +bool lidar_pushed = false, imu_pushed = false; +std::deque lidar_buffer; +std::deque time_buffer; +std::deque imu_deque; + +void standard_pcl_cbk(const sensor_msgs::PointCloud2::ConstPtr &msg) +{ + // mtx_buffer.lock(); + scan_count ++; + double preprocess_start_time = omp_get_wtime(); + if (msg->header.stamp.toSec() < last_timestamp_lidar) + { + ROS_ERROR("lidar loop back, clear buffer"); + // lidar_buffer.shrink_to_fit(); + + // mtx_buffer.unlock(); + // sig_buffer.notify_all(); + return; + } + + last_timestamp_lidar = msg->header.stamp.toSec(); + // printf("check lidar time %f\n", last_timestamp_lidar); + // if (abs(last_timestamp_imu - last_timestamp_lidar) > 1.0 && !timediff_set_flg && !imu_deque.empty()) { + // timediff_set_flg = true; + // timediff_imu_wrt_lidar = last_timestamp_imu - last_timestamp_lidar; + // printf("Self sync IMU and LiDAR, HARD time lag is %.10lf \n \n", timediff_imu_wrt_lidar); + // } + + if ((lidar_type == VELO16 || lidar_type == OUST64 || lidar_type == HESAIxt32) && cut_frame_init) { + deque ptr; + deque timestamp_lidar; + p_pre->process_cut_frame_pcl2(msg, ptr, timestamp_lidar, cut_frame_num, scan_count); + while (!ptr.empty() && !timestamp_lidar.empty()) { + lidar_buffer.push_back(ptr.front()); + ptr.pop_front(); + time_buffer.push_back(timestamp_lidar.front() / double(1000));//unit:s + timestamp_lidar.pop_front(); + } + } + else + { + PointCloudXYZI::Ptr ptr(new PointCloudXYZI(20000,1)); + p_pre->process(msg, ptr); + if (con_frame) + { + if (frame_ct == 0) + { + time_con = last_timestamp_lidar; //msg->header.stamp.toSec(); + } + if (frame_ct < 10) + { + for (int i = 0; i < ptr->size(); i++) + { + ptr->points[i].curvature += (last_timestamp_lidar - time_con) * 1000; + ptr_con->push_back(ptr->points[i]); + } + frame_ct ++; + } + else + { + PointCloudXYZI::Ptr ptr_con_i(new PointCloudXYZI(10000,1)); + // cout << "ptr div num:" << ptr_div->size() << endl; + *ptr_con_i = *ptr_con; + lidar_buffer.push_back(ptr_con_i); + double time_con_i = time_con; + time_buffer.push_back(time_con_i); + ptr_con->clear(); + frame_ct = 0; + } + } + else + { + lidar_buffer.emplace_back(ptr); + time_buffer.emplace_back(msg->header.stamp.toSec()); + } + } + s_plot11[scan_count] = omp_get_wtime() - preprocess_start_time; + // mtx_buffer.unlock(); + // sig_buffer.notify_all(); +} + +void livox_pcl_cbk(const livox_ros_driver::CustomMsg::ConstPtr &msg) +{ + // mtx_buffer.lock(); + double preprocess_start_time = omp_get_wtime(); + scan_count ++; + if (msg->header.stamp.toSec() < last_timestamp_lidar) + { + ROS_ERROR("lidar loop back, clear buffer"); + + // mtx_buffer.unlock(); + // sig_buffer.notify_all(); + return; + // lidar_buffer.shrink_to_fit(); + } + + last_timestamp_lidar = msg->header.stamp.toSec(); + // if (abs(last_timestamp_imu - last_timestamp_lidar) > 1.0 && !timediff_set_flg && !imu_deque.empty()) { + // timediff_set_flg = true; + // timediff_imu_wrt_lidar = last_timestamp_imu - last_timestamp_lidar; + // printf("Self sync IMU and LiDAR, HARD time lag is %.10lf \n \n", timediff_imu_wrt_lidar); + // } + + if (cut_frame_init) { + deque ptr; + deque timestamp_lidar; + p_pre->process_cut_frame_livox(msg, ptr, timestamp_lidar, cut_frame_num, scan_count); + + while (!ptr.empty() && !timestamp_lidar.empty()) { + lidar_buffer.push_back(ptr.front()); + ptr.pop_front(); + time_buffer.push_back(timestamp_lidar.front() / double(1000));//unit:s + timestamp_lidar.pop_front(); + } + } + else + { + PointCloudXYZI::Ptr ptr(new PointCloudXYZI(10000,1)); + p_pre->process(msg, ptr); + if (con_frame) + { + if (frame_ct == 0) + { + time_con = last_timestamp_lidar; //msg->header.stamp.toSec(); + } + if (frame_ct < 10) + { + for (int i = 0; i < ptr->size(); i++) + { + ptr->points[i].curvature += (last_timestamp_lidar - time_con) * 1000; + ptr_con->push_back(ptr->points[i]); + } + frame_ct ++; + } + else + { + PointCloudXYZI::Ptr ptr_con_i(new PointCloudXYZI(10000,1)); + // cout << "ptr div num:" << ptr_div->size() << endl; + *ptr_con_i = *ptr_con; + double time_con_i = time_con; + lidar_buffer.push_back(ptr_con_i); + time_buffer.push_back(time_con_i); + ptr_con->clear(); + frame_ct = 0; + } + } + else + { + lidar_buffer.emplace_back(ptr); + time_buffer.emplace_back(msg->header.stamp.toSec()); + } + } + s_plot11[scan_count] = omp_get_wtime() - preprocess_start_time; + // mtx_buffer.unlock(); + // sig_buffer.notify_all(); +} + +void imu_cbk(const sensor_msgs::Imu::ConstPtr &msg_in) +{ + // mtx_buffer.lock(); + + // publish_count ++; + sensor_msgs::Imu::Ptr msg(new sensor_msgs::Imu(*msg_in)); + + msg->header.stamp = ros::Time().fromSec(msg->header.stamp.toSec() - timediff_imu_wrt_lidar - time_lag_IMU_wtr_lidar); + + double timestamp = msg->header.stamp.toSec(); + // printf("time_diff%f, %f, %f\n", last_timestamp_imu - timestamp, last_timestamp_imu, timestamp); + + if (timestamp < last_timestamp_imu) + { + ROS_ERROR("imu loop back, clear deque"); + // imu_deque.shrink_to_fit(); + // cout << "check time:" << timestamp << ";" << last_timestamp_imu << endl; + // printf("time_diff%f, %f, %f\n", last_timestamp_imu - timestamp, last_timestamp_imu, timestamp); + + // mtx_buffer.unlock(); + // sig_buffer.notify_all(); + return; + } + imu_deque.emplace_back(msg); + last_timestamp_imu = timestamp; + // mtx_buffer.unlock(); + // sig_buffer.notify_all(); +} + +bool sync_packages(MeasureGroup &meas) +{ + { + if (!imu_en) + { + if (!lidar_buffer.empty()) + { + if (!lidar_pushed) + { + meas.lidar = lidar_buffer.front(); + meas.lidar_beg_time = time_buffer.front(); + lose_lid = false; + if(meas.lidar->points.size() < 1) + { + cout << "lose lidar" << std::endl; + // return false; + lose_lid = true; + } + else + { + double end_time = meas.lidar->points.back().curvature; + for (auto pt: meas.lidar->points) + { + if (pt.curvature > end_time) + { + end_time = pt.curvature; + } + } + lidar_end_time = meas.lidar_beg_time + end_time / double(1000); + meas.lidar_last_time = lidar_end_time; + } + lidar_pushed = true; + } + + time_buffer.pop_front(); + lidar_buffer.pop_front(); + lidar_pushed = false; + if (!lose_lid) + { + return true; + } + else + { + return false; + } + } + return false; + } + + if (lidar_buffer.empty() || imu_deque.empty()) + { + return false; + } + /*** push a lidar scan ***/ + if(!lidar_pushed) + { + lose_lid = false; + meas.lidar = lidar_buffer.front(); + meas.lidar_beg_time = time_buffer.front(); + if(meas.lidar->points.size() < 1) + { + cout << "lose lidar" << endl; + lose_lid = true; + // lidar_buffer.pop_front(); + // time_buffer.pop_front(); + // return false; + } + else + { + double end_time = meas.lidar->points.back().curvature; + for (auto pt: meas.lidar->points) + { + if (pt.curvature > end_time) + { + end_time = pt.curvature; + } + } + lidar_end_time = meas.lidar_beg_time + end_time / double(1000); + // cout << "check time lidar:" << end_time << endl; + meas.lidar_last_time = lidar_end_time; + } + lidar_pushed = true; + } + + if (!lose_lid && (last_timestamp_imu < lidar_end_time)) + { + return false; + } + if (lose_lid && last_timestamp_imu < meas.lidar_beg_time + lidar_time_inte) + { + return false; + } + + if (!lose_lid && !imu_pushed) + { + /*** push imu data, and pop from imu buffer ***/ + if (p_imu->imu_need_init_) + { + double imu_time = imu_deque.front()->header.stamp.toSec(); + imu_next = *(imu_deque.front()); + meas.imu.shrink_to_fit(); + while (imu_time < lidar_end_time) + { + meas.imu.emplace_back(imu_deque.front()); + imu_last = imu_next; + imu_deque.pop_front(); + if(imu_deque.empty()) break; + imu_time = imu_deque.front()->header.stamp.toSec(); // can be changed + imu_next = *(imu_deque.front()); + } + } + imu_pushed = true; + } + + if (lose_lid && !imu_pushed) + { + /*** push imu data, and pop from imu buffer ***/ + if (p_imu->imu_need_init_) + { + double imu_time = imu_deque.front()->header.stamp.toSec(); + meas.imu.shrink_to_fit(); + + imu_next = *(imu_deque.front()); + while (imu_time < meas.lidar_beg_time + lidar_time_inte) + { + meas.imu.emplace_back(imu_deque.front()); + imu_last = imu_next; + imu_deque.pop_front(); + if(imu_deque.empty()) break; + imu_time = imu_deque.front()->header.stamp.toSec(); // can be changed + imu_next = *(imu_deque.front()); + } + } + imu_pushed = true; + } + + lidar_buffer.pop_front(); + time_buffer.pop_front(); + lidar_pushed = false; + imu_pushed = false; + return true; + } +} diff --git a/src/li_initialization.h b/src/li_initialization.h new file mode 100755 index 0000000..555773c --- /dev/null +++ b/src/li_initialization.h @@ -0,0 +1,36 @@ +#pragma once + +#include +#include "Estimator.h" +#define MAXN (720000) + +extern bool data_accum_finished, data_accum_start, online_calib_finish, refine_print; +extern int frame_num_init; +extern double time_lag_IMU_wtr_lidar, move_start_time, online_calib_starts_time; //, mean_acc_norm = 9.81; + +extern double timediff_imu_wrt_lidar; +extern bool timediff_set_flg; +extern V3D gravity_lio; +extern mutex mtx_buffer; +extern condition_variable sig_buffer; +extern int scan_count; +extern int frame_ct, wait_num; +extern std::deque lidar_buffer; +extern std::deque time_buffer; +extern std::deque imu_deque; +extern std::mutex m_time; +extern bool lidar_pushed, imu_pushed; +extern double imu_first_time; +extern bool lose_lid; +extern sensor_msgs::Imu imu_last, imu_next; +extern PointCloudXYZI::Ptr ptr_con; +extern double T1[MAXN], s_plot[MAXN], s_plot2[MAXN], s_plot3[MAXN], s_plot11[MAXN]; + +// extern sensor_msgs::Imu::ConstPtr imu_last_ptr; + +void standard_pcl_cbk(const sensor_msgs::PointCloud2::ConstPtr &msg); +void livox_pcl_cbk(const livox_ros_driver::CustomMsg::ConstPtr &msg); +void imu_cbk(const sensor_msgs::Imu::ConstPtr &msg_in); +bool sync_packages(MeasureGroup &meas); + +// #endif \ No newline at end of file diff --git a/src/parameters.cpp b/src/parameters.cpp new file mode 100755 index 0000000..067ff4c --- /dev/null +++ b/src/parameters.cpp @@ -0,0 +1,170 @@ +#include "parameters.h" + +bool is_first_frame = true; +double lidar_end_time = 0.0, first_lidar_time = 0.0, time_con = 0.0; +double last_timestamp_lidar = -1.0, last_timestamp_imu = -1.0; +int pcd_index = 0; +IVoxType::Options ivox_options_; +int ivox_nearby_type = 6; + +std::vector extrinT(3, 0.0); +std::vector extrinR(9, 0.0); +state_input state_in; +state_output state_out; +std::string lid_topic, imu_topic; +bool prop_at_freq_of_imu = true, check_satu = true, con_frame = false, cut_frame = false; +bool use_imu_as_input = false, space_down_sample = true, publish_odometry_without_downsample = false; +int init_map_size = 10, con_frame_num = 1; +double match_s = 81, satu_acc, satu_gyro, cut_frame_time_interval = 0.1; +float plane_thr = 0.1f; +double filter_size_surf_min = 0.5, filter_size_map_min = 0.5, fov_deg = 180; +// double cube_len = 2000; +float DET_RANGE = 450; +bool imu_en = true; +double imu_time_inte = 0.005; +double laser_point_cov = 0.01, acc_norm; +double vel_cov, acc_cov_input, gyr_cov_input; +double gyr_cov_output, acc_cov_output, b_gyr_cov, b_acc_cov; +double imu_meas_acc_cov, imu_meas_omg_cov; +int lidar_type, pcd_save_interval; +std::vector gravity_init, gravity; +bool runtime_pos_log, pcd_save_en, path_en, extrinsic_est_en = true; +bool scan_pub_en, scan_body_pub_en; +shared_ptr p_pre; +shared_ptr p_imu; +double time_update_last = 0.0, time_current = 0.0, time_predict_last_const = 0.0, t_last = 0.0; +double time_diff_lidar_to_imu = 0.0; + +double lidar_time_inte = 0.1, first_imu_time = 0.0; +int cut_frame_num = 1, orig_odom_freq = 10; +double online_refine_time = 20.0; //unit: s +bool cut_frame_init = true; + +MeasureGroup Measures; + +ofstream fout_out, fout_imu_pbp, fout_rtk; + +void readParameters(ros::NodeHandle &nh) +{ + p_pre.reset(new Preprocess()); + p_imu.reset(new ImuProcess()); + nh.param("prop_at_freq_of_imu", prop_at_freq_of_imu, 1); + nh.param("use_imu_as_input", use_imu_as_input, 0); + nh.param("check_satu", check_satu, 1); + nh.param("init_map_size", init_map_size, 100); + nh.param("space_down_sample", space_down_sample, 1); + nh.param("mapping/satu_acc",satu_acc,3.0); + nh.param("mapping/satu_gyro",satu_gyro,35.0); + nh.param("mapping/acc_norm",acc_norm,1.0); + nh.param("mapping/plane_thr", plane_thr, 0.05f); + nh.param("point_filter_num", p_pre->point_filter_num, 2); + nh.param("common/lid_topic",lid_topic,"/livox/lidar"); + nh.param("common/imu_topic", imu_topic,"/livox/imu"); + nh.param("common/con_frame",con_frame,false); + nh.param("common/con_frame_num",con_frame_num,1); + nh.param("common/cut_frame",cut_frame,false); + nh.param("common/cut_frame_time_interval",cut_frame_time_interval,0.1); + nh.param("common/time_diff_lidar_to_imu",time_diff_lidar_to_imu,0.0); + nh.param("filter_size_surf",filter_size_surf_min,0.5); + nh.param("filter_size_map",filter_size_map_min,0.5); + // nh.param("cube_side_length",cube_len,2000); + nh.param("mapping/det_range",DET_RANGE,300.f); + nh.param("mapping/fov_degree",fov_deg,180); + nh.param("mapping/imu_en",imu_en,true); + nh.param("mapping/extrinsic_est_en",extrinsic_est_en,true); + nh.param("mapping/imu_time_inte",imu_time_inte,0.005); + nh.param("mapping/lidar_meas_cov",laser_point_cov,0.1); + nh.param("mapping/acc_cov_input",acc_cov_input,0.1); + nh.param("mapping/vel_cov",vel_cov,20); + nh.param("mapping/gyr_cov_input",gyr_cov_input,0.1); + nh.param("mapping/gyr_cov_output",gyr_cov_output,0.1); + nh.param("mapping/acc_cov_output",acc_cov_output,0.1); + nh.param("mapping/b_gyr_cov",b_gyr_cov,0.0001); + nh.param("mapping/b_acc_cov",b_acc_cov,0.0001); + nh.param("mapping/imu_meas_acc_cov",imu_meas_acc_cov,0.1); + nh.param("mapping/imu_meas_omg_cov",imu_meas_omg_cov,0.1); + nh.param("preprocess/blind", p_pre->blind, 1.0); + nh.param("preprocess/lidar_type", lidar_type, 1); + nh.param("preprocess/scan_line", p_pre->N_SCANS, 16); + nh.param("preprocess/scan_rate", p_pre->SCAN_RATE, 10); + nh.param("preprocess/timestamp_unit", p_pre->time_unit, 1); + nh.param("mapping/match_s", match_s, 81); + nh.param>("mapping/gravity", gravity, std::vector()); + nh.param>("mapping/gravity_init", gravity_init, std::vector()); + nh.param>("mapping/extrinsic_T", extrinT, std::vector()); + nh.param>("mapping/extrinsic_R", extrinR, std::vector()); + nh.param("odometry/publish_odometry_without_downsample", publish_odometry_without_downsample, false); + nh.param("publish/path_en",path_en, true); + nh.param("publish/scan_publish_en",scan_pub_en,1); + nh.param("publish/scan_bodyframe_pub_en",scan_body_pub_en,1); + nh.param("runtime_pos_log_enable", runtime_pos_log, 0); + nh.param("pcd_save/pcd_save_en", pcd_save_en, false); + nh.param("pcd_save/interval", pcd_save_interval, -1); + + nh.param("mapping/lidar_time_inte",lidar_time_inte,0.1); + nh.param("mapping/lidar_meas_cov",laser_point_cov,0.1); + + nh.param("mapping/ivox_grid_resolution", ivox_options_.resolution_, 0.2); + nh.param("ivox_nearby_type", ivox_nearby_type, 18); + if (ivox_nearby_type == 0) { + ivox_options_.nearby_type_ = IVoxType::NearbyType::CENTER; + } else if (ivox_nearby_type == 6) { + ivox_options_.nearby_type_ = IVoxType::NearbyType::NEARBY6; + } else if (ivox_nearby_type == 18) { + ivox_options_.nearby_type_ = IVoxType::NearbyType::NEARBY18; + } else if (ivox_nearby_type == 26) { + ivox_options_.nearby_type_ = IVoxType::NearbyType::NEARBY26; + } else { + // LOG(WARNING) << "unknown ivox_nearby_type, use NEARBY18"; + ivox_options_.nearby_type_ = IVoxType::NearbyType::NEARBY18; + } + p_imu->gravity_ << VEC_FROM_ARRAY(gravity); +} + +Eigen::Matrix SO3ToEuler(const SO3 &rot) +{ + double sy = sqrt(rot(0,0)*rot(0,0) + rot(1,0)*rot(1,0)); + bool singular = sy < 1e-6; + double x, y, z; + if(!singular) + { + x = atan2(rot(2, 1), rot(2, 2)); + y = atan2(-rot(2, 0), sy); + z = atan2(rot(1, 0), rot(0, 0)); + } + else + { + x = atan2(-rot(1, 2), rot(1, 1)); + y = atan2(-rot(2, 0), sy); + z = 0; + } + Eigen::Matrix ang(x, y, z); + return ang; +} + +void open_file() +{ + + fout_out.open(DEBUG_FILE_DIR("mat_out.txt"),ios::out); + fout_imu_pbp.open(DEBUG_FILE_DIR("imu_pbp.txt"),ios::out); + if (fout_out && fout_imu_pbp) + cout << "~~~~"< & P_init) +{ + P_init = MD(24, 24)::Identity() * 0.1; + P_init.block<3, 3>(21, 21) = MD(3,3)::Identity() * 0.0001; + P_init.block<6, 6>(15, 15) = MD(6,6)::Identity() * 0.001; +} + +void reset_cov_output(Eigen::Matrix & P_init_output) +{ + P_init_output = MD(30, 30)::Identity() * 0.01; + P_init_output.block<3, 3>(21, 21) = MD(3,3)::Identity() * 0.0001; + // P_init_output.block<6, 6>(6, 6) = MD(6,6)::Identity() * 0.0001; + P_init_output.block<6, 6>(24, 24) = MD(6,6)::Identity() * 0.001; +} \ No newline at end of file diff --git a/src/parameters.h b/src/parameters.h new file mode 100755 index 0000000..e41acfd --- /dev/null +++ b/src/parameters.h @@ -0,0 +1,83 @@ +// #ifndef PARAM_H +// #define PARAM_H +#pragma once +#include +#include +#include +#include +#include "preprocess.h" +#include "IMU_Processing.h" +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +// #define IVOX_NODE_TYPE_PHC + +#ifdef IVOX_NODE_TYPE_PHC + using IVoxType = faster_lio::IVox<3, faster_lio::IVoxNodeType::PHC, PointType>; +#else + using IVoxType = faster_lio::IVox<3, faster_lio::IVoxNodeType::DEFAULT, PointType>; +#endif + +extern bool is_first_frame; +extern double lidar_end_time, first_lidar_time, time_con; +extern double last_timestamp_lidar, last_timestamp_imu; +extern int pcd_index; +extern IVoxType::Options ivox_options_; +extern int ivox_nearby_type; +extern state_input state_in; +extern state_output state_out; +extern std::string lid_topic, imu_topic; +extern bool prop_at_freq_of_imu, check_satu, con_frame, cut_frame; +extern bool use_imu_as_input, space_down_sample; +extern bool extrinsic_est_en, publish_odometry_without_downsample; +extern int init_map_size, con_frame_num; +extern double match_s, satu_acc, satu_gyro, cut_frame_time_interval; +extern float plane_thr; +extern double filter_size_surf_min, filter_size_map_min, fov_deg; +// extern double cube_len; +extern float DET_RANGE; +extern bool imu_en; +extern double imu_time_inte; +extern double laser_point_cov, acc_norm; +extern double acc_cov_input, gyr_cov_input, vel_cov; +extern double gyr_cov_output, acc_cov_output, b_gyr_cov, b_acc_cov; +extern double imu_meas_acc_cov, imu_meas_omg_cov; +extern int lidar_type, pcd_save_interval; +extern std::vector gravity_init, gravity; +extern bool runtime_pos_log, pcd_save_en, path_en; +extern bool scan_pub_en, scan_body_pub_en; +extern shared_ptr p_pre; +extern shared_ptr p_imu; +extern bool is_first_frame; + +extern std::vector extrinT; +extern std::vector extrinR; +extern double time_diff_lidar_to_imu; +extern double lidar_time_inte, first_imu_time; +extern int cut_frame_num, orig_odom_freq; +extern double online_refine_time; //unit: s +extern bool cut_frame_init; +extern double time_update_last, time_current, time_predict_last_const, t_last; + +extern MeasureGroup Measures; + +extern ofstream fout_out, fout_imu_pbp, fout_rtk; +void readParameters(ros::NodeHandle &n); +void open_file(); +Eigen::Matrix SO3ToEuler(const SO3 &orient); +void reset_cov(Eigen::Matrix & P_init); +void reset_cov_output(Eigen::Matrix & P_init_output); \ No newline at end of file diff --git a/src/preprocess.cpp b/src/preprocess.cpp new file mode 100755 index 0000000..f53ecb8 --- /dev/null +++ b/src/preprocess.cpp @@ -0,0 +1,1026 @@ +#include "preprocess.h" + +#define RETURN0 0x00 +#define RETURN0AND1 0x10 + +const bool time_list_cut_frame(PointType &x, PointType &y) { + return (x.curvature < y.curvature); +} + +Preprocess::Preprocess() + :lidar_type(AVIA), blind(0.01), point_filter_num(1) +{ + inf_bound = 10; + N_SCANS = 6; + SCAN_RATE = 10; + group_size = 8; + disA = 0.01; + disA = 0.1; // B? + p2l_ratio = 225; + limit_maxmid =6.25; + limit_midmin =6.25; + limit_maxmin = 3.24; + jump_up_limit = 170.0; + jump_down_limit = 8.0; + cos160 = 160.0; + edgea = 2; + edgeb = 0.1; + smallp_intersect = 172.5; + smallp_ratio = 1.2; + given_offset_time = false; + + jump_up_limit = cos(jump_up_limit/180*M_PI); + jump_down_limit = cos(jump_down_limit/180*M_PI); + cos160 = cos(cos160/180*M_PI); + smallp_intersect = cos(smallp_intersect/180*M_PI); +} + +Preprocess::~Preprocess() {} + +void Preprocess::set(bool feat_en, int lid_type, double bld, int pfilt_num) +{ + lidar_type = lid_type; + blind = bld; + point_filter_num = pfilt_num; +} + +void Preprocess::process(const livox_ros_driver::CustomMsg::ConstPtr &msg, PointCloudXYZI::Ptr &pcl_out) +{ + avia_handler(msg); + *pcl_out = pl_surf; +} + +void Preprocess::process(const sensor_msgs::PointCloud2::ConstPtr &msg, PointCloudXYZI::Ptr &pcl_out) +{ + switch (time_unit) + { + case SEC: + time_unit_scale = 1.e3f; + break; + case MS: + time_unit_scale = 1.f; + break; + case US: + time_unit_scale = 1.e-3f; + break; + case NS: + time_unit_scale = 1.e-6f; + break; + default: + time_unit_scale = 1.f; + break; + } + + switch (lidar_type) + { + case OUST64: + oust64_handler(msg); + break; + + case VELO16: + velodyne_handler(msg); + break; + + case HESAIxt32: + hesai_handler(msg); + break; + + default: + printf("Error LiDAR Type"); + break; + } + *pcl_out = pl_surf; +} + + +void Preprocess::process_cut_frame_livox(const livox_ros_driver::CustomMsg::ConstPtr &msg, + deque &pcl_out, deque &time_lidar, + const int required_frame_num, int scan_count) { + int plsize = msg->point_num; + pl_surf.clear(); + pl_surf.reserve(plsize); + pl_full.clear(); + pl_full.resize(plsize); + int valid_point_num = 0; + + for (uint i = 1; i < plsize; i++) { + if ((msg->points[i].line < N_SCANS) && + ((msg->points[i].tag & 0x30) == 0x10 || (msg->points[i].tag & 0x30) == 0x00)) + { + valid_point_num++; + if (valid_point_num % point_filter_num == 0) { + pl_full[i].x = msg->points[i].x; + pl_full[i].y = msg->points[i].y; + pl_full[i].z = msg->points[i].z; + pl_full[i].intensity = msg->points[i].reflectivity; + //use curvature as time of each laser points，unit: ms + pl_full[i].curvature = msg->points[i].offset_time / float(1000000); + + double dist = pl_full[i].x * pl_full[i].x + pl_full[i].y * pl_full[i].y + pl_full[i].z * pl_full[i].z; + if (dist < blind * blind) continue; + + if ((abs(pl_full[i].x - pl_full[i - 1].x) > 1e-7) + || (abs(pl_full[i].y - pl_full[i - 1].y) > 1e-7) + || (abs(pl_full[i].z - pl_full[i - 1].z) > 1e-7)) { + pl_surf.push_back(pl_full[i]); + } + } + } + } + sort(pl_surf.points.begin(), pl_surf.points.end(), time_list_cut_frame); + //end time of last frame，单位ms + double last_frame_end_time = msg->header.stamp.toSec() * 1000; + uint valid_num = 0; + uint cut_num = 0; + uint valid_pcl_size = pl_surf.points.size(); + + int required_cut_num = required_frame_num; + if (scan_count < 5) + required_cut_num = 1; + + PointCloudXYZI pcl_cut; + for (uint i = 1; i < valid_pcl_size; i++) { + valid_num++; + //Compute new opffset time of each point：ms + pl_surf[i].curvature += msg->header.stamp.toSec() * 1000 - last_frame_end_time; + pcl_cut.push_back(pl_surf[i]); + if (valid_num == (int((cut_num + 1) * valid_pcl_size / required_cut_num) - 1)) { + cut_num++; + time_lidar.push_back(last_frame_end_time); + PointCloudXYZI::Ptr pcl_temp(new PointCloudXYZI()); //Initialize shared_ptr + *pcl_temp = pcl_cut; + pcl_out.push_back(pcl_temp); + //Update frame head + last_frame_end_time += pl_surf[i].curvature; + pcl_cut.clear(); + pcl_cut.reserve(valid_pcl_size * 2 / required_frame_num); + } + } +} +#define MAX_LINE_NUM 128 +void +Preprocess::process_cut_frame_pcl2(const sensor_msgs::PointCloud2::ConstPtr &msg, deque &pcl_out, + deque &time_lidar, const int required_frame_num, int scan_count) { + pl_surf.clear(); + pl_corn.clear(); + pl_full.clear(); + if (lidar_type == VELO16) { + pcl::PointCloud pl_orig; + pcl::fromROSMsg(*msg, pl_orig); + int plsize = pl_orig.points.size(); + pl_surf.reserve(plsize); + + bool is_first[MAX_LINE_NUM]; + double yaw_fp[MAX_LINE_NUM] = {0}; // yaw of first scan point + double omega_l = 3.61; // scan angular velocity (deg/ms) + float yaw_last[MAX_LINE_NUM] = {0.0}; // yaw of last scan point + float time_last[MAX_LINE_NUM] = {0.0}; // last offset time + + if (pl_orig.points[plsize - 1].time > 0) { + given_offset_time = true; + } else { + cout << "Compute offset time using constant rotation model." << endl; + given_offset_time = false; + memset(is_first, true, sizeof(is_first)); + } + + for (int i = 0; i < plsize; i++) { + PointType added_pt; + added_pt.normal_x = 0; + added_pt.normal_y = 0; + added_pt.normal_z = 0; + added_pt.x = pl_orig.points[i].x; + added_pt.y = pl_orig.points[i].y; + added_pt.z = pl_orig.points[i].z; + added_pt.intensity = pl_orig.points[i].intensity; + added_pt.curvature = pl_orig.points[i].time * 1000.0; //ms + + double dist = added_pt.x * added_pt.x + added_pt.y * added_pt.y + added_pt.z * added_pt.z; + if ( dist < blind * blind || isnan(added_pt.x) || isnan(added_pt.y) || isnan(added_pt.z)) + continue; + + if (!given_offset_time) { + int layer = pl_orig.points[i].ring; + double yaw_angle = atan2(added_pt.y, added_pt.x) * 57.2957; + + if (is_first[layer]) { + yaw_fp[layer] = yaw_angle; + is_first[layer] = false; + added_pt.curvature = 0.0; + yaw_last[layer] = yaw_angle; + time_last[layer] = added_pt.curvature; + continue; + } + // compute offset time + if (yaw_angle <= yaw_fp[layer]) { + added_pt.curvature = (yaw_fp[layer] - yaw_angle) / omega_l; + } else { + added_pt.curvature = (yaw_fp[layer] - yaw_angle + 360.0) / omega_l; + } + if (added_pt.curvature < time_last[layer]) added_pt.curvature += 360.0 / omega_l; + + yaw_last[layer] = yaw_angle; + time_last[layer] = added_pt.curvature; + } + + + if (i % point_filter_num == 0 && pl_orig.points[i].ring < N_SCANS) { + pl_surf.points.push_back(added_pt); + } + } + } else if (lidar_type == OUST64) { + pcl::PointCloud pl_orig; + pcl::fromROSMsg(*msg, pl_orig); + int plsize = pl_orig.points.size(); + pl_surf.reserve(plsize); + for (int i = 0; i < plsize; i++) { + PointType added_pt; + added_pt.normal_x = 0; + added_pt.normal_y = 0; + added_pt.normal_z = 0; + added_pt.x = pl_orig.points[i].x; + added_pt.y = pl_orig.points[i].y; + added_pt.z = pl_orig.points[i].z; + added_pt.intensity = pl_orig.points[i].intensity; + added_pt.curvature = pl_orig.points[i].t / 1e6; //ns to ms + + double dist = added_pt.x * added_pt.x + added_pt.y * added_pt.y + added_pt.z * added_pt.z; + if ( dist < blind * blind || isnan(added_pt.x) || isnan(added_pt.y) || isnan(added_pt.z)) + continue; + + if (i % point_filter_num == 0 && pl_orig.points[i].ring < N_SCANS) { + pl_surf.points.push_back(added_pt); + } + } + } else if(lidar_type == HESAIxt32) { + pcl::PointCloud pl_orig; + pcl::fromROSMsg(*msg, pl_orig); + int plsize = pl_orig.points.size(); + pl_surf.reserve(plsize); + for (int i = 0; i < plsize; i++) { + PointType added_pt; + added_pt.normal_x = 0; + added_pt.normal_y = 0; + added_pt.normal_z = 0; + added_pt.x = pl_orig.points[i].x; + added_pt.y = pl_orig.points[i].y; + added_pt.z = pl_orig.points[i].z; + added_pt.intensity = pl_orig.points[i].intensity; + added_pt.curvature = (pl_orig.points[i].timestamp - msg->header.stamp.toSec()) * 1000; //s to ms + + double dist = added_pt.x * added_pt.x + added_pt.y * added_pt.y + added_pt.z * added_pt.z; + if ( dist < blind * blind || isnan(added_pt.x) || isnan(added_pt.y) || isnan(added_pt.z)) + continue; + + if (i % point_filter_num == 0 && pl_orig.points[i].ring < N_SCANS) { + pl_surf.points.push_back(added_pt); + } + } + }else{ + cout << "Wrong LiDAR Type!!!" << endl; + return; + } + + sort(pl_surf.points.begin(), pl_surf.points.end(), time_list_cut_frame); + + //ms + double last_frame_end_time = msg->header.stamp.toSec() * 1000; + uint valid_num = 0; + uint cut_num = 0; + uint valid_pcl_size = pl_surf.points.size(); + + int required_cut_num = required_frame_num; + + if (scan_count < 20) + required_cut_num = 1; + + + PointCloudXYZI pcl_cut; + for (uint i = 1; i < valid_pcl_size; i++) { + valid_num++; + pl_surf[i].curvature += msg->header.stamp.toSec() * 1000 - last_frame_end_time; + pcl_cut.push_back(pl_surf[i]); + + if (valid_num == (int((cut_num + 1) * valid_pcl_size / required_cut_num) - 1)) { + cut_num++; + time_lidar.push_back(last_frame_end_time); + PointCloudXYZI::Ptr pcl_temp(new PointCloudXYZI()); + *pcl_temp = pcl_cut; + pcl_out.push_back(pcl_temp); + last_frame_end_time += pl_surf[i].curvature; + pcl_cut.clear(); + pcl_cut.reserve(valid_pcl_size * 2 / required_frame_num); + } + } +} + +void Preprocess::avia_handler(const livox_ros_driver::CustomMsg::ConstPtr &msg) +{ + pl_surf.clear(); + pl_corn.clear(); + pl_full.clear(); + double t1 = omp_get_wtime(); + int plsize = msg->point_num; + + pl_corn.reserve(plsize); + pl_surf.reserve(plsize); + pl_full.resize(plsize); + + for(int i=0; ipoints[i].line < N_SCANS) && ((msg->points[i].tag & 0x30) == 0x10 || (msg->points[i].tag & 0x30) == 0x00)) + { + valid_num ++; + if (valid_num % point_filter_num == 0) + { + pl_full[i].x = msg->points[i].x; + pl_full[i].y = msg->points[i].y; + pl_full[i].z = msg->points[i].z; + pl_full[i].intensity = msg->points[i].reflectivity; + pl_full[i].curvature = msg->points[i].offset_time / float(1000000); // use curvature as time of each laser points, curvature unit: ms + + if(((abs(pl_full[i].x - pl_full[i-1].x) > 1e-7) + || (abs(pl_full[i].y - pl_full[i-1].y) > 1e-7) + || (abs(pl_full[i].z - pl_full[i-1].z) > 1e-7)) + && (pl_full[i].x * pl_full[i].x + pl_full[i].y * pl_full[i].y + pl_full[i].z * pl_full[i].z > (blind * blind))) + { + pl_surf.push_back(pl_full[i]); + } + } + } + } + +} + +void Preprocess::oust64_handler(const sensor_msgs::PointCloud2::ConstPtr &msg) +{ + pl_surf.clear(); + pl_corn.clear(); + pl_full.clear(); + pcl::PointCloud pl_orig; + pcl::fromROSMsg(*msg, pl_orig); + int plsize = pl_orig.size(); + pl_corn.reserve(plsize); + pl_surf.reserve(plsize); + + + double time_stamp = msg->header.stamp.toSec(); + // cout << "===================================" << endl; + // printf("Pt size = %d, N_SCANS = %d\r\n", plsize, N_SCANS); + for (int i = 0; i < pl_orig.points.size(); i++) + { + if (i % point_filter_num != 0) continue; + + double range = pl_orig.points[i].x * pl_orig.points[i].x + pl_orig.points[i].y * pl_orig.points[i].y + pl_orig.points[i].z * pl_orig.points[i].z; + + if (range < (blind * blind)) continue; + + Eigen::Vector3d pt_vec; + PointType added_pt; + added_pt.x = pl_orig.points[i].x; + added_pt.y = pl_orig.points[i].y; + added_pt.z = pl_orig.points[i].z; + added_pt.intensity = pl_orig.points[i].intensity; + added_pt.normal_x = 0; + added_pt.normal_y = 0; + added_pt.normal_z = 0; + added_pt.curvature = pl_orig.points[i].t * time_unit_scale; // curvature unit: ms + + pl_surf.points.push_back(added_pt); + } + + // pub_func(pl_surf, pub_full, msg->header.stamp); + // pub_func(pl_surf, pub_corn, msg->header.stamp); +} + +void Preprocess::velodyne_handler(const sensor_msgs::PointCloud2::ConstPtr &msg) +{ + pl_surf.clear(); + pl_corn.clear(); + pl_full.clear(); + + pcl::PointCloud pl_orig; + pcl::fromROSMsg(*msg, pl_orig); + int plsize = pl_orig.points.size(); + if (plsize == 0) return; + pl_surf.reserve(plsize); + + /*** These variables only works when no point timestamps given ***/ + double omega_l = 0.361 * SCAN_RATE; // scan angular velocity + std::vector is_first(N_SCANS,true); + std::vector yaw_fp(N_SCANS, 0.0); // yaw of first scan point + std::vector yaw_last(N_SCANS, 0.0); // yaw of last scan point + std::vector time_last(N_SCANS, 0.0); // last offset time + /*****************************************************************/ + + if (pl_orig.points[plsize - 1].time > 0) + { + given_offset_time = true; + } + else + { + given_offset_time = false; + double yaw_first = atan2(pl_orig.points[0].y, pl_orig.points[0].x) * 57.29578; + double yaw_end = yaw_first; + int layer_first = pl_orig.points[0].ring; + for (uint i = plsize - 1; i > 0; i--) + { + if (pl_orig.points[i].ring == layer_first) + { + yaw_end = atan2(pl_orig.points[i].y, pl_orig.points[i].x) * 57.29578; + break; + } + } + } + + for (int i = 0; i < plsize; i++) + { + PointType added_pt; + // cout<<"!!!!!!"< (blind * blind)) + { + pl_surf.points.push_back(added_pt); + } + } + } + +} + +void Preprocess::hesai_handler(const sensor_msgs::PointCloud2::ConstPtr &msg) +{ + pl_surf.clear(); + pl_corn.clear(); + pl_full.clear(); + + pcl::PointCloud pl_orig; + pcl::fromROSMsg(*msg, pl_orig); + int plsize = pl_orig.points.size(); + if (plsize == 0) return; + pl_surf.reserve(plsize); + + /*** These variables only works when no point timestamps given ***/ + double omega_l = 0.361 * SCAN_RATE; // scan angular velocity + std::vector is_first(N_SCANS,true); + std::vector yaw_fp(N_SCANS, 0.0); // yaw of first scan point + std::vector yaw_last(N_SCANS, 0.0); // yaw of last scan point + std::vector time_last(N_SCANS, 0.0); // last offset time + /*****************************************************************/ + + if (pl_orig.points[plsize - 1].timestamp > 0) + { + given_offset_time = true; + } + else + { + given_offset_time = false; + double yaw_first = atan2(pl_orig.points[0].y, pl_orig.points[0].x) * 57.29578; + double yaw_end = yaw_first; + int layer_first = pl_orig.points[0].ring; + for (uint i = plsize - 1; i > 0; i--) + { + if (pl_orig.points[i].ring == layer_first) + { + yaw_end = atan2(pl_orig.points[i].y, pl_orig.points[i].x) * 57.29578; + break; + } + } + } + + double time_head = pl_orig.points[0].timestamp; + + for (int i = 0; i < plsize; i++) + { + PointType added_pt; + // cout<<"!!!!!!"< (blind * blind)) + { + pl_surf.points.push_back(added_pt); + } + } + } + +} + +void Preprocess::give_feature(pcl::PointCloud &pl, vector &types) +{ + int plsize = pl.size(); + int plsize2; + if(plsize == 0) + { + printf("something wrong\n"); + return; + } + uint head = 0; + + while(types[head].range < blind) + { + head++; + } + + // Surf + plsize2 = (plsize > group_size) ? (plsize - group_size) : 0; + + Eigen::Vector3d curr_direct(Eigen::Vector3d::Zero()); + Eigen::Vector3d last_direct(Eigen::Vector3d::Zero()); + + uint i_nex = 0, i2; + uint last_i = 0; uint last_i_nex = 0; + int last_state = 0; + int plane_type; + + for(uint i=head; i0.5) + if(last_state==1 && last_direct.norm()>0.1) + { + double mod = last_direct.transpose() * curr_direct; + if(mod>-0.707 && mod<0.707) + { + types[i].ftype = Edge_Plane; + } + else + { + types[i].ftype = Real_Plane; + } + } + + i = i_nex - 1; + last_state = 1; + } + else // if(plane_type == 2) + { + i = i_nex; + last_state = 0; + } + + last_i = i2; + last_i_nex = i_nex; + last_direct = curr_direct; + } + + plsize2 = plsize > 3 ? plsize - 3 : 0; + for(uint i=head+3; i=Real_Plane) + { + continue; + } + + if(types[i-1].dista<1e-16 || types[i].dista<1e-16) + { + continue; + } + + Eigen::Vector3d vec_a(pl[i].x, pl[i].y, pl[i].z); + Eigen::Vector3d vecs[2]; + + for(int j=0; j<2; j++) + { + int m = -1; + if(j == 1) + { + m = 1; + } + + if(types[i+m].range < blind) + { + if(types[i].range > inf_bound) + { + types[i].edj[j] = Nr_inf; + } + else + { + types[i].edj[j] = Nr_blind; + } + continue; + } + + vecs[j] = Eigen::Vector3d(pl[i+m].x, pl[i+m].y, pl[i+m].z); + vecs[j] = vecs[j] - vec_a; + + types[i].angle[j] = vec_a.dot(vecs[j]) / vec_a.norm() / vecs[j].norm(); + if(types[i].angle[j] < jump_up_limit) + { + types[i].edj[j] = Nr_180; + } + else if(types[i].angle[j] > jump_down_limit) + { + types[i].edj[j] = Nr_zero; + } + } + + types[i].intersect = vecs[Prev].dot(vecs[Next]) / vecs[Prev].norm() / vecs[Next].norm(); + if(types[i].edj[Prev]==Nr_nor && types[i].edj[Next]==Nr_zero && types[i].dista>0.0225 && types[i].dista>4*types[i-1].dista) + { + if(types[i].intersect > cos160) + { + if(edge_jump_judge(pl, types, i, Prev)) + { + types[i].ftype = Edge_Jump; + } + } + } + else if(types[i].edj[Prev]==Nr_zero && types[i].edj[Next]== Nr_nor && types[i-1].dista>0.0225 && types[i-1].dista>4*types[i].dista) + { + if(types[i].intersect > cos160) + { + if(edge_jump_judge(pl, types, i, Next)) + { + types[i].ftype = Edge_Jump; + } + } + } + else if(types[i].edj[Prev]==Nr_nor && types[i].edj[Next]==Nr_inf) + { + if(edge_jump_judge(pl, types, i, Prev)) + { + types[i].ftype = Edge_Jump; + } + } + else if(types[i].edj[Prev]==Nr_inf && types[i].edj[Next]==Nr_nor) + { + if(edge_jump_judge(pl, types, i, Next)) + { + types[i].ftype = Edge_Jump; + } + + } + else if(types[i].edj[Prev]>Nr_nor && types[i].edj[Next]>Nr_nor) + { + if(types[i].ftype == Nor) + { + types[i].ftype = Wire; + } + } + } + + plsize2 = plsize-1; + double ratio; + for(uint i=head+1; i types[i].dista) + { + ratio = types[i-1].dista / types[i].dista; + } + else + { + ratio = types[i].dista / types[i-1].dista; + } + + if(types[i].intersect &types, uint i_cur, uint &i_nex, Eigen::Vector3d &curr_direct) +{ + double group_dis = disA*types[i_cur].range + disB; + group_dis = group_dis * group_dis; + // i_nex = i_cur; + + double two_dis; + vector disarr; + disarr.reserve(20); + + for(i_nex=i_cur; i_nex= pl.size()) || (i_nex >= pl.size())) break; + + if(types[i_nex].range < blind) + { + curr_direct.setZero(); + return 2; + } + vx = pl[i_nex].x - pl[i_cur].x; + vy = pl[i_nex].y - pl[i_cur].y; + vz = pl[i_nex].z - pl[i_cur].z; + two_dis = vx*vx + vy*vy + vz*vz; + if(two_dis >= group_dis) + { + break; + } + disarr.push_back(types[i_nex].dista); + i_nex++; + } + + double leng_wid = 0; + double v1[3], v2[3]; + for(uint j=i_cur+1; j= pl.size()) || (i_cur >= pl.size())) break; + v1[0] = pl[j].x - pl[i_cur].x; + v1[1] = pl[j].y - pl[i_cur].y; + v1[2] = pl[j].z - pl[i_cur].z; + + v2[0] = v1[1]*vz - vy*v1[2]; + v2[1] = v1[2]*vx - v1[0]*vz; + v2[2] = v1[0]*vy - vx*v1[1]; + + double lw = v2[0]*v2[0] + v2[1]*v2[1] + v2[2]*v2[2]; + if(lw > leng_wid) + { + leng_wid = lw; + } + } + + + if((two_dis*two_dis/leng_wid) < p2l_ratio) + { + curr_direct.setZero(); + return 0; + } + + uint disarrsize = disarr.size(); + for(uint j=0; j=limit_maxmid || dismid_min>=limit_midmin) + { + curr_direct.setZero(); + return 0; + } + } + else + { + double dismax_min = disarr[0] / disarr[disarrsize-2]; + if(dismax_min >= limit_maxmin) + { + curr_direct.setZero(); + return 0; + } + } + + curr_direct << vx, vy, vz; + curr_direct.normalize(); + return 1; +} + +bool Preprocess::edge_jump_judge(const PointCloudXYZI &pl, vector &types, uint i, Surround nor_dir) +{ + if(nor_dir == 0) + { + if(types[i-1].rangeedgea*d2 || (d1-d2)>edgeb) + { + return false; + } + + return true; +} diff --git a/src/preprocess.h b/src/preprocess.h new file mode 100755 index 0000000..ba1630b --- /dev/null +++ b/src/preprocess.h @@ -0,0 +1,149 @@ +#include +#include +#include +#include + +using namespace std; + +#define IS_VALID(a) ((abs(a)>1e8) ? true : false) + +typedef pcl::PointXYZINormal PointType; +typedef pcl::PointCloud PointCloudXYZI; + +enum LID_TYPE{AVIA = 1, VELO16, OUST64, HESAIxt32}; //{1, 2, 3, 4} +enum TIME_UNIT{SEC = 0, MS = 1, US = 2, NS = 3}; +enum Feature{Nor, Poss_Plane, Real_Plane, Edge_Jump, Edge_Plane, Wire, ZeroPoint}; +enum Surround{Prev, Next}; +enum E_jump{Nr_nor, Nr_zero, Nr_180, Nr_inf, Nr_blind}; + +const bool time_list_cut_frame(PointType &x, PointType &y); + +struct orgtype +{ + double range; + double dista; + double angle[2]; + double intersect; + E_jump edj[2]; + Feature ftype; + orgtype() + { + range = 0; + edj[Prev] = Nr_nor; + edj[Next] = Nr_nor; + ftype = Nor; + intersect = 2; + } +}; + +namespace velodyne_ros { + struct EIGEN_ALIGN16 Point { + PCL_ADD_POINT4D; + float intensity; + float time; + uint16_t ring; + EIGEN_MAKE_ALIGNED_OPERATOR_NEW + }; +} // namespace velodyne_ros +POINT_CLOUD_REGISTER_POINT_STRUCT(velodyne_ros::Point, + (float, x, x) + (float, y, y) + (float, z, z) + (float, intensity, intensity) + (float, time, time) + (std::uint16_t, ring, ring) +) + +namespace hesai_ros { + struct EIGEN_ALIGN16 Point { + PCL_ADD_POINT4D; + float intensity; + double timestamp; + uint16_t ring; + EIGEN_MAKE_ALIGNED_OPERATOR_NEW + }; +} // namespace velodyne_ros +POINT_CLOUD_REGISTER_POINT_STRUCT(hesai_ros::Point, + (float, x, x) + (float, y, y) + (float, z, z) + (float, intensity, intensity) + (double, timestamp, timestamp) + (std::uint16_t, ring, ring) +) + +namespace ouster_ros { + struct EIGEN_ALIGN16 Point { + PCL_ADD_POINT4D; + float intensity; + uint32_t t; + uint16_t reflectivity; + uint8_t ring; + uint16_t ambient; + uint32_t range; + EIGEN_MAKE_ALIGNED_OPERATOR_NEW + }; +} // namespace ouster_ros + +// clang-format off +POINT_CLOUD_REGISTER_POINT_STRUCT(ouster_ros::Point, + (float, x, x) + (float, y, y) + (float, z, z) + (float, intensity, intensity) + // use std::uint32_t to avoid conflicting with pcl::uint32_t + (std::uint32_t, t, t) + (std::uint16_t, reflectivity, reflectivity) + (std::uint8_t, ring, ring) + (std::uint16_t, ambient, ambient) + (std::uint32_t, range, range) +) + +class Preprocess +{ + public: +// EIGEN_MAKE_ALIGNED_OPERATOR_NEW + + Preprocess(); + ~Preprocess(); + + void process_cut_frame_livox(const livox_ros_driver::CustomMsg::ConstPtr &msg, deque &pcl_out, deque &time_lidar, const int required_frame_num, int scan_count); + + void process_cut_frame_pcl2(const sensor_msgs::PointCloud2::ConstPtr &msg, deque &pcl_out, deque &time_lidar, const int required_frame_num, int scan_count); + + void process(const livox_ros_driver::CustomMsg::ConstPtr &msg, PointCloudXYZI::Ptr &pcl_out); + void process(const sensor_msgs::PointCloud2::ConstPtr &msg, PointCloudXYZI::Ptr &pcl_out); + void set(bool feat_en, int lid_type, double bld, int pfilt_num); + + // sensor_msgs::PointCloud2::ConstPtr pointcloud; + PointCloudXYZI pl_full, pl_corn, pl_surf; + PointCloudXYZI pl_buff[128]; //maximum 128 line lidar + vector typess[128]; //maximum 128 line lidar + float time_unit_scale; + int lidar_type, point_filter_num, N_SCANS, SCAN_RATE, time_unit; + double blind; + bool given_offset_time; + ros::Publisher pub_full, pub_surf, pub_corn; + + + private: + void avia_handler(const livox_ros_driver::CustomMsg::ConstPtr &msg); + void oust64_handler(const sensor_msgs::PointCloud2::ConstPtr &msg); + void velodyne_handler(const sensor_msgs::PointCloud2::ConstPtr &msg); + void hesai_handler(const sensor_msgs::PointCloud2::ConstPtr &msg); + void give_feature(PointCloudXYZI &pl, vector &types); + void pub_func(PointCloudXYZI &pl, const ros::Time &ct); + int plane_judge(const PointCloudXYZI &pl, vector &types, uint i, uint &i_nex, Eigen::Vector3d &curr_direct); + bool small_plane(const PointCloudXYZI &pl, vector &types, uint i_cur, uint &i_nex, Eigen::Vector3d &curr_direct); + bool edge_jump_judge(const PointCloudXYZI &pl, vector &types, uint i, Surround nor_dir); + + int group_size; + double disA, disB, inf_bound; + double limit_maxmid, limit_midmin, limit_maxmin; + double p2l_ratio; + double jump_up_limit, jump_down_limit; + double cos160; + double edgea, edgeb; + double smallp_intersect, smallp_ratio; + double vx, vy, vz; +};