From dfc593fef095de15cf0ec62692c4940003dbc403 Mon Sep 17 00:00:00 2001 From: wangjk Date: Tue, 12 Apr 2022 21:56:25 +0800 Subject: [PATCH] first --- .gitignore | 3 + CMakeLists.txt | 166 +++ LICENSE | 25 + README.md | 53 + README_used in hdl_graph_slam.md | 225 ++++ apps/floor_detection_nodelet.cpp | 269 +++++ apps/hdl_graph_slam_nodelet.cpp | 1056 +++++++++++++++++ apps/prefiltering_nodelet.cpp | 270 +++++ apps/scan_matching_odometry_nodelet.cpp | 430 +++++++ apps/scan_matching_odometry_nodelet_raw.cpp | 399 +++++++ cmake/FindG2O.cmake | 117 ++ docker/build.sh | 2 + docker/howtouse.md | 40 + docker/kinetic/Dockerfile | 25 + docker/kinetic_llvm/Dockerfile | 30 + docker/melodic/Dockerfile | 26 + docker/melodic_llvm/Dockerfile | 28 + docker/noetic/Dockerfile | 26 + docker/noetic_llvm/Dockerfile | 28 + docker/run.sh | 6 + imgs/birds.png | Bin 0 -> 866270 bytes imgs/ford1.png | Bin 0 -> 259098 bytes imgs/ford2.png | Bin 0 -> 483112 bytes imgs/ford3.png | Bin 0 -> 193857 bytes 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include/hdl_graph_slam/loop_detector.hpp | 189 +++ .../hdl_graph_slam/map_cloud_generator.hpp | 34 + .../hdl_graph_slam/nmea_sentence_parser.hpp | 108 ++ include/hdl_graph_slam/registrations.hpp | 21 + include/hdl_graph_slam/ros_time_hash.hpp | 24 + include/hdl_graph_slam/ros_utils.hpp | 93 ++ include/hdl_graph_slam/utility.h | 174 +++ launch/hdl_graph_slam.launch | 156 +++ launch/hdl_graph_slam_400.launch | 156 +++ launch/hdl_graph_slam_400_ours.launch | 165 +++ launch/hdl_graph_slam_501.launch | 156 +++ launch/hdl_graph_slam_imu.launch | 160 +++ launch/hdl_graph_slam_kitti.launch | 147 +++ launch/hdl_graph_slam_kitti_ours.launch | 156 +++ launch/hdl_graph_slam_mulran_ours.launch | 156 +++ launch/hdl_graph_slam_nclt_ours.launch | 156 +++ launch/hdl_graph_slam_stevens_ours.launch | 165 +++ launch/msf_config.yaml | 39 + msg/FloorCoeffs.msg | 3 + msg/ScanMatchingStatus.msg | 9 + nodelet_plugins.xml | 15 + package.xml | 44 + setup.py | 12 + src/g2o/robust_kernel_io.cpp | 156 +++ src/hdl_graph_slam/__init__.py | 0 src/hdl_graph_slam/bag_player.py | 223 ++++ src/hdl_graph_slam/featureAssociation.cpp | 693 +++++++++++ src/hdl_graph_slam/ford2bag.py | 96 ++ src/hdl_graph_slam/graph_slam.cpp | 351 ++++++ src/hdl_graph_slam/imageProjection.cpp | 385 ++++++ .../information_matrix_calculator.cpp | 82 ++ src/hdl_graph_slam/keyframe.cpp | 161 +++ src/hdl_graph_slam/map2odom_publisher.py | 41 + src/hdl_graph_slam/map_cloud_generator.cpp | 55 + src/hdl_graph_slam/registrations.cpp | 129 ++ srv/DumpGraph.srv | 4 + srv/SaveMap.srv | 5 + 83 files changed, 9407 insertions(+) create mode 100644 .gitignore create mode 100644 CMakeLists.txt create mode 100644 LICENSE create mode 100644 README.md create mode 100644 README_used in hdl_graph_slam.md create mode 100644 apps/floor_detection_nodelet.cpp create mode 100644 apps/hdl_graph_slam_nodelet.cpp create mode 100644 apps/prefiltering_nodelet.cpp create mode 100644 apps/scan_matching_odometry_nodelet.cpp create mode 100644 apps/scan_matching_odometry_nodelet_raw.cpp create mode 100644 cmake/FindG2O.cmake create mode 100755 docker/build.sh create mode 100644 docker/howtouse.md create mode 100644 docker/kinetic/Dockerfile create mode 100644 docker/kinetic_llvm/Dockerfile create mode 100644 docker/melodic/Dockerfile create mode 100644 docker/melodic_llvm/Dockerfile create mode 100644 docker/noetic/Dockerfile create mode 100644 docker/noetic_llvm/Dockerfile create mode 100755 docker/run.sh create mode 100644 imgs/birds.png create mode 100644 imgs/ford1.png create mode 100644 imgs/ford2.png create mode 100644 imgs/ford3.png create mode 100644 imgs/hdl_400_graph.png create mode 100644 imgs/hdl_400_points.png create mode 100644 imgs/hdl_graph_slam.png create mode 100644 imgs/nodelets.png create mode 100644 imgs/nodelets.vsdx create mode 100644 imgs/packages.png create mode 100644 imgs/top.png create mode 100644 include/g2o/edge_plane_identity.hpp create mode 100644 include/g2o/edge_plane_parallel.hpp create mode 100644 include/g2o/edge_plane_prior.hpp create mode 100644 include/g2o/edge_se3_plane.hpp create mode 100644 include/g2o/edge_se3_priorquat.hpp create mode 100644 include/g2o/edge_se3_priorvec.hpp create mode 100644 include/g2o/edge_se3_priorxy.hpp create mode 100644 include/g2o/edge_se3_priorxyz.hpp create mode 100644 include/g2o/robust_kernel_io.hpp create mode 100644 include/hdl_graph_slam/featureAssociation.h create mode 100644 include/hdl_graph_slam/graph_slam.hpp create mode 100644 include/hdl_graph_slam/imageProjection.h create mode 100644 include/hdl_graph_slam/information_matrix_calculator.hpp create mode 100644 include/hdl_graph_slam/keyframe.hpp create mode 100644 include/hdl_graph_slam/keyframe_updater.hpp create mode 100644 include/hdl_graph_slam/loop_detector.hpp create mode 100644 include/hdl_graph_slam/map_cloud_generator.hpp create mode 100644 include/hdl_graph_slam/nmea_sentence_parser.hpp create mode 100644 include/hdl_graph_slam/registrations.hpp create mode 100644 include/hdl_graph_slam/ros_time_hash.hpp create mode 100644 include/hdl_graph_slam/ros_utils.hpp create mode 100644 include/hdl_graph_slam/utility.h create mode 100644 launch/hdl_graph_slam.launch create mode 100644 launch/hdl_graph_slam_400.launch create mode 100644 launch/hdl_graph_slam_400_ours.launch create mode 100644 launch/hdl_graph_slam_501.launch create mode 100644 launch/hdl_graph_slam_imu.launch create mode 100644 launch/hdl_graph_slam_kitti.launch create mode 100644 launch/hdl_graph_slam_kitti_ours.launch create mode 100644 launch/hdl_graph_slam_mulran_ours.launch create mode 100644 launch/hdl_graph_slam_nclt_ours.launch create mode 100644 launch/hdl_graph_slam_stevens_ours.launch create mode 100644 launch/msf_config.yaml create mode 100644 msg/FloorCoeffs.msg create mode 100644 msg/ScanMatchingStatus.msg create mode 100644 nodelet_plugins.xml create mode 100644 package.xml create mode 100755 setup.py create mode 100644 src/g2o/robust_kernel_io.cpp create mode 100644 src/hdl_graph_slam/__init__.py create mode 100755 src/hdl_graph_slam/bag_player.py create mode 100644 src/hdl_graph_slam/featureAssociation.cpp create mode 100755 src/hdl_graph_slam/ford2bag.py create mode 100644 src/hdl_graph_slam/graph_slam.cpp create mode 100644 src/hdl_graph_slam/imageProjection.cpp create mode 100644 src/hdl_graph_slam/information_matrix_calculator.cpp create mode 100644 src/hdl_graph_slam/keyframe.cpp create mode 100755 src/hdl_graph_slam/map2odom_publisher.py create mode 100644 src/hdl_graph_slam/map_cloud_generator.cpp create mode 100644 src/hdl_graph_slam/registrations.cpp create mode 100644 srv/DumpGraph.srv create mode 100644 srv/SaveMap.srv diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..0238ce8 --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +.vscode/* +imgui.ini +rviz/* diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100644 index 0000000..a4b66c0 --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,166 @@ +# SPDX-License-Identifier: BSD-2-Clause +cmake_minimum_required(VERSION 2.8.3) +project(hdl_graph_slam) + +# Can we use C++17 in indigo? +add_definitions(-msse -msse2 -msse3 -msse4 -msse4.1 -msse4.2) +set(CMAKE_CXX_FLAGS "-msse -msse2 -msse3 -msse4 -msse4.1 -msse4.2") +set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_LIST_DIR}/cmake") + +# pcl 1.7 causes a segfault when it is built with debug mode +set(CMAKE_BUILD_TYPE "RELEASE") + +find_package(catkin REQUIRED COMPONENTS + roscpp + rospy + pcl_ros + geodesy + nmea_msgs + sensor_msgs + geometry_msgs + message_generation + interactive_markers + ndt_omp + fast_gicp +) +catkin_python_setup() + +find_package(PCL REQUIRED) +include_directories(${PCL_INCLUDE_DIRS} ${OpenCV_INCLUDE_DIRS}) +link_directories(${PCL_LIBRARY_DIRS} ${OpenCV_LIBRARY_DIRS}) +add_definitions(${PCL_DEFINITIONS}) +find_package(OpenCV 3 REQUIRED) + +message(STATUS "PCL_INCLUDE_DIRS:" ${PCL_INCLUDE_DIRS}) +message(STATUS "PCL_LIBRARY_DIRS:" ${PCL_LIBRARY_DIRS}) +message(STATUS "PCL_DEFINITIONS:" ${PCL_DEFINITIONS}) + +find_package(G2O REQUIRED) +include_directories(SYSTEM ${G2O_INCLUDE_DIR} ${G2O_INCLUDE_DIRS}) +link_directories(${G2O_LIBRARY_DIRS}) +# link_libraries(${G2O_LIBRARIES}) + +find_package(OpenMP) +if (OPENMP_FOUND) + set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") + set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") +endif() + +find_library(VGICP_CUDA_FOUND NAMES fast_vgicp_cuda) +message(STATUS "VGICP_CUDA_FOUND:" ${VGICP_CUDA_FOUND}) +if(VGICP_CUDA_FOUND) + add_definitions(-DUSE_VGICP_CUDA) +endif() + +######################## +## message generation ## +######################## +add_message_files(FILES + FloorCoeffs.msg + ScanMatchingStatus.msg +) + +add_service_files(FILES + SaveMap.srv + DumpGraph.srv +) + +generate_messages(DEPENDENCIES std_msgs geometry_msgs) + +################################### +## catkin specific configuration ## +################################### +catkin_package( + INCLUDE_DIRS include + LIBRARIES hdl_graph_slam_nodelet +# CATKIN_DEPENDS pcl_ros roscpp sensor_msgs +# DEPENDS system_lib +) + +########### +## Build ## +########### +include_directories(include) +include_directories( + ${PCL_INCLUDE_DIRS} + ${catkin_INCLUDE_DIRS} + ${OpenCV_INCLUDE_DIRS} +) + +# nodelets +add_library(prefiltering_nodelet apps/prefiltering_nodelet.cpp) +target_link_libraries(prefiltering_nodelet + ${catkin_LIBRARIES} + ${PCL_LIBRARIES} + ${OpenCV_LIBRARIES} +) + + +add_library(floor_detection_nodelet apps/floor_detection_nodelet.cpp) +target_link_libraries(floor_detection_nodelet + ${catkin_LIBRARIES} + ${PCL_LIBRARIES} + ${OpenCV_LIBRARIES} +) +add_dependencies(floor_detection_nodelet ${PROJECT_NAME}_gencpp) + + +add_library(scan_matching_odometry_nodelet + apps/scan_matching_odometry_nodelet.cpp + src/hdl_graph_slam/registrations.cpp + src/hdl_graph_slam/imageProjection.cpp + src/hdl_graph_slam/featureAssociation.cpp +) +target_link_libraries(scan_matching_odometry_nodelet + ${catkin_LIBRARIES} + ${PCL_LIBRARIES} + ${OpenCV_LIBRARIES} +) +add_dependencies(scan_matching_odometry_nodelet ${PROJECT_NAME}_gencpp) + + +add_library(hdl_graph_slam_nodelet + apps/hdl_graph_slam_nodelet.cpp + src/hdl_graph_slam/graph_slam.cpp + src/hdl_graph_slam/keyframe.cpp + src/hdl_graph_slam/map_cloud_generator.cpp + src/hdl_graph_slam/registrations.cpp + src/hdl_graph_slam/information_matrix_calculator.cpp + src/g2o/robust_kernel_io.cpp +) +target_link_libraries(hdl_graph_slam_nodelet + ${catkin_LIBRARIES} + ${PCL_LIBRARIES} + ${G2O_TYPES_DATA} + ${G2O_CORE_LIBRARY} + ${G2O_STUFF_LIBRARY} + ${G2O_SOLVER_PCG} + ${G2O_SOLVER_CSPARSE} # be aware of that CSPARSE is released under LGPL + ${G2O_SOLVER_CHOLMOD} # be aware of that cholmod is released under GPL + ${G2O_TYPES_SLAM3D} + ${G2O_TYPES_SLAM3D_ADDONS} +) +add_dependencies(hdl_graph_slam_nodelet ${PROJECT_NAME}_gencpp) + +catkin_install_python( + PROGRAMS + src/${PROJECT_NAME}/bag_player.py + src/${PROJECT_NAME}/ford2bag.py + src/${PROJECT_NAME}/map2odom_publisher.py + DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} +) + +install(FILES nodelet_plugins.xml + DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION} +) + +install(TARGETS + prefiltering_nodelet + floor_detection_nodelet + scan_matching_odometry_nodelet + hdl_graph_slam_nodelet + LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}) + +install(DIRECTORY include/ + DESTINATION ${CATKIN_GLOBAL_INCLUDE_DESTINATION} +) diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..612dcee --- /dev/null +++ b/LICENSE @@ -0,0 +1,25 @@ +BSD 2-Clause License + +Copyright (c) 2019, k.koide +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. + +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/README.md b/README.md new file mode 100644 index 0000000..386d3f8 --- /dev/null +++ b/README.md @@ -0,0 +1,53 @@ +#FD-SLAM +This is an open source ROS package for real-time 6DOF SLAM using a 3D LIDAR. + +It is based on hdl_graph_slam and the steps to run our system are same with hdl-graph-slam. + +##The names and functions do not change compared with hdl-graph-slam. + +We use a novel feature-based Lidar odometry for fast scan-matching, and use a modified GICP for keyframe matching. The backend in hdl-graph-slam is reused. We have tested this package with Velodyne (HDL32e, HDL64,VLP16) and Ouster64 sensors in indoor and outdoor environments. The corresponding configure launch files are provided. + +##key modifications +src/hdl_graph_slam/imageProjection.cpp and src/hdl_graph_slam/featureAssociation.cpp are added for fast scan matching + +As for the fast-gicp, key modifications are taken place in fast_gicp_impl.cpp----calculate_covariances() + + +## Nodelets +***FD-slam*** consists of four nodelets. + +- *prefiltering_nodelet* +- *scan_matching_odometry_nodelet* +- *floor_detection_nodelet* +- *hdl_graph_slam_nodelet* + +The input point cloud is first downsampled by *prefiltering_nodelet*, and then passed to the next nodelets. While *scan_matching_odometry_nodelet* estimates the sensor pose by iteratively applying a scan matching between consecutive frames (i.e., odometry estimation), *floor_detection_nodelet* detects floor planes by RANSAC. The estimated odometry and the detected floor planes are sent to *hdl_graph_slam*. To compensate the accumulated error of the scan matching, it performs loop detection and optimizes a pose graph which takes various constraints into account. + + + +## Parameters +All the configurable parameters are listed in *launch/****.launch* as ros params. + +## Services +- */hdl_graph_slam/dump* (hdl_graph_slam/DumpGraph) + - save all the internal data (point clouds, floor coeffs, odoms, and pose graph) to a directory. +- */hdl_graph_slam/save_map* (hdl_graph_slam/SaveMap) + - save the generated map as a PCD file. + +## Requirements +***hdl_graph_slam*** requires the following libraries: + +- OpenMP +- PCL +- g2o +- suitesparse + +The following ROS packages are required: + +- geodesy +- nmea_msgs +- pcl_ros +- [ndt_omp](https://github.com/koide3/ndt_omp) +- [fast_gicp](https://github.com/SMRT-AIST/fast_gicp) + + diff --git a/README_used in hdl_graph_slam.md b/README_used in hdl_graph_slam.md new file mode 100644 index 0000000..0c33b95 --- /dev/null +++ b/README_used in hdl_graph_slam.md @@ -0,0 +1,225 @@ +# hdl_graph_slam +***hdl_graph_slam*** is an open source ROS package for real-time 6DOF SLAM using a 3D LIDAR. It is based on 3D Graph SLAM with NDT scan matching-based odometry estimation and loop detection. It also supports several graph constraints, such as GPS, IMU acceleration (gravity vector), IMU orientation (magnetic sensor), and floor plane (detected in a point cloud). We have tested this package with Velodyne (HDL32e, VLP16) and RoboSense (16 channels) sensors in indoor and outdoor environments. + + + +[video](https://drive.google.com/open?id=0B9f5zFkpn4soSG96Tkt4SFFTbms) + +[![Build](https://github.com/koide3/hdl_graph_slam/actions/workflows/build.yml/badge.svg)](https://github.com/koide3/hdl_graph_slam/actions/workflows/build.yml) on melodic & noetic + +## Nodelets +***hdl_graph_slam*** consists of four nodelets. + +- *prefiltering_nodelet* +- *scan_matching_odometry_nodelet* +- *floor_detection_nodelet* +- *hdl_graph_slam_nodelet* + +The input point cloud is first downsampled by *prefiltering_nodelet*, and then passed to the next nodelets. While *scan_matching_odometry_nodelet* estimates the sensor pose by iteratively applying a scan matching between consecutive frames (i.e., odometry estimation), *floor_detection_nodelet* detects floor planes by RANSAC. The estimated odometry and the detected floor planes are sent to *hdl_graph_slam*. To compensate the accumulated error of the scan matching, it performs loop detection and optimizes a pose graph which takes various constraints into account. + + + +## Constraints (Edges) + +You can enable/disable each constraint by changing params in the launch file, and you can also change the weight (\*_stddev) and the robust kernel (\*_robust_kernel) of each constraint. + +- ***Odometry*** + +- ***Loop closure*** + +- ***GPS*** + - */gps/geopoint* (geographic_msgs/GeoPointStamped) + - */gps/navsat* (sensor_msgs/NavSatFix) + - */gpsimu_driver/nmea_sentence* (nmea_msgs/Sentence) + +hdl_graph_slam supports several GPS message types. All the supported types contain (latitude, longitude, and altitude). hdl_graph_slam converts them into [the UTM coordinate](http://wiki.ros.org/geodesy), and adds them into the graph as 3D position constraints. If altitude is set to NaN, the GPS data is treated as a 2D constrait. GeoPoint is the most basic one, which consists of only (lat, lon, alt). Although NavSatFix provides many information, we use only (lat, lon, alt) and ignore all other data. If you're using HDL32e, you can directly connect *hdl_graph_slam* with *velodyne_driver* via */gpsimu_driver/nmea_sentence*. + +- ***IMU acceleration (gravity vector)*** + - */gpsimu_driver/imu_data* (sensor_msgs/Imu) + +This constraint rotates each pose node so that the acceleration vector associated with the node becomes vertical (as the gravity vector). This is useful to compensate for accumulated tilt rotation errors of the scan matching. Since we ignore acceleration by sensor motion, you should not give a big weight for this constraint. + +- ***IMU orientation (magnetic sensor)*** + - */gpsimu_driver/imu_data* (sensor_msgs/Imu) + + If your IMU has a reliable magnetic orientation sensor, you can add orientation data to the graph as 3D rotation constraints. Note that, magnetic orientation sensors can be affected by external magnetic disturbances. In such cases, this constraint should be disabled. + +- ***Floor plane*** + - */floor_detection/floor_coeffs* (hdl_graph_slam/FloorCoeffs) + +This constraint optimizes the graph so that the floor planes (detected by RANSAC) of the pose nodes becomes the same. This is designed to compensate the accumulated rotation error of the scan matching in large flat indoor environments. + + +## Parameters +All the configurable parameters are listed in *launch/hdl_graph_slam.launch* as ros params. + +## Services +- */hdl_graph_slam/dump* (hdl_graph_slam/DumpGraph) + - save all the internal data (point clouds, floor coeffs, odoms, and pose graph) to a directory. +- */hdl_graph_slam/save_map* (hdl_graph_slam/SaveMap) + - save the generated map as a PCD file. + +## Requirements +***hdl_graph_slam*** requires the following libraries: + +- OpenMP +- PCL +- g2o +- suitesparse + +The following ROS packages are required: + +- geodesy +- nmea_msgs +- pcl_ros +- [ndt_omp](https://github.com/koide3/ndt_omp) +- [fast_gicp](https://github.com/SMRT-AIST/fast_gicp) + +```bash +# for melodic +sudo apt-get install ros-melodic-geodesy ros-melodic-pcl-ros ros-melodic-nmea-msgs ros-melodic-libg2o +cd catkin_ws/src +git clone https://github.com/koide3/ndt_omp.git -b melodic +git clone https://github.com/SMRT-AIST/fast_gicp.git --recursive +git clone https://github.com/koide3/hdl_graph_slam + +cd .. && catkin_make -DCMAKE_BUILD_TYPE=Release + +# for noetic +sudo apt-get install ros-noetic-geodesy ros-noetic-pcl-ros ros-noetic-nmea-msgs ros-noetic-libg2o + +cd catkin_ws/src +git clone https://github.com/koide3/ndt_omp.git +git clone https://github.com/SMRT-AIST/fast_gicp.git --recursive +git clone https://github.com/koide3/hdl_graph_slam + +cd .. && catkin_make -DCMAKE_BUILD_TYPE=Release +``` + +**[optional]** *bag_player.py* script requires ProgressBar2. +```bash +sudo pip install ProgressBar2 +``` + +## Example1 (Indoor) + +Bag file (recorded in a small room): + +- [hdl_501.bag.tar.gz](http://www.aisl.cs.tut.ac.jp/databases/hdl_graph_slam/hdl_501.bag.tar.gz) (raw data, 344MB) +- [hdl_501_filtered.bag.tar.gz](http://www.aisl.cs.tut.ac.jp/databases/hdl_graph_slam/hdl_501_filtered.bag.tar.gz) (downsampled data, 57MB, **Recommended!**) + +```bash +rosparam set use_sim_time true +roslaunch hdl_graph_slam hdl_graph_slam_501.launch +``` + +```bash +roscd hdl_graph_slam/rviz +rviz -d hdl_graph_slam.rviz +``` + +```bash +rosbag play --clock hdl_501_filtered.bag +``` + +We also provide bag_player.py which automatically adjusts the playback speed and processes data as fast as possible. + +```bash +rosrun hdl_graph_slam bag_player.py hdl_501_filtered.bag +``` + +You'll see a point cloud like: + + + +You can save the generated map by: +```bash +rosservice call /hdl_graph_slam/save_map "resolution: 0.05 +destination: '/full_path_directory/map.pcd'" +``` + +## Example2 (Outdoor) + +Bag file (recorded in an outdoor environment): +- [hdl_400.bag.tar.gz](http://www.aisl.cs.tut.ac.jp/databases/hdl_graph_slam/hdl_400.bag.tar.gz) (raw data, about 900MB) + +```bash +rosparam set use_sim_time true +roslaunch hdl_graph_slam hdl_graph_slam_400.launch +``` + +```bash +roscd hdl_graph_slam/rviz +rviz -d hdl_graph_slam.rviz +``` + +```bash +rosbag play --clock hdl_400.bag +``` + + + +## Example with GPS +Ford Campus Vision and Lidar Data Set [\[URL\]](http://robots.engin.umich.edu/SoftwareData/Ford) + +The following script converts the Ford Lidar Dataset to a rosbag and plays it. In this example, ***hdl_graph_slam*** utilizes the GPS data to correct the pose graph. + +```bash +cd IJRR-Dataset-2 +rosrun hdl_graph_slam ford2bag.py dataset-2.bag +rosrun hdl_graph_slam bag_player.py dataset-2.bag +``` + + + +## Use hdl_graph_slam in your system + +1. Define the transformation between your sensors (LIDAR, IMU, GPS) and base_link of your system using static_transform_publisher (see line #11, hdl_graph_slam.launch). All the sensor data will be transformed into the common base_link frame, and then fed to the SLAM algorithm. + +2. Remap the point cloud topic of ***prefiltering_nodelet***. Like: + +```bash + + ... +``` + +## Common Problems + +### Parameter tuning guide + +The mapping quality largely depends on the parameter setting. In particular, scan matching parameters have a big impact on the result. Tune the parameters accoding to the following instructions: + +- ***registration_method*** + **[updated] In short, use FAST_GICP for most cases and FAST_VGICP or NDT_OMP if the processing speed matters** This parameter allows to change the registration method to be used for odometry estimation and loop detection. Note that GICP in PCL1.7 (ROS kinetic) or earlier has a bug in the initial guess handling. **If you are on ROS kinectic or earlier, do not use GICP**. + +- ***ndt_resolution*** + This parameter decides the voxel size of NDT. Typically larger values are good for outdoor environements (0.5 - 2.0 [m] for indoor, 2.0 - 10.0 [m] for outdoor). If you chose NDT or NDT_OMP, tweak this parameter so you can obtain a good odometry estimation result. + +- ***other parameters*** + All the configurable parameters are available in the launch file. Copy a template launch file (hdl_graph_slam_501.launch for indoor, hdl_graph_slam_400.launch for outdoor) and tweak parameters in the launch file to adapt it to your application. + +## License + +This package is released under the BSD-2-Clause License. + + +Note that the cholmod solver in g2o is licensed under GPL. You may need to build g2o without cholmod dependency to avoid the GPL. + +## Related packages + +- [interactive_slam](https://github.com/koide3/interactive_slam) +- [hdl_graph_slam](https://github.com/koide3/hdl_graph_slam) +- [hdl_localization](https://github.com/koide3/hdl_localization) +- [hdl_people_tracking](https://github.com/koide3/hdl_people_tracking) + + + +## Papers +Kenji Koide, Jun Miura, and Emanuele Menegatti, A Portable 3D LIDAR-based System for Long-term and Wide-area People Behavior Measurement, Advanced Robotic Systems, 2019 [[link]](https://www.researchgate.net/publication/331283709_A_Portable_3D_LIDAR-based_System_for_Long-term_and_Wide-area_People_Behavior_Measurement). + +## Contact +Kenji Koide, k.koide@aist.go.jp, https://staff.aist.go.jp/k.koide + +Active Intelligent Systems Laboratory, Toyohashi University of Technology, Japan [\[URL\]](http://www.aisl.cs.tut.ac.jp) +Mobile Robotics Research Team, National Institute of Advanced Industrial Science and Technology (AIST), Japan [\[URL\]](https://unit.aist.go.jp/rirc/en/team/smart_mobility.html) diff --git a/apps/floor_detection_nodelet.cpp b/apps/floor_detection_nodelet.cpp new file mode 100644 index 0000000..ad714d9 --- /dev/null +++ b/apps/floor_detection_nodelet.cpp @@ -0,0 +1,269 @@ +// SPDX-License-Identifier: BSD-2-Clause + +#include +#include + +#include + +#include +#include +#include + +#include +#include +#include + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace hdl_graph_slam { + +class FloorDetectionNodelet : public nodelet::Nodelet { +public: + typedef pcl::PointXYZI PointT; + EIGEN_MAKE_ALIGNED_OPERATOR_NEW + + FloorDetectionNodelet() {} + virtual ~FloorDetectionNodelet() {} + + virtual void onInit() { + NODELET_DEBUG("initializing floor_detection_nodelet..."); + nh = getNodeHandle(); + private_nh = getPrivateNodeHandle(); + + initialize_params(); + + points_sub = nh.subscribe("/filtered_points", 256, &FloorDetectionNodelet::cloud_callback, this); + floor_pub = nh.advertise("/floor_detection/floor_coeffs", 32); + + read_until_pub = nh.advertise("/floor_detection/read_until", 32); + floor_filtered_pub = nh.advertise("/floor_detection/floor_filtered_points", 32); + floor_points_pub = nh.advertise("/floor_detection/floor_points", 32); + } + +private: + /** + * @brief initialize parameters + */ + void initialize_params() { + tilt_deg = private_nh.param("tilt_deg", 0.0); // approximate sensor tilt angle [deg] + sensor_height = private_nh.param("sensor_height", 2.0); // approximate sensor height [m] + height_clip_range = private_nh.param("height_clip_range", 1.0); // points with heights in [sensor_height - height_clip_range, sensor_height + height_clip_range] will be used for floor detection + floor_pts_thresh = private_nh.param("floor_pts_thresh", 512); // minimum number of support points of RANSAC to accept a detected floor plane + floor_normal_thresh = private_nh.param("floor_normal_thresh", 10.0); // verticality check thresold for the detected floor plane [deg] + use_normal_filtering = private_nh.param("use_normal_filtering", true); // if true, points with "non-"vertical normals will be filtered before RANSAC + normal_filter_thresh = private_nh.param("normal_filter_thresh", 20.0); // "non-"verticality check threshold [deg] + + points_topic = private_nh.param("points_topic", "/velodyne_points"); + } + + /** + * @brief callback for point clouds + * @param cloud_msg point cloud msg + */ + void cloud_callback(const sensor_msgs::PointCloud2ConstPtr& cloud_msg) { + pcl::PointCloud::Ptr cloud(new pcl::PointCloud()); + pcl::fromROSMsg(*cloud_msg, *cloud); + + if(cloud->empty()) { + return; + } + + // floor detection + boost::optional floor = detect(cloud); + + // publish the detected floor coefficients + hdl_graph_slam::FloorCoeffs coeffs; + coeffs.header = cloud_msg->header; + if(floor) { + coeffs.coeffs.resize(4); + for(int i = 0; i < 4; i++) { + coeffs.coeffs[i] = (*floor)[i]; + } + } + + floor_pub.publish(coeffs); + + // for offline estimation + std_msgs::HeaderPtr read_until(new std_msgs::Header()); + read_until->frame_id = points_topic; + read_until->stamp = cloud_msg->header.stamp + ros::Duration(1, 0); + read_until_pub.publish(read_until); + + read_until->frame_id = "/filtered_points"; + read_until_pub.publish(read_until); + } + + /** + * @brief detect the floor plane from a point cloud + * @param cloud input cloud + * @return detected floor plane coefficients + */ + boost::optional detect(const pcl::PointCloud::Ptr& cloud) const { + // compensate the tilt rotation + Eigen::Matrix4f tilt_matrix = Eigen::Matrix4f::Identity(); + tilt_matrix.topLeftCorner(3, 3) = Eigen::AngleAxisf(tilt_deg * M_PI / 180.0f, Eigen::Vector3f::UnitY()).toRotationMatrix(); + + // filtering before RANSAC (height and normal filtering) + pcl::PointCloud::Ptr filtered(new pcl::PointCloud); + pcl::transformPointCloud(*cloud, *filtered, tilt_matrix); + filtered = plane_clip(filtered, Eigen::Vector4f(0.0f, 0.0f, 1.0f, sensor_height + height_clip_range), false); + filtered = plane_clip(filtered, Eigen::Vector4f(0.0f, 0.0f, 1.0f, sensor_height - height_clip_range), true); + + if(use_normal_filtering) { + filtered = normal_filtering(filtered); + } + + pcl::transformPointCloud(*filtered, *filtered, static_cast(tilt_matrix.inverse())); + + if(floor_filtered_pub.getNumSubscribers()) { + filtered->header = cloud->header; + floor_filtered_pub.publish(*filtered); + } + + // too few points for RANSAC + if(filtered->size() < floor_pts_thresh) { + return boost::none; + } + + // RANSAC + pcl::SampleConsensusModelPlane::Ptr model_p(new pcl::SampleConsensusModelPlane(filtered)); + pcl::RandomSampleConsensus ransac(model_p); + ransac.setDistanceThreshold(0.1); + ransac.computeModel(); + + pcl::PointIndices::Ptr inliers(new pcl::PointIndices); + ransac.getInliers(inliers->indices); + + // too few inliers + if(inliers->indices.size() < floor_pts_thresh) { + return boost::none; + } + + // verticality check of the detected floor's normal + Eigen::Vector4f reference = tilt_matrix.inverse() * Eigen::Vector4f::UnitZ(); + + Eigen::VectorXf coeffs; + ransac.getModelCoefficients(coeffs); + + double dot = coeffs.head<3>().dot(reference.head<3>()); + if(std::abs(dot) < std::cos(floor_normal_thresh * M_PI / 180.0)) { + // the normal is not vertical + return boost::none; + } + + // make the normal upward + if(coeffs.head<3>().dot(Eigen::Vector3f::UnitZ()) < 0.0f) { + coeffs *= -1.0f; + } + + if(floor_points_pub.getNumSubscribers()) { + pcl::PointCloud::Ptr inlier_cloud(new pcl::PointCloud); + pcl::ExtractIndices extract; + extract.setInputCloud(filtered); + extract.setIndices(inliers); + extract.filter(*inlier_cloud); + inlier_cloud->header = cloud->header; + + floor_points_pub.publish(*inlier_cloud); + } + + return Eigen::Vector4f(coeffs); + } + + /** + * @brief plane_clip + * @param src_cloud + * @param plane + * @param negative + * @return + */ + pcl::PointCloud::Ptr plane_clip(const pcl::PointCloud::Ptr& src_cloud, const Eigen::Vector4f& plane, bool negative) const { + pcl::PlaneClipper3D clipper(plane); + pcl::PointIndices::Ptr indices(new pcl::PointIndices); + + clipper.clipPointCloud3D(*src_cloud, indices->indices); + + pcl::PointCloud::Ptr dst_cloud(new pcl::PointCloud); + + pcl::ExtractIndices extract; + extract.setInputCloud(src_cloud); + extract.setIndices(indices); + extract.setNegative(negative); + extract.filter(*dst_cloud); + + return dst_cloud; + } + + /** + * @brief filter points with non-vertical normals + * @param cloud input cloud + * @return filtered cloud + */ + pcl::PointCloud::Ptr normal_filtering(const pcl::PointCloud::Ptr& cloud) const { + pcl::NormalEstimation ne; + ne.setInputCloud(cloud); + + pcl::search::KdTree::Ptr tree(new pcl::search::KdTree); + ne.setSearchMethod(tree); + + pcl::PointCloud::Ptr normals(new pcl::PointCloud); + ne.setKSearch(10); + ne.setViewPoint(0.0f, 0.0f, sensor_height); + ne.compute(*normals); + + pcl::PointCloud::Ptr filtered(new pcl::PointCloud); + filtered->reserve(cloud->size()); + + for(int i = 0; i < cloud->size(); i++) { + float dot = normals->at(i).getNormalVector3fMap().normalized().dot(Eigen::Vector3f::UnitZ()); + if(std::abs(dot) > std::cos(normal_filter_thresh * M_PI / 180.0)) { + filtered->push_back(cloud->at(i)); + } + } + + filtered->width = filtered->size(); + filtered->height = 1; + filtered->is_dense = false; + + return filtered; + } + +private: + ros::NodeHandle nh; + ros::NodeHandle private_nh; + + // ROS topics + ros::Subscriber points_sub; + + ros::Publisher floor_pub; + ros::Publisher floor_points_pub; + ros::Publisher floor_filtered_pub; + + std::string points_topic; + ros::Publisher read_until_pub; + + // floor detection parameters + // see initialize_params() for the details + double tilt_deg; + double sensor_height; + double height_clip_range; + + int floor_pts_thresh; + double floor_normal_thresh; + + bool use_normal_filtering; + double normal_filter_thresh; +}; + +} // namespace hdl_graph_slam + +PLUGINLIB_EXPORT_CLASS(hdl_graph_slam::FloorDetectionNodelet, nodelet::Nodelet) diff --git a/apps/hdl_graph_slam_nodelet.cpp b/apps/hdl_graph_slam_nodelet.cpp new file mode 100644 index 0000000..23c9af2 --- /dev/null +++ b/apps/hdl_graph_slam_nodelet.cpp @@ -0,0 +1,1056 @@ +// SPDX-License-Identifier: BSD-2-Clause + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include +#include + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +#include +using namespace std; +namespace hdl_graph_slam { + +class HdlGraphSlamNodelet : public nodelet::Nodelet { +public: + typedef pcl::PointXYZI PointT; + typedef message_filters::sync_policies::ApproximateTime ApproxSyncPolicy; + + HdlGraphSlamNodelet() {} + virtual ~HdlGraphSlamNodelet() {} + + virtual void onInit() { + nh = getNodeHandle(); + mt_nh = getMTNodeHandle(); + private_nh = getPrivateNodeHandle(); + + // init parameters + map_frame_id = private_nh.param("map_frame_id", "map"); + odom_frame_id = private_nh.param("odom_frame_id", "odom"); + map_cloud_resolution = private_nh.param("map_cloud_resolution", 0.05); + trans_odom2map.setIdentity(); + + max_keyframes_per_update = private_nh.param("max_keyframes_per_update", 10); + + // + anchor_node = nullptr; + anchor_edge = nullptr; + floor_plane_node = nullptr; + graph_slam.reset(new GraphSLAM(private_nh.param("g2o_solver_type", "lm_var"))); + keyframe_updater.reset(new KeyframeUpdater(private_nh)); + loop_detector.reset(new LoopDetector(private_nh)); + map_cloud_generator.reset(new MapCloudGenerator()); + inf_calclator.reset(new InformationMatrixCalculator(private_nh)); + nmea_parser.reset(new NmeaSentenceParser()); + + gps_time_offset = private_nh.param("gps_time_offset", 0.0); + gps_edge_stddev_xy = private_nh.param("gps_edge_stddev_xy", 10000.0); + gps_edge_stddev_z = private_nh.param("gps_edge_stddev_z", 10.0); + floor_edge_stddev = private_nh.param("floor_edge_stddev", 10.0); + seq_ = private_nh.param("Sequence", "seq"); + std::cout<<"seq: "<("use_prev_test_", false); + + imu_time_offset = private_nh.param("imu_time_offset", 0.0); + enable_imu_orientation = private_nh.param("enable_imu_orientation", false); + enable_imu_acceleration = private_nh.param("enable_imu_acceleration", false); + imu_orientation_edge_stddev = private_nh.param("imu_orientation_edge_stddev", 0.1); + imu_acceleration_edge_stddev = private_nh.param("imu_acceleration_edge_stddev", 3.0); + + points_topic = private_nh.param("points_topic", "/velodyne_points"); + + // subscribers + odom_sub.reset(new message_filters::Subscriber(mt_nh, "/odom", 256)); + cloud_sub.reset(new message_filters::Subscriber(mt_nh, "/filtered_points", 32)); + sync.reset(new message_filters::Synchronizer(ApproxSyncPolicy(32), *odom_sub, *cloud_sub)); + sync->registerCallback(boost::bind(&HdlGraphSlamNodelet::cloud_callback, this, _1, _2)); + imu_sub = nh.subscribe("/gpsimu_driver/imu_data", 1024, &HdlGraphSlamNodelet::imu_callback, this); + floor_sub = nh.subscribe("/floor_detection/floor_coeffs", 1024, &HdlGraphSlamNodelet::floor_coeffs_callback, this); + + if(private_nh.param("enable_gps", true)) { + gps_sub = mt_nh.subscribe("/gps/geopoint", 1024, &HdlGraphSlamNodelet::gps_callback, this); + nmea_sub = mt_nh.subscribe("/gpsimu_driver/nmea_sentence", 1024, &HdlGraphSlamNodelet::nmea_callback, this); + navsat_sub = mt_nh.subscribe("/gps/navsat", 1024, &HdlGraphSlamNodelet::navsat_callback, this); + } + + // publishers + markers_pub = mt_nh.advertise("/hdl_graph_slam/markers", 16); + odom2map_pub = mt_nh.advertise("/hdl_graph_slam/odom2pub", 16); + map_points_pub = mt_nh.advertise("/hdl_graph_slam/map_points", 1, true); + read_until_pub = mt_nh.advertise("/hdl_graph_slam/read_until", 32); + + dump_service_server = mt_nh.advertiseService("/hdl_graph_slam/dump", &HdlGraphSlamNodelet::dump_service, this); + save_map_service_server = mt_nh.advertiseService("/hdl_graph_slam/save_map", &HdlGraphSlamNodelet::save_map_service, this); + + graph_updated = false; + double graph_update_interval = private_nh.param("graph_update_interval", 3.0); + double map_cloud_update_interval = private_nh.param("map_cloud_update_interval", 10.0); + optimization_timer = mt_nh.createWallTimer(ros::WallDuration(graph_update_interval), &HdlGraphSlamNodelet::optimization_timer_callback, this); + map_publish_timer = mt_nh.createWallTimer(ros::WallDuration(map_cloud_update_interval), &HdlGraphSlamNodelet::map_points_publish_timer_callback, this); + } + +private: + /** + * @brief received point clouds are pushed to #keyframe_queue + * @param odom_msg + * @param cloud_msg + */ + void cloud_callback(const nav_msgs::OdometryConstPtr& odom_msg, const sensor_msgs::PointCloud2::ConstPtr& cloud_msg) { + const ros::Time& stamp = cloud_msg->header.stamp; + Eigen::Isometry3d odom = odom2isometry(odom_msg); + + pcl::PointCloud::Ptr cloud(new pcl::PointCloud()); + pcl::fromROSMsg(*cloud_msg, *cloud); + if(base_frame_id.empty()) { + base_frame_id = cloud_msg->header.frame_id; + } + + if(!keyframe_updater->update(odom)) { + std::lock_guard lock(keyframe_queue_mutex); + if(keyframe_queue.empty()) { + std_msgs::Header read_until; + read_until.stamp = stamp + ros::Duration(10, 0); + read_until.frame_id = points_topic; + read_until_pub.publish(read_until); + read_until.frame_id = "/filtered_points"; + read_until_pub.publish(read_until); + } + + return; + } + + double accum_d = keyframe_updater->get_accum_distance(); + + pcl::Filter::Ptr keyframe_downsample_filter; + double res_ = 0.5; + auto keyframe_voxelgrid = new pcl::VoxelGrid(); + keyframe_voxelgrid->setLeafSize(res_, res_, res_); + keyframe_downsample_filter.reset(keyframe_voxelgrid); + + pcl::PointCloud::Ptr filtered(new pcl::PointCloud()); + keyframe_downsample_filter->setInputCloud(cloud); + keyframe_downsample_filter->filter(*filtered); + KeyFrame::Ptr keyframe(new KeyFrame(stamp, odom, accum_d, filtered)); + + std::lock_guard lock(keyframe_queue_mutex); + keyframe_queue.push_back(keyframe); + } + + /** + * @brief this method adds all the keyframes in #keyframe_queue to the pose graph (odometry edges) + * @return if true, at least one keyframe was added to the pose graph + */ + bool flush_keyframe_queue() { + std::lock_guard lock(keyframe_queue_mutex); + + if(keyframe_queue.empty()) { + return false; + } + + trans_odom2map_mutex.lock(); + Eigen::Isometry3d odom2map(trans_odom2map.cast()); + trans_odom2map_mutex.unlock(); + + int num_processed = 0; + for(int i = 0; i < std::min(keyframe_queue.size(), max_keyframes_per_update); i++) { + num_processed = i; + + const auto& keyframe = keyframe_queue[i]; + // new_keyframes will be tested later for loop closure + new_keyframes.push_back(keyframe); + + // add pose node + Eigen::Isometry3d odom = odom2map * keyframe->odom; + keyframe->node = graph_slam->add_se3_node(odom); + keyframe_hash[keyframe->stamp] = keyframe; + + // fix the first node + if(keyframes.empty() && new_keyframes.size() == 1) { + if(private_nh.param("fix_first_node", false)) { + Eigen::MatrixXd inf = Eigen::MatrixXd::Identity(6, 6); + std::stringstream sst(private_nh.param("fix_first_node_stddev", "1 1 1 1 1 1")); + for(int i = 0; i < 6; i++) { + double stddev = 1.0; + sst >> stddev; + inf(i, i) = 1.0 / stddev; + } + + anchor_node = graph_slam->add_se3_node(Eigen::Isometry3d::Identity()); + anchor_node->setFixed(true); + anchor_edge = graph_slam->add_se3_edge(anchor_node, keyframe->node, Eigen::Isometry3d::Identity(), inf); + } + } + + if(i == 0 && keyframes.empty()) { + continue; + } + + // add edge between consecutive keyframes + const auto& prev_keyframe = i == 0 ? keyframes.back() : keyframe_queue[i - 1]; + + Eigen::Isometry3d relative_pose = keyframe->odom.inverse() * prev_keyframe->odom; + Eigen::MatrixXd information = inf_calclator->calc_information_matrix(keyframe->cloud, prev_keyframe->cloud, relative_pose); + auto edge = graph_slam->add_se3_edge(keyframe->node, prev_keyframe->node, relative_pose, information); + graph_slam->add_robust_kernel(edge, private_nh.param("odometry_edge_robust_kernel", "NONE"), private_nh.param("odometry_edge_robust_kernel_size", 1.0)); + } + + std_msgs::Header read_until; + read_until.stamp = keyframe_queue[num_processed]->stamp + ros::Duration(10, 0); + read_until.frame_id = points_topic; + read_until_pub.publish(read_until); + read_until.frame_id = "/filtered_points"; + read_until_pub.publish(read_until); + + keyframe_queue.erase(keyframe_queue.begin(), keyframe_queue.begin() + num_processed + 1); + return true; + } + + void nmea_callback(const nmea_msgs::SentenceConstPtr& nmea_msg) { + GPRMC grmc = nmea_parser->parse(nmea_msg->sentence); + + if(grmc.status != 'A') { + return; + } + + geographic_msgs::GeoPointStampedPtr gps_msg(new geographic_msgs::GeoPointStamped()); + gps_msg->header = nmea_msg->header; + gps_msg->position.latitude = grmc.latitude; + gps_msg->position.longitude = grmc.longitude; + gps_msg->position.altitude = NAN; + + gps_callback(gps_msg); + } + + void navsat_callback(const sensor_msgs::NavSatFixConstPtr& navsat_msg) { + geographic_msgs::GeoPointStampedPtr gps_msg(new geographic_msgs::GeoPointStamped()); + gps_msg->header = navsat_msg->header; + gps_msg->position.latitude = navsat_msg->latitude; + gps_msg->position.longitude = navsat_msg->longitude; + gps_msg->position.altitude = navsat_msg->altitude; + gps_callback(gps_msg); + } + + /** + * @brief received gps data is added to #gps_queue + * @param gps_msg + */ + void gps_callback(const geographic_msgs::GeoPointStampedPtr& gps_msg) { + std::lock_guard lock(gps_queue_mutex); + gps_msg->header.stamp += ros::Duration(gps_time_offset); + gps_queue.push_back(gps_msg); + } + + /** + * @brief + * @return + */ + bool flush_gps_queue() { + std::lock_guard lock(gps_queue_mutex); + + if(keyframes.empty() || gps_queue.empty()) { + return false; + } + + bool updated = false; + auto gps_cursor = gps_queue.begin(); + + for(auto& keyframe : keyframes) { + if(keyframe->stamp > gps_queue.back()->header.stamp) { + break; + } + + if(keyframe->stamp < (*gps_cursor)->header.stamp || keyframe->utm_coord) { + continue; + } + + // find the gps data which is closest to the keyframe + auto closest_gps = gps_cursor; + for(auto gps = gps_cursor; gps != gps_queue.end(); gps++) { + auto dt = ((*closest_gps)->header.stamp - keyframe->stamp).toSec(); + auto dt2 = ((*gps)->header.stamp - keyframe->stamp).toSec(); + if(std::abs(dt) < std::abs(dt2)) { + break; + } + + closest_gps = gps; + } + + // if the time residual between the gps and keyframe is too large, skip it + gps_cursor = closest_gps; + if(0.2 < std::abs(((*closest_gps)->header.stamp - keyframe->stamp).toSec())) { + continue; + } + + // convert (latitude, longitude, altitude) -> (easting, northing, altitude) in UTM coordinate + geodesy::UTMPoint utm; + geodesy::fromMsg((*closest_gps)->position, utm); + Eigen::Vector3d xyz(utm.easting, utm.northing, utm.altitude); + + // the first gps data position will be the origin of the map + if(!zero_utm) { + zero_utm = xyz; + } + xyz -= (*zero_utm); + + keyframe->utm_coord = xyz; + + g2o::OptimizableGraph::Edge* edge; + if(std::isnan(xyz.z())) { + Eigen::Matrix2d information_matrix = Eigen::Matrix2d::Identity() / gps_edge_stddev_xy; + edge = graph_slam->add_se3_prior_xy_edge(keyframe->node, xyz.head<2>(), information_matrix); + } else { + Eigen::Matrix3d information_matrix = Eigen::Matrix3d::Identity(); + information_matrix.block<2, 2>(0, 0) /= gps_edge_stddev_xy; + information_matrix(2, 2) /= gps_edge_stddev_z; + edge = graph_slam->add_se3_prior_xyz_edge(keyframe->node, xyz, information_matrix); + } + graph_slam->add_robust_kernel(edge, private_nh.param("gps_edge_robust_kernel", "NONE"), private_nh.param("gps_edge_robust_kernel_size", 1.0)); + + updated = true; + } + + auto remove_loc = std::upper_bound(gps_queue.begin(), gps_queue.end(), keyframes.back()->stamp, [=](const ros::Time& stamp, const geographic_msgs::GeoPointStampedConstPtr& geopoint) { return stamp < geopoint->header.stamp; }); + gps_queue.erase(gps_queue.begin(), remove_loc); + return updated; + } + + void imu_callback(const sensor_msgs::ImuPtr& imu_msg) { + if(!enable_imu_orientation && !enable_imu_acceleration) { + return; + } + + std::lock_guard lock(imu_queue_mutex); + imu_msg->header.stamp += ros::Duration(imu_time_offset); + imu_queue.push_back(imu_msg); + } + + bool flush_imu_queue() { + std::lock_guard lock(imu_queue_mutex); + if(keyframes.empty() || imu_queue.empty() || base_frame_id.empty()) { + return false; + } + + bool updated = false; + auto imu_cursor = imu_queue.begin(); + + for(auto& keyframe : keyframes) { + if(keyframe->stamp > imu_queue.back()->header.stamp) { + break; + } + + if(keyframe->stamp < (*imu_cursor)->header.stamp || keyframe->acceleration) { + continue; + } + + // find imu data which is closest to the keyframe + auto closest_imu = imu_cursor; + for(auto imu = imu_cursor; imu != imu_queue.end(); imu++) { + auto dt = ((*closest_imu)->header.stamp - keyframe->stamp).toSec(); + auto dt2 = ((*imu)->header.stamp - keyframe->stamp).toSec(); + if(std::abs(dt) < std::abs(dt2)) { + break; + } + + closest_imu = imu; + } + + imu_cursor = closest_imu; + if(0.2 < std::abs(((*closest_imu)->header.stamp - keyframe->stamp).toSec())) { + continue; + } + + const auto& imu_ori = (*closest_imu)->orientation; + const auto& imu_acc = (*closest_imu)->linear_acceleration; + + geometry_msgs::Vector3Stamped acc_imu; + geometry_msgs::Vector3Stamped acc_base; + geometry_msgs::QuaternionStamped quat_imu; + geometry_msgs::QuaternionStamped quat_base; + + quat_imu.header.frame_id = acc_imu.header.frame_id = (*closest_imu)->header.frame_id; + quat_imu.header.stamp = acc_imu.header.stamp = ros::Time(0); + acc_imu.vector = (*closest_imu)->linear_acceleration; + quat_imu.quaternion = (*closest_imu)->orientation; + + try { + tf_listener.transformVector(base_frame_id, acc_imu, acc_base); + tf_listener.transformQuaternion(base_frame_id, quat_imu, quat_base); + } catch(std::exception& e) { + std::cerr << "failed to find transform!!" << std::endl; + return false; + } + + keyframe->acceleration = Eigen::Vector3d(acc_base.vector.x, acc_base.vector.y, acc_base.vector.z); + keyframe->orientation = Eigen::Quaterniond(quat_base.quaternion.w, quat_base.quaternion.x, quat_base.quaternion.y, quat_base.quaternion.z); + keyframe->orientation = keyframe->orientation; + if(keyframe->orientation->w() < 0.0) { + keyframe->orientation->coeffs() = -keyframe->orientation->coeffs(); + } + + if(enable_imu_orientation) { + Eigen::MatrixXd info = Eigen::MatrixXd::Identity(3, 3) / imu_orientation_edge_stddev; + auto edge = graph_slam->add_se3_prior_quat_edge(keyframe->node, *keyframe->orientation, info); + graph_slam->add_robust_kernel(edge, private_nh.param("imu_orientation_edge_robust_kernel", "NONE"), private_nh.param("imu_orientation_edge_robust_kernel_size", 1.0)); + } + + if(enable_imu_acceleration) { + Eigen::MatrixXd info = Eigen::MatrixXd::Identity(3, 3) / imu_acceleration_edge_stddev; + g2o::OptimizableGraph::Edge* edge = graph_slam->add_se3_prior_vec_edge(keyframe->node, -Eigen::Vector3d::UnitZ(), *keyframe->acceleration, info); + graph_slam->add_robust_kernel(edge, private_nh.param("imu_acceleration_edge_robust_kernel", "NONE"), private_nh.param("imu_acceleration_edge_robust_kernel_size", 1.0)); + } + updated = true; + } + + auto remove_loc = std::upper_bound(imu_queue.begin(), imu_queue.end(), keyframes.back()->stamp, [=](const ros::Time& stamp, const sensor_msgs::ImuConstPtr& imu) { return stamp < imu->header.stamp; }); + imu_queue.erase(imu_queue.begin(), remove_loc); + + return updated; + } + + /** + * @brief received floor coefficients are added to #floor_coeffs_queue + * @param floor_coeffs_msg + */ + void floor_coeffs_callback(const hdl_graph_slam::FloorCoeffsConstPtr& floor_coeffs_msg) { + if(floor_coeffs_msg->coeffs.empty()) { + return; + } + + std::lock_guard lock(floor_coeffs_queue_mutex); + floor_coeffs_queue.push_back(floor_coeffs_msg); + } + + /** + * @brief this methods associates floor coefficients messages with registered keyframes, and then adds the associated coeffs to the pose graph + * @return if true, at least one floor plane edge is added to the pose graph + */ + bool flush_floor_queue() { + std::lock_guard lock(floor_coeffs_queue_mutex); + + if(keyframes.empty()) { + return false; + } + + const auto& latest_keyframe_stamp = keyframes.back()->stamp; + + bool updated = false; + for(const auto& floor_coeffs : floor_coeffs_queue) { + if(floor_coeffs->header.stamp > latest_keyframe_stamp) { + break; + } + + auto found = keyframe_hash.find(floor_coeffs->header.stamp); + if(found == keyframe_hash.end()) { + continue; + } + + if(!floor_plane_node) { + floor_plane_node = graph_slam->add_plane_node(Eigen::Vector4d(0.0, 0.0, 1.0, 0.0)); + floor_plane_node->setFixed(true); + } + + const auto& keyframe = found->second; + + Eigen::Vector4d coeffs(floor_coeffs->coeffs[0], floor_coeffs->coeffs[1], floor_coeffs->coeffs[2], floor_coeffs->coeffs[3]); + Eigen::Matrix3d information = Eigen::Matrix3d::Identity() * (1.0 / floor_edge_stddev); + auto edge = graph_slam->add_se3_plane_edge(keyframe->node, floor_plane_node, coeffs, information); + graph_slam->add_robust_kernel(edge, private_nh.param("floor_edge_robust_kernel", "NONE"), private_nh.param("floor_edge_robust_kernel_size", 1.0)); + + keyframe->floor_coeffs = coeffs; + + updated = true; + } + + auto remove_loc = std::upper_bound(floor_coeffs_queue.begin(), floor_coeffs_queue.end(), latest_keyframe_stamp, [=](const ros::Time& stamp, const hdl_graph_slam::FloorCoeffsConstPtr& coeffs) { return stamp < coeffs->header.stamp; }); + floor_coeffs_queue.erase(floor_coeffs_queue.begin(), remove_loc); + return updated; + } + + /** + * @brief generate map point cloud and publish it + * @param event + */ + void map_points_publish_timer_callback(const ros::WallTimerEvent& event) { + if(!map_points_pub.getNumSubscribers() || !graph_updated) { + return; + } + + std::vector snapshot; + + keyframes_snapshot_mutex.lock(); + snapshot = keyframes_snapshot; + keyframes_snapshot_mutex.unlock(); + + auto cloud = map_cloud_generator->generate(snapshot, map_cloud_resolution); + if(!cloud) { + return; + } + + cloud->header.frame_id = map_frame_id; + cloud->header.stamp = snapshot.back()->cloud->header.stamp; + + sensor_msgs::PointCloud2Ptr cloud_msg(new sensor_msgs::PointCloud2()); + pcl::toROSMsg(*cloud, *cloud_msg); + + map_points_pub.publish(cloud_msg); + } + + /** + * @brief this methods adds all the data in the queues to the pose graph, and then optimizes the pose graph + * @param event + */ + void optimization_timer_callback(const ros::WallTimerEvent& event) { + std::lock_guard lock(main_thread_mutex); + + // add keyframes and floor coeffs in the queues to the pose graph + bool keyframe_updated = flush_keyframe_queue(); + + if(!keyframe_updated) { + std_msgs::Header read_until; + read_until.stamp = ros::Time::now() + ros::Duration(30, 0); + read_until.frame_id = points_topic; + read_until_pub.publish(read_until); + read_until.frame_id = "/filtered_points"; + read_until_pub.publish(read_until); + } + + + if(!keyframe_updated & !flush_floor_queue()) { + return; + } + + // loop detection + bool loop_detect_ = true; + if(loop_detect_) + { + std::vector loops = loop_detector->detect(keyframes, new_keyframes, *graph_slam); + std::cout<<"loops size: "<relative_pose.cast()); + Eigen::MatrixXd information_matrix = inf_calclator->calc_information_matrix(loop->key1->cloud, loop->key2->cloud, relpose); + auto edge = graph_slam->add_se3_edge(loop->key1->node, loop->key2->node, relpose, information_matrix); + graph_slam->add_robust_kernel(edge, private_nh.param("loop_closure_edge_robust_kernel", "NONE"), private_nh.param("loop_closure_edge_robust_kernel_size", 1.0)); + } + + } + + std::copy(new_keyframes.begin(), new_keyframes.end(), std::back_inserter(keyframes)); + new_keyframes.clear(); + + // move the first node anchor position to the current estimate of the first node pose + // so the first node moves freely while trying to stay around the origin + if(anchor_node && private_nh.param("fix_first_node_adaptive", true)) { + Eigen::Isometry3d anchor_target = static_cast(anchor_edge->vertices()[1])->estimate(); + anchor_node->setEstimate(anchor_target); + } + + // optimize the pose graph + bool use_g2o = true; + if(use_g2o) + { + int num_iterations = private_nh.param("g2o_solver_num_iterations", 1024); + graph_slam->optimize(num_iterations); + + } + + + //TO save the trajectory. + cout<<"begin save trajectory"<node->estimate(); + keyframe_M = keyframe_T.matrix(); + R = keyframe_M.block<3,3>(0,0); + q = R; + traj<stamp<<" "<get_accum_distance()<node->estimate() * keyframe->odom.inverse(); + trans_odom2map_mutex.lock(); + trans_odom2map = trans.matrix().cast(); + trans_odom2map_mutex.unlock(); + + std::vector snapshot(keyframes.size()); + std::transform(keyframes.begin(), keyframes.end(), snapshot.begin(), [=](const KeyFrame::Ptr& k) { return std::make_shared(k); }); + + keyframes_snapshot_mutex.lock(); + keyframes_snapshot.swap(snapshot); + keyframes_snapshot_mutex.unlock(); + graph_updated = true; + + if(odom2map_pub.getNumSubscribers()) { + geometry_msgs::TransformStamped ts = matrix2transform(keyframe->stamp, trans.matrix().cast(), map_frame_id, odom_frame_id); + odom2map_pub.publish(ts); + } + + if(markers_pub.getNumSubscribers()) { + auto markers = create_marker_array(ros::Time::now()); + markers_pub.publish(markers); + } + } + + /** + * @brief create visualization marker + * @param stamp + * @return + */ + visualization_msgs::MarkerArray create_marker_array(const ros::Time& stamp) const { + visualization_msgs::MarkerArray markers; + markers.markers.resize(4); + + // node markers + visualization_msgs::Marker& traj_marker = markers.markers[0]; + traj_marker.header.frame_id = "map"; + traj_marker.header.stamp = stamp; + traj_marker.ns = "nodes"; + traj_marker.id = 0; + traj_marker.type = visualization_msgs::Marker::SPHERE_LIST; + + traj_marker.pose.orientation.w = 1.0; + traj_marker.scale.x = traj_marker.scale.y = traj_marker.scale.z = 2.0;//1.0 + + visualization_msgs::Marker& imu_marker = markers.markers[1]; + imu_marker.header = traj_marker.header; + imu_marker.ns = "imu"; + imu_marker.id = 1; + imu_marker.type = visualization_msgs::Marker::SPHERE_LIST; + + imu_marker.pose.orientation.w = 1.0; + imu_marker.scale.x = imu_marker.scale.y = imu_marker.scale.z = 0.75; + + traj_marker.points.resize(keyframes.size()); + traj_marker.colors.resize(keyframes.size()); + for(int i = 0; i < keyframes.size(); i++) { + Eigen::Vector3d pos = keyframes[i]->node->estimate().translation(); + traj_marker.points[i].x = pos.x(); + traj_marker.points[i].y = pos.y(); + traj_marker.points[i].z = pos.z(); + + double p = static_cast(i) / keyframes.size(); + traj_marker.colors[i].r = 1.0 - p; + traj_marker.colors[i].g = p; + traj_marker.colors[i].b = 0.0; + traj_marker.colors[i].a = 1.0; + + if(keyframes[i]->acceleration) { + Eigen::Vector3d pos = keyframes[i]->node->estimate().translation(); + geometry_msgs::Point point; + point.x = pos.x(); + point.y = pos.y(); + point.z = pos.z(); + + std_msgs::ColorRGBA color; + color.r = 0.0; + color.g = 0.0; + color.b = 1.0; + color.a = 0.1; + + imu_marker.points.push_back(point); + imu_marker.colors.push_back(color); + } + } + + // edge markers + visualization_msgs::Marker& edge_marker = markers.markers[2]; + edge_marker.header.frame_id = "map"; + edge_marker.header.stamp = stamp; + edge_marker.ns = "edges"; + edge_marker.id = 2; + edge_marker.type = visualization_msgs::Marker::LINE_LIST; + + edge_marker.pose.orientation.w = 1.0; + edge_marker.scale.x = 2;//0.05 + + edge_marker.points.resize(graph_slam->graph->edges().size() * 2); + edge_marker.colors.resize(graph_slam->graph->edges().size() * 2); + + auto edge_itr = graph_slam->graph->edges().begin(); + for(int i = 0; edge_itr != graph_slam->graph->edges().end(); edge_itr++, i++) { + g2o::HyperGraph::Edge* edge = *edge_itr; + g2o::EdgeSE3* edge_se3 = dynamic_cast(edge); + if(edge_se3) { + g2o::VertexSE3* v1 = dynamic_cast(edge_se3->vertices()[0]); + g2o::VertexSE3* v2 = dynamic_cast(edge_se3->vertices()[1]); + Eigen::Vector3d pt1 = v1->estimate().translation(); + Eigen::Vector3d pt2 = v2->estimate().translation(); + + edge_marker.points[i * 2].x = pt1.x(); + edge_marker.points[i * 2].y = pt1.y(); + edge_marker.points[i * 2].z = pt1.z(); + edge_marker.points[i * 2 + 1].x = pt2.x(); + edge_marker.points[i * 2 + 1].y = pt2.y(); + edge_marker.points[i * 2 + 1].z = pt2.z(); + + double p1 = static_cast(v1->id()) / graph_slam->graph->vertices().size(); + double p2 = static_cast(v2->id()) / graph_slam->graph->vertices().size(); + + edge_marker.colors[i * 2].r = 1.0 - p1; + edge_marker.colors[i * 2].g = p1; + edge_marker.colors[i * 2].a = 1.0; + edge_marker.colors[i * 2 + 1].r = 1.0 - p2; + edge_marker.colors[i * 2 + 1].g = p2; + edge_marker.colors[i * 2 + 1].a = 1.0; + + + if(std::abs(v1->id() - v2->id()) > 2) { + edge_marker.points[i * 2].z += 0.5; + edge_marker.points[i * 2 + 1].z += 0.5; + } + + continue; + } + + g2o::EdgeSE3Plane* edge_plane = dynamic_cast(edge); + if(edge_plane) { + g2o::VertexSE3* v1 = dynamic_cast(edge_plane->vertices()[0]); + Eigen::Vector3d pt1 = v1->estimate().translation(); + Eigen::Vector3d pt2(pt1.x(), pt1.y(), 0.0); + + edge_marker.points[i * 2].x = pt1.x(); + edge_marker.points[i * 2].y = pt1.y(); + edge_marker.points[i * 2].z = pt1.z(); + edge_marker.points[i * 2 + 1].x = pt2.x(); + edge_marker.points[i * 2 + 1].y = pt2.y(); + edge_marker.points[i * 2 + 1].z = pt2.z(); + + edge_marker.colors[i * 2].b = 1.0; + edge_marker.colors[i * 2].a = 1.0; + edge_marker.colors[i * 2 + 1].b = 1.0; + edge_marker.colors[i * 2 + 1].a = 1.0; + + continue; + } + + g2o::EdgeSE3PriorXY* edge_priori_xy = dynamic_cast(edge); + if(edge_priori_xy) { + g2o::VertexSE3* v1 = dynamic_cast(edge_priori_xy->vertices()[0]); + Eigen::Vector3d pt1 = v1->estimate().translation(); + Eigen::Vector3d pt2 = Eigen::Vector3d::Zero(); + pt2.head<2>() = edge_priori_xy->measurement(); + + edge_marker.points[i * 2].x = pt1.x(); + edge_marker.points[i * 2].y = pt1.y(); + edge_marker.points[i * 2].z = pt1.z() + 0.5; + edge_marker.points[i * 2 + 1].x = pt2.x(); + edge_marker.points[i * 2 + 1].y = pt2.y(); + edge_marker.points[i * 2 + 1].z = pt2.z() + 0.5; + + edge_marker.colors[i * 2].r = 1.0; + edge_marker.colors[i * 2].a = 1.0; + edge_marker.colors[i * 2 + 1].r = 1.0; + edge_marker.colors[i * 2 + 1].a = 1.0; + + continue; + } + + g2o::EdgeSE3PriorXYZ* edge_priori_xyz = dynamic_cast(edge); + if(edge_priori_xyz) { + g2o::VertexSE3* v1 = dynamic_cast(edge_priori_xyz->vertices()[0]); + Eigen::Vector3d pt1 = v1->estimate().translation(); + Eigen::Vector3d pt2 = edge_priori_xyz->measurement(); + + edge_marker.points[i * 2].x = pt1.x(); + edge_marker.points[i * 2].y = pt1.y(); + edge_marker.points[i * 2].z = pt1.z() + 0.5; + edge_marker.points[i * 2 + 1].x = pt2.x(); + edge_marker.points[i * 2 + 1].y = pt2.y(); + edge_marker.points[i * 2 + 1].z = pt2.z(); + + edge_marker.colors[i * 2].r = 1.0; + edge_marker.colors[i * 2].a = 1.0; + edge_marker.colors[i * 2 + 1].r = 1.0; + edge_marker.colors[i * 2 + 1].a = 1.0; + + continue; + } + } + + // sphere + visualization_msgs::Marker& sphere_marker = markers.markers[3]; + sphere_marker.header.frame_id = "map"; + sphere_marker.header.stamp = stamp; + sphere_marker.ns = "loop_close_radius"; + sphere_marker.id = 3; + sphere_marker.type = visualization_msgs::Marker::SPHERE; + + if(!keyframes.empty()) { + Eigen::Vector3d pos = keyframes.back()->node->estimate().translation(); + sphere_marker.pose.position.x = pos.x(); + sphere_marker.pose.position.y = pos.y(); + sphere_marker.pose.position.z = pos.z(); + } + sphere_marker.pose.orientation.w = 1.0; + sphere_marker.scale.x = sphere_marker.scale.y = sphere_marker.scale.z = loop_detector->get_distance_thresh() * 2.0; + + sphere_marker.color.r = 1.0; + sphere_marker.color.a = 0.3; + + return markers; + } + + /** + * @brief dump all data to the current directory + * @param req + * @param res + * @return + */ + bool dump_service(hdl_graph_slam::DumpGraphRequest& req, hdl_graph_slam::DumpGraphResponse& res) { + std::lock_guard lock(main_thread_mutex); + + std::string directory = req.destination; + + if(directory.empty()) { + std::array buffer; + buffer.fill(0); + time_t rawtime; + time(&rawtime); + const auto timeinfo = localtime(&rawtime); + strftime(buffer.data(), sizeof(buffer), "%d-%m-%Y %H:%M:%S", timeinfo); + } + + if(!boost::filesystem::is_directory(directory)) { + boost::filesystem::create_directory(directory); + } + + std::cout << "all data dumped to:" << directory << std::endl; + + graph_slam->save(directory + "/graph.g2o"); + for(int i = 0; i < keyframes.size(); i++) { + std::stringstream sst; + sst << boost::format("%s/%06d") % directory % i; + + keyframes[i]->save(sst.str()); + } + + if(zero_utm) { + std::ofstream zero_utm_ofs(directory + "/zero_utm"); + zero_utm_ofs << boost::format("%.6f %.6f %.6f") % zero_utm->x() % zero_utm->y() % zero_utm->z() << std::endl; + } + + std::ofstream ofs(directory + "/special_nodes.csv"); + ofs << "anchor_node " << (anchor_node == nullptr ? -1 : anchor_node->id()) << std::endl; + ofs << "anchor_edge " << (anchor_edge == nullptr ? -1 : anchor_edge->id()) << std::endl; + ofs << "floor_node " << (floor_plane_node == nullptr ? -1 : floor_plane_node->id()) << std::endl; + + res.success = true; + return true; + } + + /** + * @brief save map data as pcd + * @param req + * @param res + * @return + */ + bool save_map_service(hdl_graph_slam::SaveMapRequest& req, hdl_graph_slam::SaveMapResponse& res) { + std::vector snapshot; + + keyframes_snapshot_mutex.lock(); + snapshot = keyframes_snapshot; + keyframes_snapshot_mutex.unlock(); + + auto cloud = map_cloud_generator->generate(snapshot, req.resolution); + if(!cloud) { + res.success = false; + return true; + } + + if(zero_utm && req.utm) { + for(auto& pt : cloud->points) { + pt.getVector3fMap() += (*zero_utm).cast(); + } + } + + cloud->header.frame_id = map_frame_id; + cloud->header.stamp = snapshot.back()->cloud->header.stamp; + + if(zero_utm) { + std::ofstream ofs(req.destination + ".utm"); + ofs << boost::format("%.6f %.6f %.6f") % zero_utm->x() % zero_utm->y() % zero_utm->z() << std::endl; + } + + int ret = pcl::io::savePCDFileBinary(req.destination, *cloud); + res.success = ret == 0; + + return true; + } + +private: + // ROS + ros::NodeHandle nh; + ros::NodeHandle mt_nh; + ros::NodeHandle private_nh; + ros::WallTimer optimization_timer; + ros::WallTimer map_publish_timer; + + std::unique_ptr> odom_sub; + std::unique_ptr> cloud_sub; + std::unique_ptr> sync; + + ros::Subscriber gps_sub; + ros::Subscriber nmea_sub; + ros::Subscriber navsat_sub; + + ros::Subscriber imu_sub; + ros::Subscriber floor_sub; + + ros::Publisher markers_pub; + + std::string map_frame_id; + std::string odom_frame_id; + + std::mutex trans_odom2map_mutex; + Eigen::Matrix4f trans_odom2map; + ros::Publisher odom2map_pub; + + std::string points_topic; + ros::Publisher read_until_pub; + ros::Publisher map_points_pub; + + tf::TransformListener tf_listener; + + ros::ServiceServer dump_service_server; + ros::ServiceServer save_map_service_server; + + // keyframe queue + std::string base_frame_id; + std::mutex keyframe_queue_mutex; + std::deque keyframe_queue; + + // gps queue + double gps_time_offset; + double gps_edge_stddev_xy; + double gps_edge_stddev_z; + boost::optional zero_utm; + std::mutex gps_queue_mutex; + std::deque gps_queue; + + // imu queue + double imu_time_offset; + bool enable_imu_orientation; + double imu_orientation_edge_stddev; + bool enable_imu_acceleration; + double imu_acceleration_edge_stddev; + std::mutex imu_queue_mutex; + std::deque imu_queue; + + // floor_coeffs queue + double floor_edge_stddev; + std::mutex floor_coeffs_queue_mutex; + std::deque floor_coeffs_queue; + + // for map cloud generation + std::atomic_bool graph_updated; + double map_cloud_resolution; + std::mutex keyframes_snapshot_mutex; + std::vector keyframes_snapshot; + std::unique_ptr map_cloud_generator; + + // graph slam + // all the below members must be accessed after locking main_thread_mutex + std::mutex main_thread_mutex; + + int max_keyframes_per_update; + std::deque new_keyframes; + + g2o::VertexSE3* anchor_node; + g2o::EdgeSE3* anchor_edge; + g2o::VertexPlane* floor_plane_node; + std::vector keyframes; + std::unordered_map keyframe_hash; + + std::unique_ptr graph_slam; + std::unique_ptr loop_detector; + std::unique_ptr keyframe_updater; + std::unique_ptr nmea_parser; + + std::string seq_; + bool use_test_; + + + std::unique_ptr inf_calclator; +}; + +} // namespace hdl_graph_slam + +PLUGINLIB_EXPORT_CLASS(hdl_graph_slam::HdlGraphSlamNodelet, nodelet::Nodelet) diff --git a/apps/prefiltering_nodelet.cpp b/apps/prefiltering_nodelet.cpp new file mode 100644 index 0000000..07bb648 --- /dev/null +++ b/apps/prefiltering_nodelet.cpp @@ -0,0 +1,270 @@ +// SPDX-License-Identifier: BSD-2-Clause + +#include + +#include +#include +#include +#include +#include + +#include +#include + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace hdl_graph_slam { + +class PrefilteringNodelet : public nodelet::Nodelet { +public: + typedef pcl::PointXYZI PointT; + + PrefilteringNodelet() {} + virtual ~PrefilteringNodelet() {} + + virtual void onInit() { + nh = getNodeHandle(); + private_nh = getPrivateNodeHandle(); + + initialize_params(); + + if(private_nh.param("deskewing", false)) { + imu_sub = nh.subscribe("/imu/data", 1, &PrefilteringNodelet::imu_callback, this); + } + + points_sub = nh.subscribe("/velodyne_points", 64, &PrefilteringNodelet::cloud_callback, this); + points_pub = nh.advertise("/filtered_points", 32); + colored_pub = nh.advertise("/colored_points", 32); + } + +private: + void initialize_params() { + std::string downsample_method = private_nh.param("downsample_method", "VOXELGRID"); + double downsample_resolution = private_nh.param("downsample_resolution", 0.1); + + if(downsample_method == "VOXELGRID") { + std::cout << "downsample: VOXELGRID " << downsample_resolution << std::endl; + auto voxelgrid = new pcl::VoxelGrid(); + voxelgrid->setLeafSize(downsample_resolution, downsample_resolution, downsample_resolution); + downsample_filter.reset(voxelgrid); + } else if(downsample_method == "APPROX_VOXELGRID") { + std::cout << "downsample: APPROX_VOXELGRID " << downsample_resolution << std::endl; + pcl::ApproximateVoxelGrid::Ptr approx_voxelgrid(new pcl::ApproximateVoxelGrid()); + approx_voxelgrid->setLeafSize(downsample_resolution, downsample_resolution, downsample_resolution); + downsample_filter = approx_voxelgrid; + } else { + if(downsample_method != "NONE") { + std::cerr << "warning: unknown downsampling type (" << downsample_method << ")" << std::endl; + std::cerr << " : use passthrough filter" << std::endl; + } + std::cout << "downsample: NONE" << std::endl; + } + + std::string outlier_removal_method = private_nh.param("outlier_removal_method", "STATISTICAL"); + if(outlier_removal_method == "STATISTICAL") { + int mean_k = private_nh.param("statistical_mean_k", 20); + double stddev_mul_thresh = private_nh.param("statistical_stddev", 1.0); + std::cout << "outlier_removal: STATISTICAL " << mean_k << " - " << stddev_mul_thresh << std::endl; + + pcl::StatisticalOutlierRemoval::Ptr sor(new pcl::StatisticalOutlierRemoval()); + sor->setMeanK(mean_k); + sor->setStddevMulThresh(stddev_mul_thresh); + outlier_removal_filter = sor; + } else if(outlier_removal_method == "RADIUS") { + double radius = private_nh.param("radius_radius", 0.8); + int min_neighbors = private_nh.param("radius_min_neighbors", 2); + std::cout << "outlier_removal: RADIUS " << radius << " - " << min_neighbors << std::endl; + + pcl::RadiusOutlierRemoval::Ptr rad(new pcl::RadiusOutlierRemoval()); + rad->setRadiusSearch(radius); + rad->setMinNeighborsInRadius(min_neighbors); + outlier_removal_filter = rad; + } else { + std::cout << "outlier_removal: NONE" << std::endl; + } + + use_distance_filter = private_nh.param("use_distance_filter", true); + distance_near_thresh = private_nh.param("distance_near_thresh", 1.0); + distance_far_thresh = private_nh.param("distance_far_thresh", 100.0); + + base_link_frame = private_nh.param("base_link_frame", ""); + } + + void imu_callback(const sensor_msgs::ImuConstPtr& imu_msg) { + imu_queue.push_back(imu_msg); + } + + void cloud_callback(const pcl::PointCloud& src_cloud_r) { + pcl::PointCloud::ConstPtr src_cloud = src_cloud_r.makeShared(); + if(src_cloud->empty()) { + return; + } + src_cloud = deskewing(src_cloud); + + // if base_link_frame is defined, transform the input cloud to the frame + if(!base_link_frame.empty()) { + if(!tf_listener.canTransform(base_link_frame, src_cloud->header.frame_id, ros::Time(0))) { + std::cerr << "failed to find transform between " << base_link_frame << " and " << src_cloud->header.frame_id << std::endl; + } + + tf::StampedTransform transform; + tf_listener.waitForTransform(base_link_frame, src_cloud->header.frame_id, ros::Time(0), ros::Duration(2.0)); + tf_listener.lookupTransform(base_link_frame, src_cloud->header.frame_id, ros::Time(0), transform); + + pcl::PointCloud::Ptr transformed(new pcl::PointCloud()); + pcl_ros::transformPointCloud(*src_cloud, *transformed, transform); + transformed->header.frame_id = base_link_frame; + transformed->header.stamp = src_cloud->header.stamp; + src_cloud = transformed; + } + + pcl::PointCloud::ConstPtr filtered = distance_filter(src_cloud); + filtered = downsample(filtered); + filtered = outlier_removal(filtered); + //std::cout<<"filtered cloud size: "<size()<::ConstPtr downsample(const pcl::PointCloud::ConstPtr& cloud) const { + if(!downsample_filter) { + return cloud; + } + + pcl::PointCloud::Ptr filtered(new pcl::PointCloud()); + downsample_filter->setInputCloud(cloud); + downsample_filter->filter(*filtered); + filtered->header = cloud->header; + + return filtered; + } + + pcl::PointCloud::ConstPtr outlier_removal(const pcl::PointCloud::ConstPtr& cloud) const { + if(!outlier_removal_filter) { + return cloud; + } + + pcl::PointCloud::Ptr filtered(new pcl::PointCloud()); + outlier_removal_filter->setInputCloud(cloud); + outlier_removal_filter->filter(*filtered); + filtered->header = cloud->header; + + return filtered; + } + + pcl::PointCloud::ConstPtr distance_filter(const pcl::PointCloud::ConstPtr& cloud) const { + pcl::PointCloud::Ptr filtered(new pcl::PointCloud()); + filtered->reserve(cloud->size()); + + std::copy_if(cloud->begin(), cloud->end(), std::back_inserter(filtered->points), [&](const PointT& p) { + double d = p.getVector3fMap().norm(); + return d > distance_near_thresh && d < distance_far_thresh; + }); + + filtered->width = filtered->size(); + filtered->height = 1; + filtered->is_dense = false; + + filtered->header = cloud->header; + + return filtered; + } + + pcl::PointCloud::ConstPtr deskewing(const pcl::PointCloud::ConstPtr& cloud) { + ros::Time stamp = pcl_conversions::fromPCL(cloud->header.stamp); + if(imu_queue.empty()) { + return cloud; + } + + // the color encodes the point number in the point sequence + if(colored_pub.getNumSubscribers()) { + pcl::PointCloud::Ptr colored(new pcl::PointCloud()); + colored->header = cloud->header; + colored->is_dense = cloud->is_dense; + colored->width = cloud->width; + colored->height = cloud->height; + colored->resize(cloud->size()); + + for(int i = 0; i < cloud->size(); i++) { + double t = static_cast(i) / cloud->size(); + colored->at(i).getVector4fMap() = cloud->at(i).getVector4fMap(); + colored->at(i).r = 255 * t; + colored->at(i).g = 128; + colored->at(i).b = 255 * (1 - t); + } + colored_pub.publish(*colored); + } + + sensor_msgs::ImuConstPtr imu_msg = imu_queue.front(); + + auto loc = imu_queue.begin(); + for(; loc != imu_queue.end(); loc++) { + imu_msg = (*loc); + if((*loc)->header.stamp > stamp) { + break; + } + } + + imu_queue.erase(imu_queue.begin(), loc); + + Eigen::Vector3f ang_v(imu_msg->angular_velocity.x, imu_msg->angular_velocity.y, imu_msg->angular_velocity.z); + ang_v *= -1; + + pcl::PointCloud::Ptr deskewed(new pcl::PointCloud()); + deskewed->header = cloud->header; + deskewed->is_dense = cloud->is_dense; + deskewed->width = cloud->width; + deskewed->height = cloud->height; + deskewed->resize(cloud->size()); + + double scan_period = private_nh.param("scan_period", 0.1); + for(int i = 0; i < cloud->size(); i++) { + const auto& pt = cloud->at(i); + + // TODO: transform IMU data into the LIDAR frame + double delta_t = scan_period * static_cast(i) / cloud->size(); + Eigen::Quaternionf delta_q(1, delta_t / 2.0 * ang_v[0], delta_t / 2.0 * ang_v[1], delta_t / 2.0 * ang_v[2]); + Eigen::Vector3f pt_ = delta_q.inverse() * pt.getVector3fMap(); + + deskewed->at(i) = cloud->at(i); + deskewed->at(i).getVector3fMap() = pt_; + } + + return deskewed; + } + +private: + ros::NodeHandle nh; + ros::NodeHandle private_nh; + + ros::Subscriber imu_sub; + std::vector imu_queue; + + ros::Subscriber points_sub; + ros::Publisher points_pub; + + ros::Publisher colored_pub; + + tf::TransformListener tf_listener; + + std::string base_link_frame; + + bool use_distance_filter; + double distance_near_thresh; + double distance_far_thresh; + + pcl::Filter::Ptr downsample_filter; + pcl::Filter::Ptr outlier_removal_filter; +}; + +} // namespace hdl_graph_slam + +PLUGINLIB_EXPORT_CLASS(hdl_graph_slam::PrefilteringNodelet, nodelet::Nodelet) diff --git a/apps/scan_matching_odometry_nodelet.cpp b/apps/scan_matching_odometry_nodelet.cpp new file mode 100644 index 0000000..9480d65 --- /dev/null +++ b/apps/scan_matching_odometry_nodelet.cpp @@ -0,0 +1,430 @@ +// SPDX-License-Identifier: BSD-2-Clause + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +#include +#include + +#include +#include +#include + +#include +#include +#include +#include +#include + +namespace hdl_graph_slam { + +class ScanMatchingOdometryNodelet : public nodelet::Nodelet { +public: + FeatureAssociation FA; + typedef pcl::PointXYZI PointT; + EIGEN_MAKE_ALIGNED_OPERATOR_NEW; + + ScanMatchingOdometryNodelet() {} + virtual ~ScanMatchingOdometryNodelet() {} + + virtual void onInit() { + NODELET_DEBUG("initializing scan_matching_odometry_nodelet..."); + nh = getNodeHandle(); + private_nh = getPrivateNodeHandle(); + + initialize_params(); + + if(private_nh.param("enable_imu_frontend", false)) { + msf_pose_sub = nh.subscribe("/msf_core/pose", 1, boost::bind(&ScanMatchingOdometryNodelet::msf_pose_callback, this, _1, false)); + msf_pose_after_update_sub = nh.subscribe("/msf_core/pose_after_update", 1, boost::bind(&ScanMatchingOdometryNodelet::msf_pose_callback, this, _1, true)); + } + + points_sub = nh.subscribe("/filtered_points", 256, &ScanMatchingOdometryNodelet::cloud_callback, this); + read_until_pub = nh.advertise("/scan_matching_odometry/read_until", 32); + odom_pub = nh.advertise("/odom", 32); + trans_pub = nh.advertise("/scan_matching_odometry/transform", 32); + status_pub = private_nh.advertise("/scan_matching_odometry/status", 8); + aligned_points_pub = nh.advertise("/aligned_points", 32); + } + +private: + /** + * @brief initialize parameters + */ + void initialize_params() { + auto& pnh = private_nh; + points_topic = pnh.param("points_topic", "/velodyne_points"); + odom_frame_id = pnh.param("odom_frame_id", "odom"); + robot_odom_frame_id = pnh.param("robot_odom_frame_id", "robot_odom"); + + // The minimum tranlational distance and rotation angle between keyframes. + // If this value is zero, frames are always compared with the previous frame + keyframe_delta_trans = pnh.param("keyframe_delta_trans", 0.25); + keyframe_delta_angle = pnh.param("keyframe_delta_angle", 0.15); + keyframe_delta_time = pnh.param("keyframe_delta_time", 1.0); + + Thre_ = pnh.param("Thre_", 1.0); + Time_ = pnh.param("Time_", 1.0); + std::cout<<"Thre_ and Time_: "<("transform_thresholding", false); + max_acceptable_trans = pnh.param("max_acceptable_trans", 1.0); + max_acceptable_angle = pnh.param("max_acceptable_angle", 1.0); + + // select a downsample method (VOXELGRID, APPROX_VOXELGRID, NONE) + std::string downsample_method = pnh.param("downsample_method", "VOXELGRID"); + double downsample_resolution = pnh.param("downsample_resolution", 0.25); + if(downsample_method == "VOXELGRID") { + std::cout << "downsample: VOXELGRID " << downsample_resolution << std::endl; + auto voxelgrid = new pcl::VoxelGrid(); + voxelgrid->setLeafSize(downsample_resolution, downsample_resolution, downsample_resolution); + downsample_filter.reset(voxelgrid); + } else if(downsample_method == "APPROX_VOXELGRID") { + std::cout << "downsample: APPROX_VOXELGRID " << downsample_resolution << std::endl; + pcl::ApproximateVoxelGrid::Ptr approx_voxelgrid(new pcl::ApproximateVoxelGrid()); + approx_voxelgrid->setLeafSize(downsample_resolution, downsample_resolution, downsample_resolution); + downsample_filter = approx_voxelgrid; + } else { + if(downsample_method != "NONE") { + std::cerr << "warning: unknown downsampling type (" << downsample_method << ")" << std::endl; + std::cerr << " : use passthrough filter" << std::endl; + } + std::cout << "downsample: NONE" << std::endl; + pcl::PassThrough::Ptr passthrough(new pcl::PassThrough()); + downsample_filter = passthrough; + } + registration = select_registration_method(pnh); + + + } + + /** + * @brief callback for point clouds + * @param cloud_msg point cloud msg + */ + void cloud_callback(const sensor_msgs::PointCloud2ConstPtr& cloud_msg) { + if(!ros::ok()) { + return; + } + + pcl::PointCloud::Ptr cloud(new pcl::PointCloud()); + pcl::fromROSMsg(*cloud_msg, *cloud); + + Eigen::Matrix4f pose = matching(cloud_msg->header.stamp, cloud); + publish_odometry(cloud_msg->header.stamp, cloud_msg->header.frame_id, pose); + + // In offline estimation, point clouds until the published time will be supplied + std_msgs::HeaderPtr read_until(new std_msgs::Header()); + read_until->frame_id = points_topic; + read_until->stamp = cloud_msg->header.stamp + ros::Duration(1, 0); + read_until_pub.publish(read_until); + + read_until->frame_id = "/filtered_points"; + read_until_pub.publish(read_until); + } + + void msf_pose_callback(const geometry_msgs::PoseWithCovarianceStampedConstPtr& pose_msg, bool after_update) { + if(after_update) { + msf_pose_after_update = pose_msg; + } else { + msf_pose = pose_msg; + } + } + + /** + * @brief downsample a point cloud + * @param cloud input cloud + * @return downsampled point cloud + */ + pcl::PointCloud::ConstPtr downsample(const pcl::PointCloud::ConstPtr& cloud) const { + if(!downsample_filter) { + return cloud; + } + + pcl::PointCloud::Ptr filtered(new pcl::PointCloud()); + downsample_filter->setInputCloud(cloud); + downsample_filter->filter(*filtered); + + return filtered; + } + + /** + * @brief estimate the relative pose between an input cloud and a keyframe cloud + * @param stamp the timestamp of the input cloud + * @param cloud the input cloud + * @return the relative pose between the input cloud and the keyframe cloud + */ + Eigen::Matrix4f matching(const ros::Time& stamp, const pcl::PointCloud::ConstPtr& cloud) { + if(!keyframe) { + prev_time = ros::Time(); + prev_trans.setIdentity(); + prev_trans0_.setIdentity(); + keyframe_pose.setIdentity(); + keyframe_stamp = stamp; +// + keyframe = downsample(cloud); + registration->setInputTarget(keyframe); + Accumulated_trans_.setIdentity(); + FA.setInitTargetCloud(cloud); + last_frame_ = cloud; + return Eigen::Matrix4f::Identity(); + } + frames_num_++; + Eigen::Matrix4f trans0; + clock_t start_s = clock(); + FA.setSourceCloud(cloud); + clock_t end_s = clock(); + std::cout<<"setSourceCloud "<<(double)(end_s-start_s)/CLOCKS_PER_SEC<("enable_imu_frontend", false)) { + if(msf_pose && msf_pose->header.stamp > keyframe_stamp && msf_pose_after_update && msf_pose_after_update->header.stamp > keyframe_stamp) { + Eigen::Isometry3d pose0 = pose2isometry(msf_pose_after_update->pose.pose); + Eigen::Isometry3d pose1 = pose2isometry(msf_pose->pose.pose); + Eigen::Isometry3d delta = pose0.inverse() * pose1; + msf_source = "imu"; + msf_delta = delta.cast(); + } else { + std::cerr << "msf data is too old" << std::endl; + } + } else if(private_nh.param("enable_robot_odometry_init_guess", false) && !prev_time.isZero()) { + tf::StampedTransform transform; + if(tf_listener.waitForTransform(cloud->header.frame_id, stamp, cloud->header.frame_id, prev_time, robot_odom_frame_id, ros::Duration(0))) { + tf_listener.lookupTransform(cloud->header.frame_id, stamp, cloud->header.frame_id, prev_time, robot_odom_frame_id, transform); + } else if(tf_listener.waitForTransform(cloud->header.frame_id, ros::Time(0), cloud->header.frame_id, prev_time, robot_odom_frame_id, ros::Duration(0))) { + tf_listener.lookupTransform(cloud->header.frame_id, ros::Time(0), cloud->header.frame_id, prev_time, robot_odom_frame_id, transform); + } + + if(transform.stamp_.isZero()) { + NODELET_WARN_STREAM("failed to look up transform between " << cloud->header.frame_id << " and " << robot_odom_frame_id); + } else { + msf_source = "odometry"; + msf_delta = tf2isometry(transform).cast(); + } + } + + //publish_scan_matching_status(stamp, cloud->header.frame_id, aligned, msf_source, msf_delta); + trans0 = FA.getFinalTransformation(); + //std::cout<<"trans0: "<(0, 3).norm(); + double da = std::acos(Eigen::Quaternionf(delta.block<3, 3>(0, 0)).w()); + //if uniform motion is not satisfied, will not accept this result. + if(dx > max_acceptable_trans || da > max_acceptable_angle) { + NODELET_INFO_STREAM("too large transform!! " << dx << "[m] " << da << "[rad]"); + NODELET_INFO_STREAM("ignore this frame(" << stamp << ")"); + return keyframe_pose * prev_trans; + } + } + prev_trans0_ = trans0; + prev_time = stamp; + prev_trans = trans; + + auto keyframe_trans = matrix2transform(stamp, keyframe_pose, odom_frame_id, "keyframe"); + keyframe_broadcaster.sendTransform(keyframe_trans); + + double delta_trans = trans.block<3, 1>(0, 3).norm(); + double delta_angle = std::acos(Eigen::Quaternionf(trans.block<3, 3>(0, 0)).w()); + double delta_time = (stamp - keyframe_stamp).toSec(); + pcl::PointCloud::Ptr aligned(new pcl::PointCloud()); + // + std::cout<<"delta_trans and time: "< keyframe_delta_trans || delta_angle > keyframe_delta_angle || delta_time > keyframe_delta_time) { + registration->setInputSource(filtered); + clock_t start = clock(); + registration->align(*aligned, prev_trans * msf_delta.matrix()); + clock_t end = clock(); + std::cout<<"GICP running speed "<<(double)(end-start)/CLOCKS_PER_SEC<hasConverged()) { + NODELET_INFO_STREAM("scan matching has not converged!!"); + NODELET_INFO_STREAM("ignore this frame(" << stamp << ")"); + return keyframe_pose * prev_trans; + } + keyframe = filtered; + registration->setInputTarget(keyframe); + Accumulated_trans_.setIdentity(); + odom = keyframe_pose * registration->getFinalTransformation(); + keyframe_pose = odom; + keyframe_stamp = stamp; + prev_time = stamp; + prev_trans.setIdentity(); + }} + + if (aligned_points_pub.getNumSubscribers() > 0) + { + pcl::transformPointCloud (*cloud, *aligned, odom); + aligned->header.frame_id=odom_frame_id; + aligned_points_pub.publish(*aligned); + } + return odom; + } + + /** + * @brief publish odometry + * @param stamp timestamp + * @param pose odometry pose to be published + */ + void publish_odometry(const ros::Time& stamp, const std::string& base_frame_id, const Eigen::Matrix4f& pose) { + // publish transform stamped for IMU integration + geometry_msgs::TransformStamped odom_trans = matrix2transform(stamp, pose, odom_frame_id, base_frame_id); + trans_pub.publish(odom_trans); + + // broadcast the transform over tf + odom_broadcaster.sendTransform(odom_trans); + + // publish the transform + nav_msgs::Odometry odom; + odom.header.stamp = stamp; + odom.header.frame_id = odom_frame_id; + + odom.pose.pose.position.x = pose(0, 3); + odom.pose.pose.position.y = pose(1, 3); + odom.pose.pose.position.z = pose(2, 3); + odom.pose.pose.orientation = odom_trans.transform.rotation; + + odom.child_frame_id = base_frame_id; + odom.twist.twist.linear.x = 0.0; + odom.twist.twist.linear.y = 0.0; + odom.twist.twist.angular.z = 0.0; + + odom_pub.publish(odom); + } + + /** + * @brief publish scan matching status + */ + void publish_scan_matching_status(const ros::Time& stamp, const std::string& frame_id, pcl::PointCloud::ConstPtr aligned, const std::string& msf_source, const Eigen::Isometry3f& msf_delta) { + if(!status_pub.getNumSubscribers()) { + return; + } + + ScanMatchingStatus status; + status.header.frame_id = frame_id; + status.header.stamp = stamp; + status.has_converged = registration->hasConverged(); + status.matching_error = registration->getFitnessScore(); + + const double max_correspondence_dist = 0.5; + + int num_inliers = 0; + std::vector k_indices; + std::vector k_sq_dists; + for(int i=0; isize(); i++) { + const auto& pt = aligned->at(i); + registration->getSearchMethodTarget()->nearestKSearch(pt, 1, k_indices, k_sq_dists); + if(k_sq_dists[0] < max_correspondence_dist * max_correspondence_dist) { + num_inliers++; + } + } + status.inlier_fraction = static_cast(num_inliers) / aligned->size(); + + status.relative_pose = isometry2pose(Eigen::Isometry3f(registration->getFinalTransformation()).cast()); + + if(!msf_source.empty()) { + status.prediction_labels.resize(1); + status.prediction_labels[0].data = msf_source; + + status.prediction_errors.resize(1); + Eigen::Isometry3f error = Eigen::Isometry3f(registration->getFinalTransformation()).inverse() * msf_delta; + status.prediction_errors[0] = isometry2pose(error.cast()); + } + + status_pub.publish(status); + } + +private: + // ROS topics + ros::NodeHandle nh; + ros::NodeHandle private_nh; + + ros::Subscriber points_sub; + ros::Subscriber msf_pose_sub; + ros::Subscriber msf_pose_after_update_sub; + + ros::Publisher odom_pub; + ros::Publisher trans_pub; + ros::Publisher aligned_points_pub; + ros::Publisher status_pub; + tf::TransformListener tf_listener; + tf::TransformBroadcaster odom_broadcaster; + tf::TransformBroadcaster keyframe_broadcaster; + + std::string points_topic; + std::string odom_frame_id; + std::string robot_odom_frame_id; + ros::Publisher read_until_pub; + + // keyframe parameters + double keyframe_delta_trans; // minimum distance between keyframes + double keyframe_delta_angle; // + double keyframe_delta_time; // + + // registration validation by thresholding + bool transform_thresholding; // + double max_acceptable_trans; // + double max_acceptable_angle; + + // odometry calculation + geometry_msgs::PoseWithCovarianceStampedConstPtr msf_pose; + geometry_msgs::PoseWithCovarianceStampedConstPtr msf_pose_after_update; + + ros::Time prev_time; + Eigen::Matrix4f prev_trans; // previous estimated transform from keyframe + Eigen::Matrix4f keyframe_pose; // keyframe pose + ros::Time keyframe_stamp; // keyframe time + pcl::PointCloud::ConstPtr keyframe; // keyframe point cloud + pcl::PointCloud::ConstPtr last_frame_; // keyframe point cloud + Eigen::Matrix4f Accumulated_trans_; + Eigen::Matrix4f prev_trans0_; //used for save the scan-to-scan matching + pcl::PointCloud Accumulated_frames_; + int Accumulated_no_; + int frames_num_; + + double Time_; + double Thre_; + + // + pcl::Filter::Ptr downsample_filter; + pcl::Registration::Ptr registration; +}; + +} // namespace hdl_graph_slam + +PLUGINLIB_EXPORT_CLASS(hdl_graph_slam::ScanMatchingOdometryNodelet, nodelet::Nodelet) diff --git a/apps/scan_matching_odometry_nodelet_raw.cpp b/apps/scan_matching_odometry_nodelet_raw.cpp new file mode 100644 index 0000000..fb95159 --- /dev/null +++ b/apps/scan_matching_odometry_nodelet_raw.cpp @@ -0,0 +1,399 @@ +// SPDX-License-Identifier: BSD-2-Clause + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +#include +#include + +#include +#include +#include + +#include +#include +#include + +namespace hdl_graph_slam { + +class ScanMatchingOdometryNodelet : public nodelet::Nodelet { +public: + typedef pcl::PointXYZI PointT; + EIGEN_MAKE_ALIGNED_OPERATOR_NEW + + ScanMatchingOdometryNodelet() {} + virtual ~ScanMatchingOdometryNodelet() {} + + virtual void onInit() { + NODELET_DEBUG("initializing scan_matching_odometry_nodelet..."); + nh = getNodeHandle(); + private_nh = getPrivateNodeHandle(); + + initialize_params(); + + if(private_nh.param("enable_imu_frontend", false)) { + msf_pose_sub = nh.subscribe("/msf_core/pose", 1, boost::bind(&ScanMatchingOdometryNodelet::msf_pose_callback, this, _1, false)); + msf_pose_after_update_sub = nh.subscribe("/msf_core/pose_after_update", 1, boost::bind(&ScanMatchingOdometryNodelet::msf_pose_callback, this, _1, true)); + } + + points_sub = nh.subscribe("/filtered_points", 256, &ScanMatchingOdometryNodelet::cloud_callback, this); + read_until_pub = nh.advertise("/scan_matching_odometry/read_until", 32); + odom_pub = nh.advertise("/odom", 32); + trans_pub = nh.advertise("/scan_matching_odometry/transform", 32); + status_pub = private_nh.advertise("/scan_matching_odometry/status", 8); + aligned_points_pub = nh.advertise("/aligned_points", 32); + } + +private: + /** + * @brief initialize parameters + */ + void initialize_params() { + auto& pnh = private_nh; + points_topic = pnh.param("points_topic", "/velodyne_points"); + odom_frame_id = pnh.param("odom_frame_id", "odom"); + robot_odom_frame_id = pnh.param("robot_odom_frame_id", "robot_odom"); + + // The minimum tranlational distance and rotation angle between keyframes. + // If this value is zero, frames are always compared with the previous frame + keyframe_delta_trans = pnh.param("keyframe_delta_trans", 0.25); + keyframe_delta_angle = pnh.param("keyframe_delta_angle", 0.15); + keyframe_delta_time = pnh.param("keyframe_delta_time", 1.0); + + // Registration validation by thresholding + transform_thresholding = pnh.param("transform_thresholding", false); + max_acceptable_trans = pnh.param("max_acceptable_trans", 1.0); + max_acceptable_angle = pnh.param("max_acceptable_angle", 1.0); + + // select a downsample method (VOXELGRID, APPROX_VOXELGRID, NONE) + std::string downsample_method = pnh.param("downsample_method", "VOXELGRID"); + double downsample_resolution = pnh.param("downsample_resolution", 0.1); + if(downsample_method == "VOXELGRID") { + std::cout << "downsample: VOXELGRID " << downsample_resolution << std::endl; + auto voxelgrid = new pcl::VoxelGrid(); + voxelgrid->setLeafSize(downsample_resolution, downsample_resolution, downsample_resolution); + downsample_filter.reset(voxelgrid); + } else if(downsample_method == "APPROX_VOXELGRID") { + std::cout << "downsample: APPROX_VOXELGRID " << downsample_resolution << std::endl; + pcl::ApproximateVoxelGrid::Ptr approx_voxelgrid(new pcl::ApproximateVoxelGrid()); + approx_voxelgrid->setLeafSize(downsample_resolution, downsample_resolution, downsample_resolution); + downsample_filter = approx_voxelgrid; + } else { + if(downsample_method != "NONE") { + std::cerr << "warning: unknown downsampling type (" << downsample_method << ")" << std::endl; + std::cerr << " : use passthrough filter" << std::endl; + } + std::cout << "downsample: NONE" << std::endl; + pcl::PassThrough::Ptr passthrough(new pcl::PassThrough()); + downsample_filter = passthrough; + } + + registration = select_registration_method(pnh); + } + + /** + * @brief callback for point clouds + * @param cloud_msg point cloud msg + */ + void cloud_callback(const sensor_msgs::PointCloud2ConstPtr& cloud_msg) { + if(!ros::ok()) { + return; + } + + pcl::PointCloud::Ptr cloud(new pcl::PointCloud()); + pcl::fromROSMsg(*cloud_msg, *cloud); + clock_t start = clock(); + Eigen::Matrix4f pose = matching(cloud_msg->header.stamp, cloud); + clock_t end = clock(); + std::cout<<"matching time cost: "<<(double)(end-start)/CLOCKS_PER_SEC<header.stamp, cloud_msg->header.frame_id, pose); + + // In offline estimation, point clouds until the published time will be supplied + std_msgs::HeaderPtr read_until(new std_msgs::Header()); + read_until->frame_id = points_topic; + read_until->stamp = cloud_msg->header.stamp + ros::Duration(1, 0); + read_until_pub.publish(read_until); + + read_until->frame_id = "/filtered_points"; + read_until_pub.publish(read_until); + } + + void msf_pose_callback(const geometry_msgs::PoseWithCovarianceStampedConstPtr& pose_msg, bool after_update) { + if(after_update) { + msf_pose_after_update = pose_msg; + } else { + msf_pose = pose_msg; + } + } + + /** + * @brief downsample a point cloud + * @param cloud input cloud + * @return downsampled point cloud + */ + pcl::PointCloud::ConstPtr downsample(const pcl::PointCloud::ConstPtr& cloud) const { + if(!downsample_filter) { + return cloud; + } + + pcl::PointCloud::Ptr filtered(new pcl::PointCloud()); + downsample_filter->setInputCloud(cloud); + downsample_filter->filter(*filtered); + + return filtered; + } + + /** + * @brief estimate the relative pose between an input cloud and a keyframe cloud + * @param stamp the timestamp of the input cloud + * @param cloud the input cloud + * @return the relative pose between the input cloud and the keyframe cloud + */ + Eigen::Matrix4f matching(const ros::Time& stamp, const pcl::PointCloud::ConstPtr& cloud) { + if(!keyframe) { + prev_time = ros::Time(); + prev_trans.setIdentity(); + keyframe_pose.setIdentity(); + keyframe_stamp = stamp; + keyframe = downsample(cloud); + registration->setInputTarget(keyframe); + frame_num_ = 0; + return Eigen::Matrix4f::Identity(); + } +// + auto filtered = downsample(cloud); + registration->setInputSource(filtered); + + std::string msf_source; + Eigen::Isometry3f msf_delta = Eigen::Isometry3f::Identity(); + + if(private_nh.param("enable_imu_frontend", false)) { + if(msf_pose && msf_pose->header.stamp > keyframe_stamp && msf_pose_after_update && msf_pose_after_update->header.stamp > keyframe_stamp) { + Eigen::Isometry3d pose0 = pose2isometry(msf_pose_after_update->pose.pose); + Eigen::Isometry3d pose1 = pose2isometry(msf_pose->pose.pose); + Eigen::Isometry3d delta = pose0.inverse() * pose1; + + msf_source = "imu"; + msf_delta = delta.cast(); + } else { + std::cerr << "msf data is too old" << std::endl; + } + } else if(private_nh.param("enable_robot_odometry_init_guess", false) && !prev_time.isZero()) { + tf::StampedTransform transform; + if(tf_listener.waitForTransform(cloud->header.frame_id, stamp, cloud->header.frame_id, prev_time, robot_odom_frame_id, ros::Duration(0))) { + tf_listener.lookupTransform(cloud->header.frame_id, stamp, cloud->header.frame_id, prev_time, robot_odom_frame_id, transform); + } else if(tf_listener.waitForTransform(cloud->header.frame_id, ros::Time(0), cloud->header.frame_id, prev_time, robot_odom_frame_id, ros::Duration(0))) { + tf_listener.lookupTransform(cloud->header.frame_id, ros::Time(0), cloud->header.frame_id, prev_time, robot_odom_frame_id, transform); + } + + if(transform.stamp_.isZero()) { + NODELET_WARN_STREAM("failed to look up transform between " << cloud->header.frame_id << " and " << robot_odom_frame_id); + } else { + msf_source = "odometry"; + msf_delta = tf2isometry(transform).cast(); + } + } + + pcl::PointCloud::Ptr aligned(new pcl::PointCloud()); + clock_t t0 = clock(); + registration->align(*aligned, prev_trans * msf_delta.matrix()); + clock_t t1 = clock(); + std::cout<<"align time: "<<(double)(t1-t0)/CLOCKS_PER_SEC<header.frame_id, aligned, msf_source, msf_delta); + clock_t t2 = clock(); + std::cout<<"publish_scan_matching_status time: "<<(double)(t2-t1)/CLOCKS_PER_SEC<hasConverged()) { + NODELET_INFO_STREAM("scan matching has not converged!!"); + NODELET_INFO_STREAM("ignore this frame(" << stamp << ")"); + return keyframe_pose * prev_trans; + } + + Eigen::Matrix4f trans = registration->getFinalTransformation(); + + //std::cout<<"trans: "<(0, 3).norm(); + double da = std::acos(Eigen::Quaternionf(delta.block<3, 3>(0, 0)).w()); + + if(dx > max_acceptable_trans || da > max_acceptable_angle) { + NODELET_INFO_STREAM("too large transform!! " << dx << "[m] " << da << "[rad]"); + NODELET_INFO_STREAM("ignore this frame(" << stamp << ")"); + return keyframe_pose * prev_trans; + } + } + + prev_time = stamp; + prev_trans = trans; + + auto keyframe_trans = matrix2transform(stamp, keyframe_pose, odom_frame_id, "keyframe"); + keyframe_broadcaster.sendTransform(keyframe_trans); + + double delta_trans = trans.block<3, 1>(0, 3).norm(); + double delta_angle = std::acos(Eigen::Quaternionf(trans.block<3, 3>(0, 0)).w()); + double delta_time = (stamp - keyframe_stamp).toSec(); + //std::cout<<"delta_time: "< keyframe_delta_trans || delta_angle > keyframe_delta_angle || delta_time > keyframe_delta_time) { + keyframe = filtered; + registration->setInputTarget(keyframe); + //use fastgicp + keyframe_pose = odom; + keyframe_stamp = stamp; + prev_time = stamp; + prev_trans.setIdentity(); + } + + if (aligned_points_pub.getNumSubscribers() > 0) + { + pcl::transformPointCloud(*cloud, *aligned, odom); + aligned->header.frame_id=odom_frame_id; + aligned_points_pub.publish(*aligned); + } + frame_num_++; + //if(frame_num_>100) + // sleep(1000); + + return odom; + } + + /** + * @brief publish odometry + * @param stamp timestamp + * @param pose odometry pose to be published + */ + void publish_odometry(const ros::Time& stamp, const std::string& base_frame_id, const Eigen::Matrix4f& pose) { + // publish transform stamped for IMU integration + geometry_msgs::TransformStamped odom_trans = matrix2transform(stamp, pose, odom_frame_id, base_frame_id); + trans_pub.publish(odom_trans); + + // broadcast the transform over tf + odom_broadcaster.sendTransform(odom_trans); + + // publish the transform + nav_msgs::Odometry odom; + odom.header.stamp = stamp; + odom.header.frame_id = odom_frame_id; + + odom.pose.pose.position.x = pose(0, 3); + odom.pose.pose.position.y = pose(1, 3); + odom.pose.pose.position.z = pose(2, 3); + odom.pose.pose.orientation = odom_trans.transform.rotation; + + odom.child_frame_id = base_frame_id; + odom.twist.twist.linear.x = 0.0; + odom.twist.twist.linear.y = 0.0; + odom.twist.twist.angular.z = 0.0; + + odom_pub.publish(odom); + } + + /** + * @brief publish scan matching status + */ + void publish_scan_matching_status(const ros::Time& stamp, const std::string& frame_id, pcl::PointCloud::ConstPtr aligned, const std::string& msf_source, const Eigen::Isometry3f& msf_delta) { + if(!status_pub.getNumSubscribers()) { + return; + } + + ScanMatchingStatus status; + status.header.frame_id = frame_id; + status.header.stamp = stamp; + status.has_converged = registration->hasConverged(); + status.matching_error = registration->getFitnessScore(); + + const double max_correspondence_dist = 0.5; + + int num_inliers = 0; + std::vector k_indices; + std::vector k_sq_dists; + for(int i=0; isize(); i++) { + const auto& pt = aligned->at(i); + registration->getSearchMethodTarget()->nearestKSearch(pt, 1, k_indices, k_sq_dists); + if(k_sq_dists[0] < max_correspondence_dist * max_correspondence_dist) { + num_inliers++; + } + } + status.inlier_fraction = static_cast(num_inliers) / aligned->size(); + + status.relative_pose = isometry2pose(Eigen::Isometry3f(registration->getFinalTransformation()).cast()); + + if(!msf_source.empty()) { + status.prediction_labels.resize(1); + status.prediction_labels[0].data = msf_source; + + status.prediction_errors.resize(1); + Eigen::Isometry3f error = Eigen::Isometry3f(registration->getFinalTransformation()).inverse() * msf_delta; + status.prediction_errors[0] = isometry2pose(error.cast()); + } + + status_pub.publish(status); + } + +private: + // ROS topics + ros::NodeHandle nh; + ros::NodeHandle private_nh; + + ros::Subscriber points_sub; + ros::Subscriber msf_pose_sub; + ros::Subscriber msf_pose_after_update_sub; + + ros::Publisher odom_pub; + ros::Publisher trans_pub; + ros::Publisher aligned_points_pub; + ros::Publisher status_pub; + tf::TransformListener tf_listener; + tf::TransformBroadcaster odom_broadcaster; + tf::TransformBroadcaster keyframe_broadcaster; + + std::string points_topic; + std::string odom_frame_id; + std::string robot_odom_frame_id; + ros::Publisher read_until_pub; + + // keyframe parameters + double keyframe_delta_trans; // minimum distance between keyframes + double keyframe_delta_angle; // + double keyframe_delta_time; // + + // registration validation by thresholding + bool transform_thresholding; // + double max_acceptable_trans; // + double max_acceptable_angle; + + // odometry calculation + geometry_msgs::PoseWithCovarianceStampedConstPtr msf_pose; + geometry_msgs::PoseWithCovarianceStampedConstPtr msf_pose_after_update; + + ros::Time prev_time; + Eigen::Matrix4f prev_trans; // previous estimated transform from keyframe + Eigen::Matrix4f keyframe_pose; // keyframe pose + ros::Time keyframe_stamp; // keyframe time + pcl::PointCloud::ConstPtr keyframe; // keyframe point cloud + int frame_num_; + + // + pcl::Filter::Ptr downsample_filter; + pcl::Registration::Ptr registration; +}; + +} // namespace hdl_graph_slam + +PLUGINLIB_EXPORT_CLASS(hdl_graph_slam::ScanMatchingOdometryNodelet, nodelet::Nodelet) diff --git a/cmake/FindG2O.cmake b/cmake/FindG2O.cmake new file mode 100644 index 0000000..a2438ca --- /dev/null +++ b/cmake/FindG2O.cmake @@ -0,0 +1,117 @@ +# Find the header files + +FIND_PATH(G2O_INCLUDE_DIR g2o/core/base_vertex.h + ${G2O_ROOT}/include + $ENV{G2O_ROOT}/include + $ENV{G2O_ROOT} + /usr/local/include + /usr/include + /opt/local/include + /sw/local/include + /sw/include + /opt/ros/$ENV{ROS_DISTRO}/include + NO_DEFAULT_PATH + ) + +# Macro to unify finding both the debug and release versions of the +# libraries; this is adapted from the OpenSceneGraph FIND_LIBRARY +# macro. + +MACRO(FIND_G2O_LIBRARY MYLIBRARY MYLIBRARYNAME) + + FIND_LIBRARY("${MYLIBRARY}_DEBUG" + NAMES "g2o_${MYLIBRARYNAME}_d" + PATHS + ${G2O_ROOT}/lib/Debug + ${G2O_ROOT}/lib + $ENV{G2O_ROOT}/lib/Debug + $ENV{G2O_ROOT}/lib + /opt/ros/$ENV{ROS_DISTRO}/lib + NO_DEFAULT_PATH + ) + + FIND_LIBRARY("${MYLIBRARY}_DEBUG" + NAMES "g2o_${MYLIBRARYNAME}_d" + PATHS + ~/Library/Frameworks + /Library/Frameworks + /usr/local/lib + /usr/local/lib64 + /usr/lib + /usr/lib64 + /opt/local/lib + /sw/local/lib + /sw/lib + ) + + FIND_LIBRARY(${MYLIBRARY} + NAMES "g2o_${MYLIBRARYNAME}" + PATHS + ${G2O_ROOT}/lib/Release + ${G2O_ROOT}/lib + $ENV{G2O_ROOT}/lib/Release + $ENV{G2O_ROOT}/lib + /opt/ros/$ENV{ROS_DISTRO}/lib + NO_DEFAULT_PATH + ) + + FIND_LIBRARY(${MYLIBRARY} + NAMES "g2o_${MYLIBRARYNAME}" + PATHS + ~/Library/Frameworks + /Library/Frameworks + /usr/local/lib + /usr/local/lib64 + /usr/lib + /usr/lib64 + /opt/local/lib + /sw/local/lib + /sw/lib + ) + + IF(NOT ${MYLIBRARY}_DEBUG) + IF(MYLIBRARY) + SET(${MYLIBRARY}_DEBUG ${MYLIBRARY}) + ENDIF(MYLIBRARY) + ENDIF( NOT ${MYLIBRARY}_DEBUG) + +ENDMACRO(FIND_G2O_LIBRARY LIBRARY LIBRARYNAME) + +# Find the core elements +FIND_G2O_LIBRARY(G2O_STUFF_LIBRARY stuff) +FIND_G2O_LIBRARY(G2O_CORE_LIBRARY core) + +# Find the CLI library +FIND_G2O_LIBRARY(G2O_CLI_LIBRARY cli) + +# Find the pluggable solvers +FIND_G2O_LIBRARY(G2O_SOLVER_CHOLMOD solver_cholmod) +FIND_G2O_LIBRARY(G2O_SOLVER_CSPARSE solver_csparse) +FIND_G2O_LIBRARY(G2O_SOLVER_CSPARSE_EXTENSION csparse_extension) +FIND_G2O_LIBRARY(G2O_SOLVER_DENSE solver_dense) +FIND_G2O_LIBRARY(G2O_SOLVER_PCG solver_pcg) +FIND_G2O_LIBRARY(G2O_SOLVER_SLAM2D_LINEAR solver_slam2d_linear) +FIND_G2O_LIBRARY(G2O_SOLVER_STRUCTURE_ONLY solver_structure_only) +FIND_G2O_LIBRARY(G2O_SOLVER_EIGEN solver_eigen) + +# Find the predefined types +FIND_G2O_LIBRARY(G2O_TYPES_DATA types_data) +FIND_G2O_LIBRARY(G2O_TYPES_ICP types_icp) +FIND_G2O_LIBRARY(G2O_TYPES_SBA types_sba) +FIND_G2O_LIBRARY(G2O_TYPES_SCLAM2D types_sclam2d) +FIND_G2O_LIBRARY(G2O_TYPES_SIM3 types_sim3) +FIND_G2O_LIBRARY(G2O_TYPES_SLAM2D types_slam2d) +FIND_G2O_LIBRARY(G2O_TYPES_SLAM3D types_slam3d) +FIND_G2O_LIBRARY(G2O_TYPES_SLAM3D_ADDONS types_slam3d_addons) + +# G2O solvers declared found if we found at least one solver +SET(G2O_SOLVERS_FOUND "NO") +IF(G2O_SOLVER_CHOLMOD OR G2O_SOLVER_CSPARSE OR G2O_SOLVER_DENSE OR G2O_SOLVER_PCG OR G2O_SOLVER_SLAM2D_LINEAR OR G2O_SOLVER_STRUCTURE_ONLY OR G2O_SOLVER_EIGEN) + SET(G2O_SOLVERS_FOUND "YES") +ENDIF(G2O_SOLVER_CHOLMOD OR G2O_SOLVER_CSPARSE OR G2O_SOLVER_DENSE OR G2O_SOLVER_PCG OR G2O_SOLVER_SLAM2D_LINEAR OR G2O_SOLVER_STRUCTURE_ONLY OR G2O_SOLVER_EIGEN) + +# G2O itself declared found if we found the core libraries and at least one solver +SET(G2O_FOUND "NO") +IF(G2O_STUFF_LIBRARY AND G2O_CORE_LIBRARY AND G2O_INCLUDE_DIR AND G2O_SOLVERS_FOUND) + SET(G2O_FOUND "YES") +ENDIF(G2O_STUFF_LIBRARY AND G2O_CORE_LIBRARY AND G2O_INCLUDE_DIR AND G2O_SOLVERS_FOUND) diff --git a/docker/build.sh b/docker/build.sh new file mode 100755 index 0000000..17fb7a6 --- /dev/null +++ b/docker/build.sh @@ -0,0 +1,2 @@ +#!/bin/bash +docker build --tag hdl_graph_slam -f noetic/Dockerfile .. diff --git a/docker/howtouse.md b/docker/howtouse.md new file mode 100644 index 0000000..fb5a373 --- /dev/null +++ b/docker/howtouse.md @@ -0,0 +1,40 @@ +# hdl_graph_slam + +Original repository: https://github.com/koide3/hdl_graph_slam + + +## Build +```bash +cd hdl_graph_slam/docker +./build.sh +``` + +## Run + +### On host: +```bash +roscore +``` + +```bash +rosparam set use_sim_time true + +cd hdl_graph_slam/rviz +rviz -d hdl_graph_slam.rviz +``` + +```bash +rosbag play --clock hdl_400.bag +``` +http://www.aisl.cs.tut.ac.jp/databases/hdl_graph_slam/hdl_400.bag.tar.gz + +### On docker image: +```bash +cd hdl_graph_slam/docker +./run.sh + +roslaunch hdl_graph_slam hdl_graph_slam_400.launch +``` + + +![hdl_graph_slam](https://user-images.githubusercontent.com/31344317/98347836-4fed5a00-205b-11eb-931c-158f6cd056bf.gif) diff --git a/docker/kinetic/Dockerfile b/docker/kinetic/Dockerfile new file mode 100644 index 0000000..5331b1c --- /dev/null +++ b/docker/kinetic/Dockerfile @@ -0,0 +1,25 @@ +FROM ros:kinetic + +RUN apt-get update && apt-get install --no-install-recommends -y && apt-get install --no-install-recommends -y wget nano build-essential ros-kinetic-geodesy ros-kinetic-pcl-ros ros-kinetic-nmea-msgs ros-kinetic-rviz ros-kinetic-tf-conversions ros-kinetic-libg2o \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + + +RUN mkdir -p /root/catkin_ws/src +WORKDIR /root/catkin_ws/src +RUN /bin/bash -c '. /opt/ros/kinetic/setup.bash; catkin_init_workspace' +RUN git clone https://github.com/koide3/ndt_omp.git +RUN git clone https://github.com/SMRT-AIST/fast_gicp.git --recursive + +COPY . /root/catkin_ws/src/hdl_graph_slam/ + +WORKDIR /root/catkin_ws +RUN /bin/bash -c '. /opt/ros/kinetic/setup.bash; catkin_make' +RUN sed -i "6i source \"/root/catkin_ws/devel/setup.bash\"" /ros_entrypoint.sh + +RUN apt-get clean && rm -rf /var/lib/apt/lists/* + +WORKDIR / + +ENTRYPOINT ["/ros_entrypoint.sh"] +CMD ["bash"] diff --git a/docker/kinetic_llvm/Dockerfile b/docker/kinetic_llvm/Dockerfile new file mode 100644 index 0000000..e16aa86 --- /dev/null +++ b/docker/kinetic_llvm/Dockerfile @@ -0,0 +1,30 @@ +FROM ros:kinetic + +RUN apt-get update && apt-get install --no-install-recommends -y \ + && apt-get install --no-install-recommends -y \ + wget nano build-essential libomp-dev clang lld-6.0 \ + ros-kinetic-geodesy ros-kinetic-pcl-ros ros-kinetic-nmea-msgs \ + ros-kinetic-rviz ros-kinetic-tf-conversions ros-kinetic-libg2o \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +RUN update-alternatives --install /usr/bin/ld ld /usr/bin/ld.lld-6.0 50 + +RUN mkdir -p /root/catkin_ws/src +WORKDIR /root/catkin_ws/src +RUN /bin/bash -c '. /opt/ros/kinetic/setup.bash; catkin_init_workspace' +RUN git clone https://github.com/koide3/ndt_omp.git +RUN git clone https://github.com/SMRT-AIST/fast_gicp.git --recursive + +COPY . /root/catkin_ws/src/hdl_graph_slam/ + +WORKDIR /root/catkin_ws +RUN /bin/bash -c '. /opt/ros/kinetic/setup.bash; CC=clang CXX=clang++ catkin_make' +RUN sed -i "6i source \"/root/catkin_ws/devel/setup.bash\"" /ros_entrypoint.sh + +RUN apt-get clean && rm -rf /var/lib/apt/lists/* + +WORKDIR / + +ENTRYPOINT ["/ros_entrypoint.sh"] +CMD ["bash"] diff --git a/docker/melodic/Dockerfile b/docker/melodic/Dockerfile new file mode 100644 index 0000000..0c2237b --- /dev/null +++ b/docker/melodic/Dockerfile @@ -0,0 +1,26 @@ +FROM ros:melodic + +RUN apt-get update && apt-get install -y --no-install-recommends \ + && apt-get install -y --no-install-recommends \ + wget nano build-essential libomp-dev clang lld\ + ros-melodic-geodesy ros-melodic-pcl-ros ros-melodic-nmea-msgs \ + ros-melodic-rviz ros-melodic-tf-conversions ros-melodic-libg2o \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +RUN mkdir -p /root/catkin_ws/src +WORKDIR /root/catkin_ws/src +RUN /bin/bash -c '. /opt/ros/melodic/setup.bash; catkin_init_workspace' +RUN git clone https://github.com/koide3/ndt_omp.git +RUN git clone https://github.com/SMRT-AIST/fast_gicp.git --recursive + +COPY . /root/catkin_ws/src/hdl_graph_slam/ + +WORKDIR /root/catkin_ws +RUN /bin/bash -c '. /opt/ros/melodic/setup.bash; catkin_make' +RUN sed -i "6i source \"/root/catkin_ws/devel/setup.bash\"" /ros_entrypoint.sh + +WORKDIR / + +ENTRYPOINT ["/ros_entrypoint.sh"] +CMD ["bash"] diff --git a/docker/melodic_llvm/Dockerfile b/docker/melodic_llvm/Dockerfile new file mode 100644 index 0000000..e388b28 --- /dev/null +++ b/docker/melodic_llvm/Dockerfile @@ -0,0 +1,28 @@ +FROM ros:melodic + +RUN apt-get update && apt-get install -y --no-install-recommends \ + && apt-get install -y --no-install-recommends \ + wget nano build-essential libomp-dev clang lld\ + ros-melodic-geodesy ros-melodic-pcl-ros ros-melodic-nmea-msgs \ + ros-melodic-rviz ros-melodic-tf-conversions ros-melodic-libg2o \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +RUN mkdir -p /root/catkin_ws/src +WORKDIR /root/catkin_ws/src +RUN /bin/bash -c '. /opt/ros/melodic/setup.bash; catkin_init_workspace' +RUN git clone -b melodic https://github.com/koide3/ndt_omp.git +RUN git clone https://github.com/SMRT-AIST/fast_gicp.git --recursive + +COPY . /root/catkin_ws/src/hdl_graph_slam/ + +RUN update-alternatives --install /usr/bin/ld ld /usr/bin/ld.lld 50 + +WORKDIR /root/catkin_ws +RUN /bin/bash -c '. /opt/ros/melodic/setup.bash; CC=clang CXX=clang++ catkin_make' +RUN sed -i "6i source \"/root/catkin_ws/devel/setup.bash\"" /ros_entrypoint.sh + +WORKDIR / + +ENTRYPOINT ["/ros_entrypoint.sh"] +CMD ["bash"] diff --git a/docker/noetic/Dockerfile b/docker/noetic/Dockerfile new file mode 100644 index 0000000..a0759d5 --- /dev/null +++ b/docker/noetic/Dockerfile @@ -0,0 +1,26 @@ +FROM ros:noetic + +RUN apt-get update && apt-get install -y --no-install-recommends \ + && apt-get install -y --no-install-recommends \ + wget nano build-essential libomp-dev clang lld git\ + ros-noetic-geodesy ros-noetic-pcl-ros ros-noetic-nmea-msgs \ + ros-noetic-rviz ros-noetic-tf-conversions ros-noetic-libg2o \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +RUN mkdir -p /root/catkin_ws/src +WORKDIR /root/catkin_ws/src +RUN /bin/bash -c '. /opt/ros/noetic/setup.bash; catkin_init_workspace' +RUN git clone https://github.com/koide3/ndt_omp.git +RUN git clone https://github.com/SMRT-AIST/fast_gicp.git --recursive + +COPY . /root/catkin_ws/src/hdl_graph_slam/ + +WORKDIR /root/catkin_ws +RUN /bin/bash -c '. /opt/ros/noetic/setup.bash; catkin_make' +RUN sed -i "6i source \"/root/catkin_ws/devel/setup.bash\"" /ros_entrypoint.sh + +WORKDIR / + +ENTRYPOINT ["/ros_entrypoint.sh"] +CMD ["bash"] diff --git a/docker/noetic_llvm/Dockerfile b/docker/noetic_llvm/Dockerfile new file mode 100644 index 0000000..0cfcfbc --- /dev/null +++ b/docker/noetic_llvm/Dockerfile @@ -0,0 +1,28 @@ +FROM ros:noetic + +RUN apt-get update && apt-get install -y --no-install-recommends \ + && apt-get install -y --no-install-recommends \ + wget nano build-essential libomp-dev clang lld git\ + ros-noetic-geodesy ros-noetic-pcl-ros ros-noetic-nmea-msgs \ + ros-noetic-rviz ros-noetic-tf-conversions ros-noetic-libg2o \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +RUN mkdir -p /root/catkin_ws/src +WORKDIR /root/catkin_ws/src +RUN /bin/bash -c '. /opt/ros/noetic/setup.bash; catkin_init_workspace' +RUN git clone https://github.com/koide3/ndt_omp.git +RUN git clone https://github.com/SMRT-AIST/fast_gicp.git --recursive + +COPY . /root/catkin_ws/src/hdl_graph_slam/ + +RUN update-alternatives --install /usr/bin/ld ld /usr/bin/ld.lld 50 + +WORKDIR /root/catkin_ws +RUN /bin/bash -c '. /opt/ros/noetic/setup.bash; CC=clang CXX=clang++ catkin_make' +RUN sed -i "6i source \"/root/catkin_ws/devel/setup.bash\"" /ros_entrypoint.sh + +WORKDIR / + +ENTRYPOINT ["/ros_entrypoint.sh"] +CMD ["bash"] diff --git a/docker/run.sh b/docker/run.sh new file mode 100755 index 0000000..473cd43 --- /dev/null +++ b/docker/run.sh @@ -0,0 +1,6 @@ +#!/bin/bash +docker run --net=host -it --rm \ + -v $(realpath ..):/root/catkin_ws/src/hdl_graph_slam \ + -w /root/catkin_ws/src/hdl_graph_slam \ + $@ \ + hdl_graph_slam diff --git a/imgs/birds.png b/imgs/birds.png new file mode 100644 index 0000000000000000000000000000000000000000..835839036830029ea9c7edff65b0d571f3191c72 GIT binary patch literal 866270 zcmYhi1yCGK7cPtk4eqW1f)m``oghIM4est9+}$05LvV-S?(XjHb|>%m-&=pNTf5a& z%*=M5KIf6sp^EYnNbq>@U|?WKQj(&|U|^6sz#9h^1{h&FkMspTU<_m=M8Q7({pGY5 z#{(m9wvw6-U|$PPZgUckUez@$WlR9%-(+dXWx zOx=s0d^x+gNCdSdA;_TdAfRO|r>(*V`;y#wStGN?C2JbRLDKC@_=B;zBO%^aCDNDLbDG_=k@K(6G`!J$E+W6&sW zlZLL4g4=$8!J?AUC|%1XUzFMpJ=#_hcR7qBU%b5Q&Rkwz;z9TcUZ&K#*uZAnU}H1t zZuUpIF_XG<8HxzVF0*yXSHWiNzUXL*u!pZg3u1vQ21B z`M1x$o%Xj;C-i3Ag^GSU5^oPK<7v2BdBFr)y^3;!HEfP!5j;OI^`!j-e=&B8_%;_8 zA8dq~p#YDxB1ea^D8lU(vZfLe{Fm!N^x=k;X1i(Gmu9vC3P~v@ z_wNk!@5HXsV(WGt!KU?1M~hGp-Mte%JrHVTn83QLl_drRK{L}J)5N9xi-)FaqP5|J zWVf5KPE_=3R1l+gQ)hn*QevfJ;H!R~@v54PB2&omwuy-Jw)=B-hZxnyv;8wFk`}w) ztFxuV0Mngzg3w}u{ZDFIO0EbGyAxPV(m$WH4348#7(!%R);`pZ0=rW=uj_FF;OVD0 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