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https://github.com/rsasaki0109/lidarslam_ros2
ROS 2 package of 3D lidar slam using ndt/gicp registration and pose-optimization
https://github.com/rsasaki0109/lidarslam_ros2
lidar localization mapping robotics ros ros2 slam
Last synced: 24 days ago
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ROS 2 package of 3D lidar slam using ndt/gicp registration and pose-optimization
- Host: GitHub
- URL: https://github.com/rsasaki0109/lidarslam_ros2
- Owner: rsasaki0109
- License: bsd-2-clause
- Created: 2020-01-29T12:57:45.000Z (almost 5 years ago)
- Default Branch: develop
- Last Pushed: 2024-05-29T21:51:55.000Z (7 months ago)
- Last Synced: 2024-08-04T00:03:58.738Z (4 months ago)
- Topics: lidar, localization, mapping, robotics, ros, ros2, slam
- Language: C++
- Homepage:
- Size: 3.35 MB
- Stars: 480
- Watchers: 12
- Forks: 118
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
lidarslam_ros2
====
ros2 slam package of the frontend using OpenMP-boosted gicp/ndt scan matching and the backend using graph-based slam.
mobile robot mapping
Green: path with loopclosure
(the 25x25 grids in size of 10m × 10m)
Red and yellow: map
## summary
`lidarslam_ros2` is a ROS2 package of the frontend using OpenMP-boosted gicp/ndt scan matching and the backend using graph-based slam.
I found that even a four-core laptop with 16GB of memory could work in outdoor environments for several kilometers with only 16 line LiDAR.
(WIP)
## requirement to build
You need [ndt_omp_ros2](https://github.com/rsasaki0109/ndt_omp_ros2) for scan-matcher
clone
(If you forget to add the --recursive option when you do a git clone, run `git submodule update --init --recursive` in the lidarslam_ros2 directory)
```
cd ~/ros2_ws/src
git clone --recursive https://github.com/rsasaki0109/lidarslam_ros2
cd ..
rosdep install --from-paths src --ignore-src -r -y
```
build
```
colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release
```
## io
### frontend(scan-matcher)
- input
/input_cloud (sensor_msgs/PointCloud2)
/tf(from "base_link" to LiDAR's frame)
/initial_pose (geometry_msgs/PoseStamed)(optional)
/imu (sensor_msgs/Imu)(optional)
/tf(from "odom" to "base_link")(Odometry)(optional)
- output
/current_pose (geometry_msgs/PoseStamped)
/map (sensor_msgs/PointCloud2)
/path (nav_msgs/Path)
/tf(from "map" to "base_link")
/map_array(lidarslam_msgs/MapArray)
### backend(graph-based-slam)
- input
/map_array(lidarslam_msgs/MapArray)
- output
/modified_path (nav_msgs/Path)
/modified_map (sensor_msgs/PointCloud2)
- srv
/map_save (std_srvs/Empty)
## how to save the map
`pose_graph.g2o` and `map.pcd` are saved in loop closing or using the following service call.
```
ros2 service call /map_save std_srvs/Empty
```
## params
- frontend(scan-matcher)
|Name|Type|Default value|Description|
|---|---|---|---|
|registration_method|string|"NDT"|"NDT" or "GICP"|
|ndt_resolution|double|5.0|resolution size of voxel[m]|
|ndt_num_threads|int|0|threads using ndt(if `0` is set, maximum alloawble threads are used.)(The higher the number, the better, but reduce it if the CPU processing is too large to estimate its own position.)|
|gicp_corr_dist_threshold|double|5.0|the distance threshold between the two corresponding points of the source and target[m]|
|trans_for_mapupdate|double|1.5|moving distance of map update[m]|
|vg_size_for_input|double|0.2|down sample size of input cloud[m]|
|vg_size_for_map|double|0.05|down sample size of map cloud[m]|
|use_min_max_filter|bool|false|whether or not to use minmax filter|
|scan_max_range|double|100.0|max range of input cloud[m]|
|scan_min_range|double|1.0|min range of input cloud[m]|
|scan_period|double|0.1|scan period of input cloud[sec](If you want to compound imu, you need to change this parameter.)|
|map_publish_period|double|15.0|period of map publish[sec]|
|num_targeted_cloud|int|10|number of targeted cloud in registration(The higher this number, the less distortion.)|
|debug_flag|bool|false|Whether or not to display the registration information|
|set_initial_pose|bool|false|whether or not to set the default pose value in the param file|
|initial_pose_x|double|0.0|x-coordinate of the initial pose value[m]|
|initial_pose_y|double|0.0|y-coordinate of the initial pose value[m]|
|initial_pose_z|double|0.0|z-coordinate of the initial pose value[m]|
|initial_pose_qx|double|0.0|Quaternion x of the initial pose value|
|initial_pose_qy|double|0.0|Quaternion y of the initial pose value|
|initial_pose_qz|double|0.0|Quaternion z of the initial pose value|
|initial_pose_qw|double|1.0|Quaternion w of the initial pose value|
|publish_tf|bool|true|Whether or not to publish tf from global frame to robot frame|
|use_odom|bool|false|whether odom is used or not for initial attitude in point cloud registration|
|use_imu|bool|false|whether 9-axis imu(Angular velocity, acceleration and orientation must be included.) is used or not for point cloud distortion correction.(Note that you must also set the `scan_period`.)|
|debug_flag|bool|false|Whether or not to display the registration information|
- backend(graph-based-slam)
|Name|Type|Default value|Description|
|---|---|---|---|
|registration_method|string|"NDT"|"NDT" or "GICP"|
|ndt_resolution|double|5.0|resolution size of voxel[m]|
|ndt_num_threads|int|0|threads using ndt(if `0` is set, maximum alloawble threads are used.)|
|voxel_leaf_size|double|0.2|down sample size of input cloud[m]|
|loop_detection_period|int|1000|period of searching loop detection[ms]|
|threshold_loop_closure_score|double|1.0| fitness score of ndt for loop clousure|
|distance_loop_closure|double|20.0| distance far from revisit candidates for loop clousure[m]|
|range_of_searching_loop_closure|double|20.0|search radius for candidate points from the present for loop closure[m]|
|search_submap_num|int|2|the number of submap points before and after the revisit point used for registration|
|num_adjacent_pose_cnstraints|int|5|the number of constraints between successive nodes in a pose graph over time|
|use_save_map_in_loop|bool|true|Whether to save the map when loop close(If the map saving process in loop close is too heavy and the self-position estimation fails, set this to `false`.)|
## demo
### trial environment
demo data(ROS1) is `hdl_400.bag` in [hdl_graph_slam](https://github.com/koide3/hdl_graph_slam)
The Velodyne VLP-32 was used in this data.
To use rosbag in ROS1, use [rosbags](https://pypi.org/project/rosbags/)
```
ros2 launch lidarslam lidarslam.launch.py
```
```
ros2 bag play hdl_400/
```
Green: path with loopclosure, Yellow: path without loopclosure
### The larger environment
(This data is not available at the moment...)
demo data(ROS1) by Autoware Foundation
https://data.tier4.jp/rosbag_details/?id=212
The Velodyne VLP-16 was used in this data.
```
ros2 launch lidarslam lidarslam_tukuba.launch.py
```
```
ros2 bag play tc_2017-10-15-15-34-02_free_download/
```
Green: path
Red and yellow: map
## Used Libraries
- Eigen
- PCL(BSD3)
- g2o(BSD2 except a part)
- [ndt_omp](https://github.com/koide3/ndt_omp) (BSD2)
## Related packages
- [li_slam_ros2](https://github.com/rsasaki0109/li_slam_ros2) (BSD2)