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https://github.com/rsasaki0109/li_slam_ros2
ROS 2 package of tightly-coupled lidar inertial ndt/gicp slam
https://github.com/rsasaki0109/li_slam_ros2
g2o gicp gtsam lidar loop-closure ndt ros2 slam
Last synced: 24 days ago
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ROS 2 package of tightly-coupled lidar inertial ndt/gicp slam
- Host: GitHub
- URL: https://github.com/rsasaki0109/li_slam_ros2
- Owner: rsasaki0109
- License: bsd-2-clause
- Created: 2020-07-09T00:27:50.000Z (over 4 years ago)
- Default Branch: develop
- Last Pushed: 2023-11-28T23:42:29.000Z (about 1 year ago)
- Last Synced: 2024-08-04T00:03:57.507Z (4 months ago)
- Topics: g2o, gicp, gtsam, lidar, loop-closure, ndt, ros2, slam
- Language: C++
- Homepage:
- Size: 3.67 MB
- Stars: 286
- Watchers: 8
- Forks: 41
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
li_slam_ros2
====
This package is a combination of [lidarslam_ros2](https://github.com/rsasaki0109/lidarslam_ros2) and the [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM) IMU composite method.
See LIO-SAM for IMU composites, otherwise see lidarslam_ros2.
- Walking dataset(casual_walk.bag)
Yellow path: path without loop closure, Green path: modified path, Red: map
Reference(From the LIO-SAM paper)
https://github.com/TixiaoShan/LIO-SAM/blob/master/config/doc/paper.pdf
- Campus dataset (large) demo(big_loop.bag)
Yellow path: path without loop closure, Red: map
(the 10x10 grids in size of 10m × 10m)
Green path: modified path with loop closure, Red: map
## requirement to build
You need [ndt_omp_ros2](https://github.com/rsasaki0109/ndt_omp_ros2) and gtsam for scan-matcher
clone
```
cd ~/ros2_ws/src
git clone --recursive https://github.com/rsasaki0109/li_slam_ros2
```
gtsam install
```
sudo add-apt-repository ppa:borglab/gtsam-release-4.1
sudo apt update
sudo apt install libgtsam-dev libgtsam-unstable-dev
```
build
```
cd ~/ros2_ws
rosdep update
rosdep install --from-paths src --ignore-src -yr
colcon build --cmake-args -DCMAKE_BUILD_TYPE=Release
```
## Walking dataset demo(casual_walk.bag)
The optimization pipeline in Lidar Inertial SLAM were taken from [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM).
(Note: See the LIO-SAM repository for detailed settings regarding IMU.
The other thing to note is that the speed will diverge if the voxel_grid_size is large.
demo data(ROS1) in LIO-SAM
https://github.com/TixiaoShan/LIO-SAM
To use ros1 rosbag , use [rosbags](https://pypi.org/project/rosbags/).
The Velodyne VLP-16 was used in this data.
```
rviz2 -d src/li_slam_ros2/scanmatcher/rviz/lio.rviz
```
```
ros2 launch scanmatcher lio.launch.py
```
```
ros2 bag play walking_dataset/
```
Green arrow: pose, Yellow path: path, Green path: path by imu
Yellow path: path without loop closure, Green path: modified path, Red: map
rosgraph
`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
```
## Campus dataset (large) demo(big_loop.bag)
```
rviz2 -d src/li_slam_ros2/scanmatcher/rviz/lio_bigloop.rviz
```
```
ros2 launch scanmatcher lio_bigloop.launch.py
```
```
ros2 bag play rosbag_v2 big_loop/
```
Yellow path: path without loop closure, Red: map
(the 10x10 grids in size of 10m × 10m)
Green path: modified path with loop closure, Red: map
## Used Libraries
- Eigen
- PCL(BSD3)
- g2o(BSD2 except a part)
- [ndt_omp](https://github.com/koide3/ndt_omp) (BSD2)
- gtsam(BSD2)