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https://github.com/gisbi-kim/SC-LIO-SAM
LiDAR-inertial SLAM: Scan Context + LIO-SAM
https://github.com/gisbi-kim/SC-LIO-SAM
lidar-slam place-recognition
Last synced: 19 days ago
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LiDAR-inertial SLAM: Scan Context + LIO-SAM
- Host: GitHub
- URL: https://github.com/gisbi-kim/SC-LIO-SAM
- Owner: gisbi-kim
- Created: 2020-11-19T14:16:32.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2023-04-27T12:39:48.000Z (over 1 year ago)
- Last Synced: 2024-08-01T03:18:48.758Z (4 months ago)
- Topics: lidar-slam, place-recognition
- Language: C++
- Homepage:
- Size: 6.05 MB
- Stars: 610
- Watchers: 15
- Forks: 174
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
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README
# SC-LIO-SAM
version 2021-06-24
## What is SC-LIO-SAM?
- SC-LIO-SAM is **a real-time lidar-inertial SLAM package.**
- LiDAR-inertial SLAM: Scan Context + LIO-SAM
- This repository is an example use-case of [Scan Context](https://github.com/irapkaist/scancontext), which is a fast and robust LiDAR place recognition method.
- For more details for each algorithm please refer to
Scan Context https://github.com/irapkaist/scancontext
LIO-SAM https://github.com/TixiaoShan/LIO-SAM
- You can also use the LiDAR-only versions of this project: [SC-LeGO-LOAM](https://github.com/irapkaist/SC-LeGO-LOAM) and [SC-A-LOAM](https://github.com/gisbi-kim/SC-A-LOAM).
## Scan Context: A fast and robust place recognition
- Light-weight: a single header and cpp file named "Scancontext.h" and "Scancontext.cpp"
- Our module has KDtree and we used nanoflann. nanoflann is an also single-header-program and that file is in our directory.
- Easy to use: A user just remembers and uses only two API functions; ```makeAndSaveScancontextAndKeys``` and ```detectLoopClosureID```.
- Fast: A single loop detection requires under 30 ms (for 20 x 60 size, 3 candidates)
## Examples
We provide example results using [MulRan dataset](https://sites.google.com/view/mulran-pr/dataset), which provides LiDAR and 9dof IMU data. You can see the parameter file (i.e., params_mulran.yaml) modified for MulRan dataset.
#### example 1: [KAIST02 of MulRan dataset](https://youtu.be/Il0YxztuZEU?t=280)
#### example 2: [Riverside03 of MulRan dataset](https://youtu.be/Y6DXlC34qlc?t=459)
- As seen in the above [video](https://youtu.be/Y6DXlC34qlc?t=459), the combination of Scan Context loop detector and LIO-SAM's odometry is robust to highly dynamic and less structured environments (e.g., a wide road on a bridge with many moving objects).
## How to use?
- We provide a tutorial that runs SC-LIO-SAM on MulRan dataset, you can reproduce the above results by following these steps.
1. You can download the dataset at the [MulRan dataset website](https://sites.google.com/view/mulran-pr/home)
2. Place the directory `SC-LIO-SAM` under user catkin work space
For example,
```
cd ~/catkin_ws/src
git clone https://github.com/gisbi-kim/SC-LIO-SAM.git
cd ..
catkin_make
source devel/setup.bash
roslaunch lio_sam run.launch # or roslaunch lio_sam run_mulran.launch
```
3. By following [this guideline](https://github.com/irapkaist/file_player_mulran), you can easily publish the MulRan dataset's LiDAR and IMU topics via ROS.
## Dependency
- All dependencies are same as the original [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM#dependency)
## Notes
#### About performance
- We used two types of loop detetions (i.e., radius search (RS)-based as already implemented in the original LIO-SAM and Scan context (SC)-based global revisit detection). See mapOptmization.cpp for details. ```performSCLoopClosure``` is good for correcting large drifts and ```performRSLoopClosure``` is good for fine-stitching.
- To prevent the wrong map correction, we used Cauchy (but DCS can be used) kernel for loop factor. See mapOptmization.cpp for details. We found that Cauchy is emprically enough.
#### Minor
- We used C++14 to use std::make_unique in Scancontext.cpp but you can use C++11 with slightly modifying only that part.
- We used a larger value for velocity upper bound (see ```failureDetection``` in imuPreintegration.cpp) for fast motions of a MulRan dataset's car platform.
- The some code lines are adapted for Ouster LiDAR. Thus, if you use an other LiDAR, please refer [the original author's guideline](https://github.com/TixiaoShan/LIO-SAM#other-notes) and fix some lines.
- A LiDAR scan of MulRan dataset has no ring information, thus we simply made a hardcoding like ```int rowIdn = (i % 64) + 1 ``` in imageProjection.cpp to make a ring index information that LIO-SAM requires, and it works. However, if you use an other LiDAR, you need to change this line.
## Applications
- With our [save utility](https://github.com/gisbi-kim/SC-LIO-SAM/blob/1e1bfea4f7708fbd93d9eb471e63ae9804e1dd16/SC-LIO-SAM/src/mapOptmization.cpp#L203) accompanied with this repository, we can save a set of keyframe's time, estimated pose, a corresponding point cloud, and Scan Context descriptors. The estimated poses are saved as a file named optimized_poses.txt and its format is equivalent to the famous KITTI odometry dataset's pose.txt file. For example:
- If you use the above saved files, you can feed these data to [Removert](https://github.com/irapkaist/removert) and can removing dynamic objects. No GT labels or external sensor data such as RTK-GPS is required. This [tutorial](https://youtu.be/UiYYrPMcIRU) guides steps from running SC-LIO-SAM to save data to Removert to remove dynamic objects in a scan. Example results are:
- For the safe and light-weight map saver, we support off-line scan merging utils for the global map construction within user's ROI (see tools/python/makeMergedMap.py, for the details, see the [tutorial video](https://youtu.be/jmR3DH_A4Co))
## Cite SC-LIO-SAM
```
@INPROCEEDINGS { gkim-2018-iros,
author = {Kim, Giseop and Kim, Ayoung},
title = { Scan Context: Egocentric Spatial Descriptor for Place Recognition within {3D} Point Cloud Map },
booktitle = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems },
year = { 2018 },
month = { Oct. },
address = { Madrid }
}
```
and
```
@inproceedings{liosam2020shan,
title={LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping},
author={Shan, Tixiao and Englot, Brendan and Meyers, Drew and Wang, Wei and Ratti, Carlo and Rus Daniela},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={5135-5142},
year={2020},
organization={IEEE}
}
```
## Contact
- Maintainer: Giseop Kim (`[email protected]`)
#### Contributors
- Minwoo Jung: made the original LIO-SAM runs on the MulRan dataset.
## Acknowledgement
- SC-LIO-SAM is based on LIO-SAM (Tixiao Shan et al., IROS 2020). We thank Tixiao Shan and the LIO-SAM authors for providing a great base framework.
## Update history
- 2021.06.23
- yaml file is reformatted to support the compatible form with the recent original LIO-SAM repository.
- offline ROI global map construction python util is supported.
## TODO
- About performance
- improve better RS loop closing (ICP test sometimes fails in reverse directions)
- support reverse-closing of SC loops with Scan Context initial yaw
- support SC augmentation
- lagged RS loop closing after large drifts solved
- About funtions for convenience
- [x] save extended data: nodes' time, 6D pose, node's point cloud, and corresponding SC descriptors
- [x] make a static map and self-labeled dynamic points by combining SC-LIO-SAM and [removert](https://github.com/irapkaist/removert).
- Minor (not related to the performance)
- fix the visualization issue: disappearing map points after SC loop closing
- fix safe-save for map points after closing the program