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, 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 and 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, 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

example 2: Riverside03 of MulRan dataset

As seen in the above video, 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.

You can download the dataset at the MulRan dataset website

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

By following this guideline, you can easily publish the MulRan dataset's LiDAR and IMU topics via ROS.

Dependency

All dependencies are same as the original LIO-SAM

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 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 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 and can removing dynamic objects. No GT labels or external sensor data such as RTK-GPS is required. This tutorial 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)

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
- save extended data: nodes' time, 6D pose, node's point cloud, and corresponding SC descriptors
- make a static map and self-labeled dynamic points by combining SC-LIO-SAM and 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

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