version 2021-06-24
- 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.
- 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;
makeAndSaveScancontextAndKeysanddetectLoopClosureID. - Fast: A single loop detection requires under 30 ms (for 20 x 60 size, 3 candidates)
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).
- 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-SAMunder 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.
- All dependencies are same as the original LIO-SAM
- 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.
performSCLoopClosureis good for correcting large drifts andperformRSLoopClosureis 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.
- 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
failureDetectionin 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) + 1in 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.
- 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)
@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}
}
- Maintainer: Giseop Kim (
[email protected])
- Minwoo Jung: made the original LIO-SAM runs on the MulRan dataset.
- 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.
- 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.
- 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
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent