/SC-LeGO-LOAM

LiDAR SLAM: Scan Context + LeGO-LOAM

Primary LanguageC++

SC-LeGO-LOAM

NEWS (Nov, 2020)

Real-time LiDAR SLAM: Scan Context (18 IROS) + LeGO-LOAM (18 IROS)

  • This repository is an example use-case of Scan Context C++ , the LiDAR place recognition method, for LiDAR SLAM applications.
  • For more details for each algorithm please refer to
    Scan Context https://github.com/irapkaist/scancontext
    LeGO LOAM https://github.com/facontidavide/LeGO-LOAM-BOR
  • Just include Scancontext.h. For details see the file mapOptmization.cpp.
  • This example is integrated with LOAM, but our simple module (i.e., Scancontext.h) can be easily integrated with any other key-frame-based odometry (e.g., wheel odometry or ICP-based odometry).
  • Current version: April, 2020.

Features

  • 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: The loop detector runs at 10-15Hz (for 20 x 60 size, 10 candidates)

Examples

Scan Context integration

  • For implementation details, see the mapOptmization.cpp; all other files are same as the original LeGO-LOAM.
  • Some detail comments
    • We use non-conservative threshold for Scan Context's nearest distance, so expect to maximise true-positive loop factors, while the number of false-positive increases.
    • To prevent the wrong map correction, we used Cauchy (but DCS can be used) kernel for loop factor. See mapOptmization.cpp for details. (the original LeGO-LOAM used non-robust kernel). We found that Cauchy is emprically enough.
    • We use both two-type of loop factor additions (i.e., radius search (RS)-based as already implemented in the original LeGO-LOAM and Scan context (SC)-based global revisit detection). See mapOptmization.cpp for details. SC is good for correcting large drifts and RS is good for fine-stitching.
    • Originally, Scan Context supports reverse-loop closure (i.e., revisit a place in a reversed direction) and examples in here (py-icp slam) . Our Scancontext.cpp module contains this feature. However, we did not use this for closing a loop in this repository because we found PCL's ICP with non-eye initial is brittle.

How to use

  • Place the directory SC-LeGO-LOAM under user catkin work space
  • For example, 
    cd ~/catkin_ws/src
git clone https://github.com/irapkaist/SC-LeGO-LOAM.git
cd ..
catkin_make
source devel/setup.bash
roslaunch lego_loam run.launch

MulRan dataset

Dependencies

  • All dependencies are same as LeGO-LOAM (i.e., ROS, PCL, and GTSAM).
  • We used C++14 to use std::make_unique in Scancontext.cpp but you can use C++11 with slightly modifying only that part.

Cite SC-LeGO-LOAM

@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{legoloam2018,
  title={LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain},
  author={Shan, Tixiao and Englot, Brendan},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={4758-4765},
  year={2018},
  organization={IEEE}
}

Contact

  • Maintainer: Giseop Kim (paulgkim@kaist.ac.kr)

Misc notes

  • You may also be interested in this (from the other author's) implementation :)