Tomato1107 / SC-A-LOAM

Robust LiDAR SLAM

SC-A-LOAM

What is SC-A-LOAM?

• A real-time LiDAR SLAM package that integrates A-LOAM and ScanContext.
  • A-LOAM for odometry (i.e., consecutive motion estimation)
  • ScanContext for coarse global localization that can deal with big drifts (i.e., place recognition as kidnapped robot problem without initial pose)
  • and iSAM2 of GTSAM is used for pose-graph optimization.
• This package aims to show ScanContext's handy applicability.
  • The only things a user should do is just to include Scancontext.h, call makeAndSaveScancontextAndKeys and detectLoopClosureID.

Features

1. A strong place recognition and loop closing
   • We integrated ScanContext as a loop detector into A-LOAM, and ISAM2-based pose-graph optimization is followed. (see https://youtu.be/okML_zNadhY?t=313 to enjoy the drift-closing moment)
2. A modular implementation
   • The only difference from A-LOAM is the addition of the laserPosegraphOptimization.cpp file. In the new file, we subscribe the point cloud topic and odometry topic (as a result of A-LOAM, published from laserMapping.cpp). That is, our implementation is generic to any front-end odometry methods. Thus, our pose-graph optimization module (i.e., laserPosegraphOptimization.cpp) can easily be integrated with any odometry algorithms such as non-LOAM family or even other sensors (e.g., visual odometry).

   • 

3. (optional) Altitude stabilization using consumer-level GPS
   • To make a result more trustworthy, we supports GPS (consumer-level price, such as U-Blox EVK-7P)-based altitude stabilization. The LOAM family of methods are known to be susceptible to z-errors in outdoors. We used the robust loss for only the altitude term. For the details, see the variable robustGPSNoise in the laserPosegraphOptimization.cpp file.

Prerequisites (dependencies)

• We mainly depend on ROS, Ceres (for A-LOAM), and GTSAM (for pose-graph optimization).
  • For the details to install the prerequisites, please follow the A-LOAM and LIO-SAM repositiory.
• The below examples are done under ROS melodic (ubuntu 18) and GTSAM version 4.x.

How to use?

• First, install the abovementioned dependencies, and follow below lines.

    mkdir -p ~/catkin_scaloam_ws/src
    cd ~/catkin_scaloam_ws/src
    git clone https://github.com/gisbi-kim/SC-A-LOAM.git
    cd ../
    catkin_make
    source ~/catkin_scaloam_ws/devel/setup.bash
    roslaunch aloam_velodyne aloam_mulran.launch # for MulRan dataset setting 

Example Results

Riverside 01, MulRan dataset

• The MulRan dataset provides lidar scans (Ouster OS1-64, horizontally mounted, 10Hz) and consumer level gps (U-Blox EVK-7P, 4Hz) data.
  • About how to use (publishing data) data: see here https://github.com/irapkaist/file_player_mulran
• example videos on Riverside 01 sequence.
  1. with consumer level GPS-based altitude stabilization: https://youtu.be/FwAVX5TVm04
  2. without the z stabilization: https://youtu.be/okML_zNadhY
• example result:

KITTI 05

• For KITTI (HDL-64 sensor), run using the command

  roslaunch aloam_velodyne aloam_velodyne_HDL_64.launch # for KITTI dataset setting
  

• To publish KITTI scans, you can use mini-kitti publisher, a simple python script: https://github.com/gisbi-kim/mini-kitti-publisher
• example video (no GPS used here): https://youtu.be/hk3Xx8SKkv4
• example result:

Indoor

• ScanContext also works at indoor environments (use smaller sc_max_radius value).
• example video: https://youtu.be/Uv6_BRmxJho
• example result:

For Livox LiDAR

• Scan Context also works for Livox LiDAR data
  • In this example, Scan Context is integrated with FAST-LIO (https://github.com/hku-mars/FAST_LIO).
  • Note: No additional integration effort is required. A user just run seperately FAST-LIO node and SC-A-LOAM's posegraphoptimization.cpp node!
• example video (tutoial and results): https://youtu.be/Fw9S6D6HozA
• example result:

  

Acknowledgements

• Thanks to LOAM, A-LOAM, and LIO-SAM code authors. The major codes in this repository are borrowed from their efforts.

Maintainer

• please contact me through [e-mail]

TODO

• More examples on other datasets (KITTI, complex urban dataset, etc.)
• Delayed RS loop closings
• data saver (e.g., pose-graph, optimized map)
• SLAM with multi-session localization
• Efficient whole map visualization