Skip to content

Latest commit

 

History

History
142 lines (122 loc) · 6.86 KB

README.md

File metadata and controls

142 lines (122 loc) · 6.86 KB
Raw

Navtech-Radar-SLAM



Video   •   Install   •   Paper (ORORA)   •   Paper (ScanContext)

animated

ORORA-SLAM: SLAM using Outlier-robust radar odometry (ORORA) + ScanContext-based Loop Closing

What is Navtech-Radar-SLAM?

  • In this repository, a (minimal) SLAM problem is defeind as SLAM = Odometry + Loop closing, and the optimized states are only robot poses along a trajectory.
  • Based on the above view, this repository aims to integrate current available radar odometry, radar place recognition, and pose-graph optimization.
    1. Radar odometry: ORORA open source.
      • The odometry modules consumes file-based input (not ROS subscription) in this example. See here for the details.
    2. Radar place recognition: Scan Context open source
      • In MulRan dataset paper, the radar scan context is also proposed, but in this repository we use a Cartesian 2D feature point cloud (extracted via cen2019 method) as an input for the original Scan Context (IROS2018) method and it works.
      • The Scan Context-based loop detection is included in the file pgo/SC-A-LOAM/laserPosegraphOptimization.cpp.
    3. Pose-graph optimization
      • iSAM2 in GTSAM is used. See pgo/SC-A-LOAM/laserPosegraphOptimization.cpp for the details (ps. the implementation is eqaul to SC-A-LOAM and it means laserPosegraphOptimization.cpp node is generic!)

How to use?

Dependencies

  • ORORA: OpenCV, and SC-PGO: GTSAM
  • Code is tested on Ubuntu 20.04 with ROS Noetic.

Steps

First, clone and build. Note, there's a submodule in the repository.

$ mkdir -p ~/catkin_radarslam/src && cd ~/catkin_radarslam/src
$ git clone https://github.com/gisbi-kim/navtech-radar-slam.git 
$ cd navtech-radar-slam && git submodule init && git submodule update
$ cd ../..
$ catkin_make

Second,

Then, enjoy!

$ source devel/setup.bash
$ roslaunch src/navtech-radar-slam/launch/navtech_radar_slam_mulran.launch seq_dir:=${DATA_DIR}

For example,

$ roslaunch src/navtech-radar-slam/launch/navtech_radar_slam_mulran.launch seq_dir:="/media/shapelim/UX980/UX960NVMe/mulran-radar/KAIST03"

Examples

  • The examples are from MulRan dataset, which is suitable to evaluate the radar odometry or SLAM algorithm in complex urban sites.
    • The MulRan dataset provides the oxford-radar-robotcar-radar data format (i.e., meta data such as ray-wise timestamps are imbedded in an radar image, see details here)

1. Recent Result in KAIST 03 of MulRan dataset

2. KAIST 03 of MulRan dataset

3. Riverside 03 of MulRan dataset

Related papers

If you cite this repository, please consider below papers.

  • ORORA open source for radar odometry: 
    @INPROCEEDINGS { lim-2023-icra,
    author = {Lim, Hyungtae and Han, Kawon and Shin, Gunhee and Kim, Giseop and Hong, Songcheol and Myung, Hyun},
    title = { ORORA: Outlier-robust radar odometry },
    booktitle = { Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) },
    pages={2046--2053},
    year = { 2023 },
}
  • Yeti open source for radar odometry: 
    @ARTICLE{burnett_ral21,
    author = {Keenan Burnett, Angela P. Schoellig, Timothy D. Barfoot},
    journal={IEEE Robotics and Automation Letters},
    title={Do We Need to Compensate for Motion Distortion and Doppler Effects in Spinning Radar Navigation?},
    year={2021},
    volume={6},
    number={2},
    pages={771-778},
    doi={10.1109/LRA.2021.3052439}}
}
  • Scan Context open source for place recognition: 
    @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 }
}
  • MulRan dataset: 
    @INPROCEEDINGS{ gskim-2020-mulran, 
    TITLE={MulRan: Multimodal Range Dataset for Urban Place Recognition}, 
    AUTHOR={Giseop Kim and Yeong Sang Park and Younghun Cho and Jinyong Jeong and Ayoung Kim}, 
    BOOKTITLE = { Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) },
    YEAR = { 2020 },
    MONTH = { May },
    ADDRESS = { Paris }
}

TODO

  • About utilities
    • support ROS-based input (topic subscription)
    • support a resulting map save functions.
  • About performances
    • support reverse loop closing.
    • enhance RS (radius-search) loop closings.