[IEEE RA-L] This repository is the official code for ReFeree: Radar-Based Lightweight and Robust Localization Using Feature and Free space.
Hogyun Kim*, Byunghee Choi*, Euncheol Choi, Younggun Cho†
(* represents equal contribution.)
Spatial AI and Robotics Lab (SPARO)
- This repository is using the structure of Navtech-Radar-SLAM. The odometry and pose-graph optimization part is Yeti-Odometry and iSAM2, respectively.
- The Radar Place Recognition parts of this repository are referee.hpp and referee.cpp, and matching part in odometry.cpp.
This repository built and complied in environment Ubuntu 20.04, ROS Noetic(desktop-full).
The required apt install packges are below.
$ sudo apt-get install python3-catkin-tools
$ sudo apt-get install ros-noetic-gtsam
First, clone and build the repository in your ros workspace.
$ cd your_ros_workspace/src
$ git clone https://github.com/sparolab/ReFeree-Radar-SLAM.git
$ cd your_ros_workspace
$ catkin build nano_gicp && catkin build
Second, prepare dataset in following format:
(dataset_name)/
├── polar/
│ ├── (timestamp_start).png
│ ├── ...
│ └── (timestamp_start).png
The timstamp-named png radar image files should be located in (dataset_name)/polar directory.
And revise seq_dir and save_directory parameters in yeti_radar_odometry.launch.
<param name="seq_dir" type="string" value="(path_of_your_dataset)/(dataset_name)/" />
...
<param name="save_directory" type="string" value="(path_of_your_results)/results/"/>
Third, launch the yeti_radar_odometry.launch.
$ source devel/setup.bash
$ roslaunch yeti yeti_radar_odometry.launch
@article{kim2024referee,
title={ReFeree: Radar-Based Lightweight and Robust Localization using Feature and Free space},
author={Kim, Hogyun and Choi, Byunghee and Choi, Euncheol and Cho, Younggun},
journal={IEEE Robotics and Automation Letters},
year={2024},
publisher={IEEE}
}
- Hogyun Kim ([email protected])
- Byunghee Choi ([email protected])