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Radar4Motion: IMU-Free 4D Radar Odometry with Robust Dynamic Filtering and RCS-Weighted Matching

Radar4Motion is a robust odometry method that utilizes Doppler and RCS information from the 4D imaging radar's point cloud, even in the presence of noisy and sparse point cloud data.

View-of-Delft Dataset Seq 03

View-of-Delft Dataset Seq 17

• The above gif shows ONLY odometry-based mapping results.
  • NO inertial sensor, NO GNSS sensor, NO loop-closure
  • Only Single front-view 4D Imaging Radar!

Prerequisites

To run this project, you need: - ROS (Robot Operating System), tested with ROS Noetic - Eigen3, for matrix and vector operations - PCL (Point Cloud Library), for handling radar point cloud data - nlohmann_json, for JSON parsing

Installation

Clone the Repository

cd ~/catkin_ws/src
git clone https://github.com/ailab-hanyang/Radar4Motion.git

Build the Project

Using catkin, set up your workspace and build the project:

cd ~/catkin_ws
catkin_make
source devel/setup.bash

Using the View-of-Delft (VoD) Dataset

If you want to evaluate Radar4Motion with the View-of-Delft (VoD) dataset, please do the following:

1. Download or place the VoD dataset in your preferred location.

2. Set VoD datset path str_vod_dataset_base_path in /launch/radar4motion_offline.launch.

   • OR, create a symbolic link inside the dataset/ folder so the code can locate VoD data under dataset/view_of_delft_PUBLIC.
   • Set <param name="str_vod_dataset_base_path" value="$(find radar4motion)/dataset/"/>

   • For example: sh cd ~/catkin_ws/src/Radar4Motion mkdir -p dataset ln -s /path/to/view_of_delft_PUBLIC dataset/

     Folder tree: sh Radar4Motion/ ├── src/ │ ├── .cpp files.. └── dataset/ └── clips/ └── view_of_delft_PUBLIC/ -> symlink to /path/to/view_of_delft_PUBLIC ├── radar/ │ ├── training/ │ │ ├── velodyne/ │ │ │ ├── (bin files) │ │ ├── pose/ │ │ │ ├── (label files)

3. Create a test folder for evaluation (option) sh cd ~/catkin_ws/src/Radar4Motion mkdir -p test

Usage

To start the radar odometry processing, launch the provided ROS launch files: (Deactivate any active conda environment.) - View-of-delft dataset sh cd ~/catkin_ws/src source devel/setup.bash roslaunch radar4motion radar4motion_offline.launch - ROS topic sh cd ~/catkin_ws/src source devel/setup.bash roslaunch radar4motion radar4motion_online.launch - Note: The accumulated scans-to-submap matching algorithm requires precise sensor-vehicle calibration.
Therefore, you must accurately set the following parameters in the ./config/radar_point_cloud_odometry.ini file according to your environment:

```sh
m_d_radar_calib_x_m = 3.5
m_d_radar_calib_y_m = 0.0
m_d_radar_calib_z_m = 0.0
m_d_radar_calib_roll_deg = 1.0
m_d_radar_calib_pitch_deg = -0.568
m_d_radar_calib_yaw_deg = 0.43
```

- In particular, for the View-of-Delft (VOD) dataset, exact sensor-vehicle calibration values are not provided by the dataset.
- Hence, we have set approximate values based on the sensor mounting position and vehicle images. These values will be updated in the future through our ongoing *radar4selfcalibration* research, which will estimate these parameters more accurately.

Configurations

Detailed descriptions of parameters can be found in PARAMETERS. 1. ROS & File path configuration - File: /launch/radar4motion_[offline/online].launch 2. Radar ego motion estimation (ref: REVE) - File: /config/radar_ego_motion_estimation.ini 3. Odometry - File: /config/radar_point_cloud_odometry.ini

TODO

• [ ] Update voxel struct (frame-scan unit)
• [ ] Update VoD calibration params with Radar4SelfCalibration

License

This project is licensed under the Apache-2.0 License - see the LICENSE.md file for details.

Authors

• Soyeong Kim - soyeongkim@hanyang.ac.kr
• Jiwon Seok - jiwonseok@hanyang.ac.kr

Citation

If you find this work useful, please cite the following paper:

@article{kim2024radar4motion,
title={Radar4Motion: IMU-Free 4D Radar Odometry with Robust Dynamic Filtering and RCS-Weighted Matching},
author={Kim, Soyeong and Seok, Jiwon and Lee, Jaehwan and Jo, Kichun},
journal={IEEE Transactions on Intelligent Vehicles},
year={2024},
publisher={IEEE}
}

Acknowledgement

We would like to express our gratitude to all the contributors and resources that made this research possible. - In the development of this package, we refer to KISS-ICP and REVE for source codes. - Dataset: View-of-Delft (VoD) - Evaluation: evo package for odometry evaluation