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

Accurate localization is crucial for autonomous vehicle navigation. In particular, there is active research in odometry that estimates a vehicle's transformation over time. To achieve this, various environmental perception sensors are utilized, among which radar sensors stand out for their costeffectiveness and robustness against adverse weather conditions compared to LiDAR sensors. However, traditional radar sensors provide only 2D spatial information (x, y,doppler). Recent technical advancements have introduced 4D imaging radar, providing 3D spatial (x, y,z,doppler) information. Nonetheless, radar data remains sparse and noisy when compared to LiDAR data, making it challenging to apply conventional LiDAR-based odometry algorithms. To address this challenge, we propose a radar point cloud odometry system that leverages Doppler, representing relative velocity, and Radar cross-section (RCS), a unique measure of an object's reflective ability. The IMU-free ego-motion estimation step utilizes Doppler data to generate an initial guess for registration. Additionally, to effectively extract meaningful points, we propose a polar-grid-based feature extraction algorithm utilizing RCS. To overcome the sparsity of radar point cloud data, we perform RCS-weighted accumulated scans-to-submap matching, where weights for point cloud registration are modeled based on RCS values to achieve robust odometry. Our proposed algorithm was experimentally evaluated using the View-of-Delft dataset. The results demonstrate that our algorithm outperforms existing methods, providing superior odometry performance.