Radar4Motion: IMU-Free 4D Radar Odometry With Robust Dynamic Filtering and RCS-Weighted Matching
Soyeong Kim, Jiwon Seok, Jaehwan Lee, Kichun Jo · IEEE Transactions on Intelligent Vehicles · 2024
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 cost-effectiveness 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 leveragesDoppler, representing relative velocity, andRadar 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 theView-of-Delftdataset. The results demonstrate that our algorithm outperforms existing methods, providing superior odometry performance.