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 cost-effectiveness and robustness against adverse weather conditions compared to LiDAR sensors. However, traditional radar sensors provide only 2D spatial information (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$x, y, doppler$</tex-math></inline-formula>). Recent technical advancements have introduced 4D imaging radar, providing 3D spatial (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$x, y, z, doppler$</tex-math></inline-formula>) 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Doppler</i>, representing relative velocity, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Radar cross-section (RCS)</i>, 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">View-of-Delft</i> dataset. The results demonstrate that our algorithm outperforms existing methods, providing superior odometry performance.