Radar-Based Odometry for Low-Speed Driving

Abstract--We address automotive odometry for low-speed driving and parking, where centimeter-level accuracy is required due to tight spaces and nearby obstacles. Traditional methods using inertial-measurement units and wheel encoders require vehicle-specific calibration, making them costly for consumer-grade vehicles. T o overcome this, we propose a radar-based simultaneous localization and mapping (SLAM) approach that fuses inertial and 4D radar measurements. Our approach tightly couples feature positions and Doppler velocities for accurate localization and robust data association. Key contributions include a tightly coupled radar-Doppler extended Kalman filter, multi-radar support and an information-based feature-pruning strategy. Experiments using both proprietary and public datasets demonstrate high-accuracy localization during low-speed driving. Accurate relative localization is critical for automated parking applications, where the vehicle executes low-speed maneuvers in complex environments. Unlike highway or urban driving, parking scenarios demand centimeter-level accuracy due to space constraints and the proximity to surrounding obstacles.