Radar-Inertial Odometry for Low-Speed Driving

Abstract

We address automotive odometry for low-speed driving and parking, where high 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. To overcome this limitation, we propose a radar-inertial odometry approach that fuses inertial and 4D radar measurements. Our approach tightly couples tracked feature positions and Doppler velocity 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 accuracy during low-speed driving.