Implicit Extended Kalman Filter for Radar-Only 2D Odometry and Sensor-Vehicle Calibration

Accurate and robust pose estimation is essential for any autonomous vehicle. While sensors like GNSS, LiDAR, and camera are widely used for state estimation, they admit weaknesses under challenging environments e.g. poor satellite signals, low light, or adverse weather. Recent 3D and 4D radars have emerged as a robust alternative, providing sparse point clouds with information on the detected targets’ range, angle, and radial velocity. As a result, interest in radar-based odometry (RO) has grown steadily. In this paper, we explore two aspects: radar-only ego-motion estimation and multi-radar- vehicle extrinsic calibration. We present a Doppler-based radar odometry algorithm using an Implicit Extended Kalman Fil- ter and propose a novel, radar-only and target-less multi- radar-vehicle calibration method. An observability analysis is conducted to optimize sensor placement considering both the odometry and calibration. Finally, the proposed methods are validated through both simulation and real data.