Automotive Radar Missing Dimension Reconstruction from Motion

Automotive radars have been reliably used in most assisted and autonomous driving systems due to their robustness to extreme weather conditions. With radial velocity measurements from automotive radars, moving targets such as cars, trucks, and buses can be tracked robustly. However, due to the lack of elevation angles in measurements from automotive radars, stationary targets at different heights, such as maintenance holes and bridges, cannot be distinguished. Most autonomous systems rely on sensor fusion or ignore stationary targets to tackle the problem of missing the elevation angle dimension, which derives safety issues. We propose a simple yet effective approach to estimate the elevation angles of stationary targets from relative velocity and radial velocity measurements from an automotive radar. In contrast to structure from motion in computer vision, we utilize the instantaneous velocity generated from the motion of the ego vehicle. The radial velocity of each target is the projection of relative velocity onto the radial direction from radar to target. The radial velocity of each target can be inferred given the target's azimuth, elevation angle, and relative velocity. Accordingly, the elevation angle of each stationary target can be uniquely calculated given the velocity of radar and the target's azimuth and radial velocity measurements. The radar's velocity is estimated with the existing radar odometry algorithm and IMU. The proposed method is verified with real-world data. We evaluate the system's performance with a pre-built point cloud map and a good localization module in a real-world scenario. The proposed elevation angle reconstruction can reach a 1.41-degree mean error and standard deviation of 0.6 degrees in elevation angle.