Ground-Aware Automotive Radar Odometry

Abstract

Odometry is crucial for the navigation of autonomous vehicles in unknown environments. While cameras and LiDARs are commonly used to estimate the ego-motion of a vehicle, these sensors face limitations under bad lighting and severe weather conditions. Automotive radars overcome these challenges, but radar point clouds are generally sparse and noisy, making it difficult to identify useful features within a radar scan. In this paper, we address the problem of ego-motion estimation using a single automotive radar sensor. We propose a simple, yet effective, heuristic-based method to extract the ground plane from single radar scans and perform ground plane matching between consecutive scans. Additionally, we perform a windowed factor-graph optimization of the poses together with the ground plane, improving the accuracy of the pose estimation. We put our work to the test using the 4DRadarDataset. Our findings illustrate the state-of-the-art performance of our odometry approach compared to existing alternatives that use radar point clouds.