Radar-only Instantaneous Ego-motion Estimation Using Neural Networks

Abstract

The problem of 2D instantaneous ego-motion estimation for vehicles equipped with automotive radars is studied. To leverage multi-dimensional radar point clouds and exploit point features automatically, without human engineering, a novel approach is proposed that transforms ego-motion estimation into a weighted least squares (wLSQ) problem using neural networks. Comparison with existing methods is done using a challenging real-world radar dataset. The comparison results show that the proposed method can achieve better performance in terms of estimation accuracy, long-term stability, and runtime performance compared to a representative approach selected from the recent literature.