  This paper presents a novel place recognition approach to autonomous vehicles
by using low-cost, single-chip automotive radar. Aimed at improving recognition
robustness and fully exploiting the rich information provided by this emerging
automotive radar, our approach follows a principled pipeline that comprises (1)
dynamic points removal from instant Doppler measurement, (2) spatial-temporal
feature embedding on radar point clouds, and (3) retrieved candidates
refinement from Radar Cross Section measurement. Extensive experimental results
on the public nuScenes dataset demonstrate that existing visual/LiDAR/spinning
radar place recognition approaches are less suitable for single-chip automotive
radar. In contrast, our purpose-built approach for automotive radar
consistently outperforms a variety of baseline methods via a comprehensive set
of metrics, providing insights into the efficacy when used in a realistic
system.
