Precise Ego-Motion Estimation with Millimeter-Wave Radar Under Diverse and Challenging Conditions

In contrast to cameras, lidars, GPS, and proprioceptive sensors, radars are affordable and efficient systems that operate well under variable weather and lighting conditions, require no external infrastructure, and detect long-range objects. In this paper, we present a reliable and accurate radar-only motion estimation algorithm for mobile autonomous systems. Using a frequency-modulated continuous-wave (FMCW) scanning radar, we first extract landmarks with an algorithm that accounts for unwanted effects in radar returns. To estimate relative motion, we then perform scan matching by greedily adding point correspondences based on unary descriptors and pairwise compatibility scores. Our radar odometry results are robust under a variety of conditions, including those under which visual odometry and GPS/INS fail.