  This paper presents an efficient and accurate radar odometry pipeline for
large-scale localization. We propose a radar filter that keeps only the
strongest reflections per-azimuth that exceeds the expected noise level. The
filtered radar data is used to incrementally estimate odometry by registering
the current scan with a nearby keyframe. By modeling local surfaces, we were
able to register scans by minimizing a point-to-line metric and accurately
estimate odometry from sparse point sets, hence improving efficiency.
Specifically, we found that a point-to-line metric yields significant
improvements compared to a point-to-point metric when matching sparse sets of
surface points. Preliminary results from an urban odometry benchmark show that
our odometry pipeline is accurate and efficient compared to existing methods
with an overall translation error of 2.05%, down from 2.78% from the previously
best published method, running at 12.5ms per frame without need of
environmental specific training.
