  This paper presents a new detector for filtering noise from true detections
in radar data, which improves the state of the art in radar odometry. Scanning
Frequency-Modulated Continuous Wave (FMCW) radars can be useful for
localization and mapping in low visibility, but return a lot of noise compared
to (more commonly used) lidar, which makes the detection task more challenging.
Our Bounded False-Alarm Rate (BFAR) detector is different from the classical
Constant False-Alarm Rate (CFAR) detector in that it applies an affine
transformation on the estimated noise level after which the parameters that
minimize the estimation error can be learned. BFAR is an optimized combination
between CFAR and fixed-level thresholding. Only a single parameter needs to be
learned from a training dataset. We apply BFAR to the use case of radar
odometry, and adapt a state-of-the-art odometry pipeline (CFEAR), replacing its
original conservative filtering with BFAR. In this way we reduce the
state-of-the-art translation/rotation odometry errors from 1.76%/0.5deg/100 m
to 1.55%/0.46deg/100 m; an improvement of 12.5%.
