BFAR-Bounded False Alarm Rate detector for improved radar odometry estimation

TL;DR

This paper introduces BFAR, a novel noise filtering detector for radar data that enhances radar odometry accuracy by optimally balancing false alarms and true detections, outperforming traditional methods.

Contribution

The paper proposes BFAR, a new detector that combines CFAR and fixed thresholding with learned parameters, improving radar odometry accuracy.

Findings

Reduced odometry errors by 12.5%

Improved noise filtering in radar data

Enhanced localization in low visibility conditions

Abstract

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%.