Oriented surface points for efficient and accurate radar odometry

TL;DR

This paper introduces a radar odometry method that uses a novel surface point registration technique and a radar filter to achieve efficient, accurate large-scale localization without environmental training.

Contribution

It proposes a new radar filtering and surface modeling approach that improves odometry accuracy and efficiency over existing methods.

Findings

Achieved 2.05% translation error on urban benchmark

Operates at 12.5ms per frame without environment-specific training

Outperforms previous state-of-the-art methods in accuracy and speed

Abstract

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.