Oriented surface points for efficient and accurate radar odometry
This work addresses efficient and accurate large-scale localization for autonomous systems, representing an incremental improvement over existing radar odometry methods.
The paper tackles radar odometry by proposing a pipeline that filters radar data to keep strong reflections and uses a point-to-line metric for scan registration, achieving a translation error of 2.05% compared to 2.78% from the previous best method, with processing at 12.5ms per frame.
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.