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This paper extends 3D Gaussian-based radar scan matching by incorporating Radar Cross Section (RCS) as a fourth dimension, modeling its physical behavior to enrich scene representation. They derive a novel RCS observation model and integrate it into the scan matching pipeline. Experiments demonstrate that incorporating RCS improves the accuracy and robustness of scan matching, particularly in challenging environments.
Overlooked no more: RCS data from 4D radar significantly boosts scan matching accuracy by explicitly modeling its physical behavior.
4D millimeter-wave (mmWave) radars are increasingly used in robotics, as they offer robustness against adverse environmental conditions. Besides the usual XYZ position, they provide Doppler velocity measurements as well as Radar Cross Section (RCS) information for every point. While Doppler is widely used to filter out dynamic points, RCS is often overlooked and not usually used in modeling and scan matching processes. Building on previous 3D Gaussian modeling and scan matching work, we propose incorporating the physical behavior of RCS in the model, in order to further enrich the summarized information about the scene, and improve the scan matching process.