4D Radar Gaussian Modeling and Scan Matching with RCS
For robotics applications using 4D mmWave radar, this work enhances scan matching accuracy by leveraging RCS, a previously underutilized signal attribute.
This work proposes a 4D radar Gaussian model that incorporates Radar Cross Section (RCS) information, building on prior 3D Gaussian modeling, to improve scan matching. The approach enriches scene representation and achieves more accurate scan matching compared to methods that ignore RCS.
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.