4D Radar Gaussian Modeling and Scan Matching with RCS

TL;DR

This paper introduces a novel approach to 4D radar scan matching by incorporating Radar Cross Section (RCS) modeling, enhancing scene understanding and robustness in robotics applications.

Contribution

It extends previous 3D Gaussian modeling by integrating RCS information, which is often overlooked, to improve scan matching accuracy.

Findings

RCS modeling enriches scene representation in radar data.

Incorporating RCS improves scan matching robustness.

The method leverages physical RCS behavior for better scene understanding.

Abstract

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.