4D Radar Gaussian Modeling and Scan Matching with RCS

Fernando Amodeo; Luis Merino; Fernando Caballero

cs.ROApr 16, 2026

4D Radar Gaussian Modeling and Scan Matching with RCS

Authors: Fernando Amodeo, Luis Merino, Fernando Caballero

Organizations: Service Robotics Laboratory – Universidad Pablo de Olavide (Seville), Spain

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.

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Registering the 4D Millimeter Wave Radar Point Clouds Via Generalized Method of Moments

Xingyi Li, Han Zhang, Ziliang Wang, Yukai Yang+1

School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China · Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China · Department of Statistics, Uppsala University, Sweden

4D millimeter wave radars (4D radars) are new emerging sensors that provide point clouds of objects with both position and radial velocity measurements. Compared to LiDARs, they are more affordable and reliable sensors for robots' perception under extreme weather conditions. On the other hand, point cloud registration is an essential perception module that provides robot's pose feedback information in applications such as Simultaneous Localization and Mapping (SLAM). Nevertheless, the 4D radar point clouds are sparse and noisy compared to those of LiDAR, and hence we shall confront great challenges in registering the radar point clouds. To address this issue, we propose a point cloud registration framework for 4D radars based on Generalized Method of Moments. The method does not require explicit point-to-point correspondences between the source and target point clouds, which is difficult to compute for sparse 4D radar point clouds. Moreover, we show the consistency of the proposed method. Experiments on both synthetic and real-world datasets show that our approach achieves higher accuracy and robustness than benchmarks, and the accuracy is even comparable to LiDAR-based frameworks.

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Abstract

Explore similar work

Registering the 4D Millimeter Wave Radar Point Clouds Via Generalized Method of Moments

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