4D Radar Gaussian Modeling and Scan Matching with RCS
Fernando Amodeo, Luis Merino, Fernando Caballero
TLDR
This paper introduces a 4D Gaussian modeling approach that incorporates Radar Cross Section (RCS) to improve scan matching for mmWave radars.
Key contributions
- Proposes a 4D Gaussian model integrating Radar Cross Section (RCS) for mmWave radars.
- Utilizes RCS's physical behavior to enrich scene information and improve data representation.
- Enhances scan matching accuracy by leveraging previously overlooked RCS data.
Why it matters
4D radars offer robust sensing but often neglect valuable RCS data. This work unlocks RCS's potential, leading to more detailed scene understanding and improved robotic navigation in challenging environments.
Original 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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