DiffRadar: Differentiable Physics-Aware Radar SLAM with Gaussian Fields
DiffRadar is a real-time radar SLAM system that models radar observations as a differentiable, physics-aware Gaussian field rather than discrete scans. It achieves substantial reductions in trajectory error on benchmarks, especially in feature-poor corridor motion, more than doubles map consistency, and maintains real-time performance at 70 FPS.
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[Submitted on 14 Jul 2026]
Title:DiffRadar: Differentiable Physics-Aware Radar SLAM with Gaussian Fields
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Abstract:Radar sensing is increasingly used in mobile systems because it operates reliably under poor lighting, adverse weather, and privacy-sensitive settings where cameras and LiDAR often fail. However, most existing radar SLAM systems estimate motion through scan matching on discretized radar heatmaps, which breaks geometric continuity and fails to capture key radar sensing properties, often leading to unstable pose estimation and degraded mapping in regenerate or dynamically changing environments. We present DiffRadar, a real-time radar SLAM system that models radar observations as a differentiable, physics-aware Gaussian field rather than discrete scans. DiffRadar represents the scene as anisotropic Gaussian primitives and renders radar measurements in range-azimuth and Doppler-azimuth spaces through a differentiable radar forward model, enabling joint optimization of robot pose and scene structure directly from radar measurements. We implement DiffRadar on commodity FMCW radar hardware and evaluate it on both the public Radarize benchmark and a controlled stress-test suite that targets common radar SLAM failure modes, including corridor degeneracy, motion regime transitions, dynamic clutter, and long-horizon loop closures. DiffRadar achieves substantial reductions in trajectory error on the benchmark, with especially large gains under feature-poor corridor motion, while more than doubling map consistency and maintaining real-time performance at 70 FPS. These results show that modeling radar observations directly in the signal domain enables substantially more robust and consistent radar-only SLAM for mobile platforms.
Subjects:
Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2607.12265 [cs.RO]
(or arXiv:2607.12265v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.12265
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Gaurav Bagwe [view email] [v1] Tue, 14 Jul 2026 02:08:14 UTC (3,855 KB)
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