Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction
GAIA is a geometry-aware, infrastructure-anchored learning framework that addresses non-line-of-sight propagation, burst noise, and long-tail errors in UWB ranging by combining temporal range modeling, latent anchor-layout estimation, and deterministic distance projection. On a real-world outdoor UWB dataset, GAIA reduces range MSE by 18.4% and improves polygon IoU by 15.5% over PoseMLP, enabling accurate work-zone reconstruction.
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[Submitted on 5 Jul 2026]
Title:Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction
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Abstract:Accurate work-zone geometry perception is critical for intelligent transportation systems, and ultra-wideband sensing offers a low-cost approach for infrastructure-aided reconstruction. However, outdoor UWB ranging is often degraded by non-line-of-sight propagation, burst noise, and long-tail errors, which can distort downstream spatial reconstruction. We present GAIA, a geometry-aware, infrastructure-anchored learning framework that couples temporal range modeling with latent anchor-layout estimation and deterministic distance projection. GAIA preserves range denoising as the supervised task while orienting the learned distances toward boundary-consistent reconstruction. We evaluate GAIA on a real-world outdoor UWB dataset with synchronized UWB, GNSS, and IMU measurements, and further test robustness using a real-data-calibrated stress-test simulator. GAIA achieves the lowest overall range MSE and highest polygon IoU among evaluated filtering-based and learning-based baselines, reducing MSE by 18.4% and improving polygon IoU by 15.5% over PoseMLP. These results show that geometry-aware range denoising provides an effective path toward spatially coherent work-zone reconstruction.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
Cite as: arXiv:2607.05449 [cs.LG]
(or arXiv:2607.05449v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.05449
arXiv-issued DOI via DataCite
Submission history
From: Weizhe Tang [view email] [v1] Sun, 5 Jul 2026 07:14:11 UTC (616 KB)
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