Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction

• URL: http://arxiv.org/abs/2607.05449v1
• Date: Sun, 05 Jul 2026 07:14:11 GMT
• Title: Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction
• Abstract summary: We present GAIA, a geometry-aware, infrastructure-anchored learning framework that couples temporal range modeling with latent anchor distances estimation.<n>GAIA preserves range denoising as the supervised task while orienting the learned robustness toward boundary-consistent reconstruction.<n>These results show that geometry-aware range denoising provides an effective path toward spatially coherent work-zone reconstruction.
• Score: 19.641189530346335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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