用于 UWB 传感和工作区重建的几何感知基础设施锚定降噪器

arXiv:2607.05449v1 Announce Type: new 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.然而，室外 UWB 测距通常会因非视距传播、突发噪声和长尾误差而降低性能，这可能会扭曲下游空间重建。 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 保留范围去噪作为监督任务，同时将学习距离定向到边界一致重建。 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.这些结果表明，几何感知范围去噪为空间相干工作区重建提供了一条有效途径。