提出GAIA框架，用基础设施锚点提升超宽带测距精度，实现更准确的施工区重建。
Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction
- 融合时序测距建模与隐式锚点布局估计，实现几何感知去噪
- 实测数据上测距均方误差降低18.4%，多边形交并比提升15.5%
- 适合需要高精度空间重建的智能交通与工地感知场景
精准的施工区几何感知对智能交通系统至关重要，超宽带（UWB）传感为基于基础设施的重建提供了低成本方案。然而，户外UWB测距常受非直视传播、突发噪声和长尾误差影响，导致下游空间重建失真。本文提出GAIA——一种几何感知、基础设施锚定的学习框架，将时序测距建模与潜在锚点布局估计相结合，并引入确定性距离投影机制。GAIA以测距去噪作为监督任务，同时引导学习到的距离向边界一致的重建结果对齐。我们在包含同步UWB、GNSS与IMU测量的真实室外数据集上评估了GAIA，并通过真实数据校准的压力测试模拟器验证其鲁棒性。在所有对比的基于滤波和基于学习的基线中，GAIA实现了最低的整体测距均方误差（MSE）与最高的多边形交并比（IoU），相比PoseMLP，MSE降低18.4%，多边形IoU提升15.5%。结果表明，几何感知的测距去噪是实现空间一致性施工区重建的有效路径。
原文摘要 · Abstract (English)
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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