Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction
arXiv:2607.05449v1 Announce Type: cross 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
The continuous advancements in AI and sensing technologies, combined with the increasing need for robust perception in autonomous systems, drive innovation in areas like UWB-based work-zone reconstruction.
Improved perception systems for intelligent transportation enhance safety, efficiency, and the reliability of autonomous operations in complex environments.
This technology provides a more accurate and resilient method for environmental perception, particularly where traditional GPS or visual systems are limited by non-line-of-sight issues.
- · Autonomous vehicle developers
- · Intelligent transportation systems (ITS)
- · Construction and logistics sectors
- · UWB sensing hardware manufacturers
- · Legacy GPS-reliant perception systems
- · Systems highly vulnerable to non-line-of-sight errors
More reliable and safer operation of autonomous vehicles and machinery in challenging environments.
Reduced operational costs and increased efficiency in work zones due to enhanced automation and fewer human interventions.
Acceleration of fully autonomous systems deployment in complex industrial and public infrastructure settings, reshaping urban mobility and logistics.
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Read at arXiv cs.AI