# V2I Work Zone Geometry Reconstruction with Pose-Conditioned UWB Range Denoising

arXiv: https://arxiv.org/abs/2606.00119 · HTML: https://mlsanity.com/paper/2606.00119/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 8 of 8)

- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones](https://mlsanity.com/paper/2406.07661/) (2024)
- [Robust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge](https://mlsanity.com/paper/2011.14684/) (2020)
- [A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform](https://mlsanity.com/paper/2305.18208/) (2023)
- [Are Work Zones and Connected Automated Vehicles Ready for a Harmonious Coexistence? A Scoping Review and Research Agenda](https://mlsanity.com/paper/2102.00087/) (2021)
- [Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories](https://mlsanity.com/paper/2301.09194/) (2023)
- [On the Robotic Uncertainty of Fully Autonomous Traffic: From Stochastic Car-Following to Mobility-Safety Trade-Offs](https://mlsanity.com/paper/2309.12611/) (2023)

## Built on by (most cited first, 1 of 1)

- [Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction](https://mlsanity.com/paper/2607.05449/) (2026)

## Abstract (arXiv)

Reliable work zone mapping is important for connected and autonomous vehicles (CAVs) to navigate safely and smoothly through work zone areas. Cone-mounted ultra-wideband (UWB) roadside units (RSU) offer a cost-effective way for work zone layout inference, as roadside anchors and vehicle tags provide direct vehicle-to-infrastructure (V2I) range constraints for work zone geometry reconstruction. However, UWB range estimation is degraded by bursty outliers, non-line-of-sight (NLOS) errors, arbitrary anchor-ordering issues, and vehicle pose uncertainties in practical field deployments. To address these challenges, this study proposes a pose-conditioned, permutation-equivariant predictive denoiser for multi-anchor UWB ranging. The model employs shared anchor-wise temporal prediction to capture range dynamics, symmetric set aggregation to handle unordered and missing anchors, and pose-conditioned residual decoding to incorporate vehicle motion as a geometric prior. A two-stage training strategy first learns prediction from observed ranges, and then fine-tunes the denoiser with NLOS-weighted supervision. The method is evaluated on rare real-world V2I UWB field data collected with a CAV, as well as on controlled large-scale simulation benchmarks for ablative insights. Results show that the proposed method substantially improves range accuracy, cone localization, and work zone geometry reconstruction in challenging NLOS-dominated regimes, remains robust to anchor re-indexing and moderate anchor dropout, and reduces measurement-weighted field MSE by 66.9% relative to the raw input.