# OmniPath: A Multi-Modal Agentic Framework for Auditing Wheelchair Accessibility

arXiv: https://arxiv.org/abs/2606.24129 · HTML: https://mlsanity.com/paper/2606.24129/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 42 references that are in the map.
- First posted on arXiv 23 Jun 2026.

## Builds on (most cited first, 6 of 6)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Safe Road-Crossing by Autonomous Wheelchairs: a Novel Dataset and its Experimental Evaluation](https://mlsanity.com/paper/2403.08984/) (2024)
- [Automated GIS-Based Framework for Detecting Crosswalk Changes from Bi-Temporal High-Resolution Aerial Images](https://mlsanity.com/paper/2406.09731/) (2024)
- [Automated Road Safety: Enhancing Sign and Surface Damage Detection with AI](https://mlsanity.com/paper/2407.15406/) (2024)
- [Development of a Modular Real-time Shared-control System for a Smart Wheelchair](https://mlsanity.com/paper/2211.14711/) (2022)
- [Towards a real-time distributed feedback system for the transportation assistance of PwD](https://mlsanity.com/paper/2406.06154/) (2024)

## Abstract (arXiv)

For a wheelchair user, a standard blue line on a map is often a broken promise. While platforms like OpenStreetMap (OSM) successfully capture where a path is, they frequently fail to convey how it physically feels to travel on it. This information barrier is problematic for wheelchair users. To solve this issue, we present OmniPath, a system that moves from passive mapping to proactive environmental auditing. Our framework fuses the network topology of OSM with the submeter precision of high-density aerial LiDAR (USGS 3DEP) to create a high-fidelity 3D model of the pedestrian environment. Rather than simply routing a user, our agent virtually traverses the network, analyzing the surface in 0.5 meter increments. It rigorously quantifies physical friction points specifically running slope, cross slope, and vertical discontinuities against ADA compliance standards, calculating a weighted severity score to categorize hazards from ``Mild'' to ``Critical.'' To ensure real world reliability, we validated the system against 200 physical ground truth field surveys across the National Mall using stratified random sampling. The framework demonstrated strong diagnostic reliability for high-severity hazards, achieving F1-scores of 0.60 for Severe and 0.58 for critical categories. By automating this micro-scale inspection, OmniPath identifies the ``invisible'' barriers that standard maps miss, effectively transforming a static dataset into accessibility data source that anticipates accessibility challenges before the user ever leaves home.