OmniPath: A Multi-Modal Agentic Framework for Auditing Wheelchair Accessibility
Quick Answer
OmniPath is a proactive auditing framework that combines OpenStreetMap with high-density aerial LiDAR to create a detailed 3D model of pedestrian environments.
Quick Take
It quantifies accessibility hazards for wheelchair users by analyzing surface conditions and achieving F1-scores of 0.60 for severe and 0.58 for critical hazards, transforming static maps into actionable accessibility data.
Key Points
- OmniPath analyzes surfaces in 0.5 meter increments for precise accessibility assessment.
- It identifies hazards based on ADA compliance, categorizing them from 'Mild' to 'Critical.'
- Validated against 200 ground truth surveys, showing strong reliability for severe hazards.
- Transforms static datasets into proactive accessibility data for wheelchair users.
- Addresses the information gap in traditional mapping for wheelchair accessibility.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Comments: | 10 pages, 13 figures. Submitted to IEEE COMPSAC 2026. OmniPath: A Multi-Modal Agentic Framework for Auditing Wheelchair Accessibility |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.24129 [cs.AI] |
| (or arXiv:2606.24129v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24129 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Asm Mobarak Hossain [view email]
[v1]
Tue, 23 Jun 2026 04:23:44 UTC (7,610 KB)
— Originally published at arxiv.org
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