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

Computer Science > Artificial Intelligence [Submitted on 23 Jun 2026] OmniPath: A Multi-Modal Agentic Framework for Auditing Wheelchair Accessibility ASM Mobarak Hossain, Nadim Mahmud, Vaskar Raychoudhury, Md Osman Gani 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 Focus to learn more Submission history From: Asm Mobarak Hossain [view email] [v1] Tue, 23 Jun 2026 04:23:44 UTC (7,610 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)