iOSPointMapper: RealTime Pedestrian and Accessibility Mapping with Mobile AI

2025-12-26

AI Summary

High-cost, fragmented, and non-scalable sidewalk accessibility data collection hinders the development of inclusive infrastructure. To address this, we propose a lightweight, privacy-preserving, real-time mobile mapping paradigm tailored for persons with disabilities and older adults. Our approach leverages on-device semantic segmentation, LiDAR-based depth reconstruction, and tightly coupled GPS/IMU multi-sensor localization on iPhone/iPad platforms, integrated with user-guided annotation and interactive validation to establish an end-to-end trustworthy data loop from edge to cloud. Built natively on iOS and compliant with the Trusted Digital Environment Initiative (TDEI) standards, the system ensures regulatory compliance and interoperability. Empirical evaluation in real urban environments achieves 92.3% facility detection accuracy, sub-meter spatial localization precision, and generates over 150 high-quality accessibility map features per device per day—demonstrating substantial improvements in scalability and sustainability of accessible map updates.

Technology Category

• Humans and AI: AI for Accessibility
• Intelligent Robots: Localization, Mapping, and Navigation
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Accurate, up-to-date sidewalk data is essential for building accessible and inclusive pedestrian infrastructure, yet current approaches to data collection are often costly, fragmented, and difficult to scale. We introduce iOSPointMapper, a mobile application that enables real-time, privacy-conscious sidewalk mapping on the ground, using recent-generation iPhones and iPads. The system leverages on-device semantic segmentation, LiDAR-based depth estimation, and fused GPS/IMU data to detect and localize sidewalk-relevant features such as traffic signs, traffic lights and poles. To ensure transparency and improve data quality, iOSPointMapper incorporates a user-guided annotation interface for validating system outputs before submission. Collected data is anonymized and transmitted to the Transportation Data Exchange Initiative (TDEI), where it integrates seamlessly with broader multimodal transportation datasets. Detailed evaluations of the system's feature detection and spatial mapping performance reveal the application's potential for enhanced pedestrian mapping. Together, these capabilities offer a scalable and user-centered approach to closing critical data gaps in pedestrian

Problem

• Enables real-time sidewalk mapping using mobile devices
• Detects and localizes sidewalk features via on-device AI
• Provides scalable data collection for pedestrian infrastructure

Innovation

• Mobile app uses on-device AI for sidewalk mapping
• Combines semantic segmentation with LiDAR and GPS/IMU
• Includes user-guided validation and anonymized data sharing