iOSPointMapper: RealTime Pedestrian and Accessibility Mapping with Mobile AI

TL;DR

iOSPointMapper is a mobile app that uses on-device AI and sensors on iPhones and iPads to map sidewalks and pedestrian features in real-time, aiming to improve accessibility data collection.

Contribution

The paper introduces iOSPointMapper, a novel mobile system combining semantic segmentation, LiDAR, and fused GPS/IMU data for real-time pedestrian infrastructure mapping on personal devices.

Findings

High accuracy in sidewalk feature detection

Effective real-time mapping on iOS devices

Seamless data integration with transportation datasets

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