An Improved Map Information Collection Tool Using 360° Panoramic Images for Indoor Navigation Systems

Nowadays, pedestrian navigation systems using smartphones have become common in daily activities. For their ubiquitous, accurate, and reliable services, the map information collection is essential for constructing a comprehensive spatial database. Previously, we have developed a map information collection tool to extract building information using Google Maps, optical character recognition (OCR), geolocation, and web scraping with smartphones. However, indoor navigation often suffers from inaccurate localization coming from degraded GPS signals inside buildings and Simultaneous Localization and Mapping (SLAM) estimation errors, causing position errors and confusing augmented reality (AR) guidance. In this paper, we present an improved map information collection tool to address this problem. It captures 360° panoramic images to build 3D models, apply photogrammetry-based mesh reconstruction to correct geometry, and georeference point clouds to refine latitude-longitude coordinates. For evaluations of the proposal, we conducted experiments with indoor scenarios and assessed the performance based on positional accuracy with Haversine distance, geometric accuracy (RMSE), AR drift, task completion rates, completion time, and the System Usability Scale (SUS). The results demonstrate significant enhancements of reliability and user satisfaction compared with the previous one.