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

At present, pedestrian navigation systems using smartphones have become common in daily activities. For their ubiquitous, accurate, and reliable services, map information collection is essential for constructing comprehensive spatial databases. 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 due to 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, experiments in various indoor scenarios were conducted. The results demonstrate that the proposed method effectively mitigates positional errors with an average drift correction of 3.15 m, calculated via the Haversine formula. Geometric validation using point cloud analysis showed high registration accuracy, which translated to a 100% task completion rate and an average navigation time of 124.5 s among participants. Furthermore, usability testing using the System Usability Scale (SUS) yielded an average score of 96.5, categorizing the user interface as ’Best Imaginable’. These quantitative findings substantiate that the integration of 360° imaging and photogrammetric correction significantly enhances navigation reliability and user satisfaction compared with previous sensor fusion approaches.