Integrated Raw Data Collection and Validation System for Indoor Positioning

Recent indoor positioning technologies have evolved into AI-based hybrid positioning systems that utilize wireless communication devices, IMU sensors, cameras, and other devices embedded in smartphones, and the amount of data used for positioning is increasing. This paper introduces a raw data collection and validation system that supports more accurate and easier data collection by the collector during the expanding data collection stage, and supports the validation of the collected results on-site. The proposed system minimizes the collector’s position error using indoor maps and PDR technology, and synchronizes the collected raw data with the estimated position of the collector to create high-quality raw data. In addition, the raw data validation system supports the collector to directly verify the quality of the collected data by simulating the performance of the positioning algorithm and the quality of the raw data.