Optimization-Based Wi-Fi Radiomap Construction for Multifloor Indoor Positioning

Wi-Fi radiomaps offer a convenient solution to position users in GNSS denied areas, however the process of obtaining them through manual surveys is expensive and time consuming, while the use of crowdsourced uncalibrated data is noisier and lacks coverage. In this work we propose to calibrate the crowdsourced data using an optimization framework based on GraphSLAM using GPU processing, in order to automatically cluster the data into floors and perform 2D graph optimization and alignment, combined with opportunistic map matching when floor plans are available. Using our approach we are able to generate radio maps of multi floor buildings, processing data from thousands of user and millions of fingerprints. In our experiments we are able to produce a radiomap with a floor accuracy above 98% and median positioning error of 7.63 m in a 14 floor shopping mall (Joy City) in Beijing, China using single shot WKNN for localization.