Calibration-free radiomap construction based on graph map matching

Global deployment of Wi-Fi radiomaps is the key to high-accuracy, low-cost positioning systems that can provide indoor positioning at scale. Data-driven approaches to automate the creation of these radiomaps using unlabelled data are becoming increasingly popular. However, many systems rely on highly accurate indoor maps and low-noise crowdsourced trajectories. For deployment at scale, the concerns of inaccuracies in both the map and the data domains must be considered. In this research, we propose a 2-stage approach for calibration-free radiomap construction consisting of unsupervised trajectory alignment followed by a map matching optimisation stage. We evaluate the crowdsourced radiomap quality by utilizing an extensive ground truth data set consisting of thousands of estimates spanning 26 floors in 4 venues. We run positioning based on a singleshot Wi-Fi positioning algorithm (WKNN) and a particle filter-based recursive state estimation algorithm (PF). These algorithms achieve a median positioning error of 2.2 m and 1.3 m in an office environment, respectively. In larger mall environments, the average median errors are 8.1m (WKNN) and 4.6 m (PF).