Realtime Indoor Localization on Smartphones by Multi-Grained Grid-Based Filters

Accurate indoor localization has attracted much research interest in the past decades. Utilizing existing WiFi infrastructures and inertial measurement units on smartphones is a promising method for indoor localization. In this paper, we propose a multi-grained grid-based filter to jointly estimate the location and the heading direction which can efficiently use map information for localization. Our method calculates the transition probability of location on fine-grained grids and only keeps location probabilities on coarse grids. The on/off model and the wall attenuation factor model are applied to generate observation likelihood from WiFi RSSI measurements. We implemented the algorithm on smartphones and it can run in real time. Experimental results have shown that the root mean squared error of the proposed method is 1.46m and the 90th percentile error is 2.18m.