A Novel Lightweight Particle Filter for Indoor Localization

In this paper, we describe an infrastructureindependent indoor localization approach for various indoor environments. Our method introduces a novel particle filter implementation that enables the fusion of inertial motion unit sensors, user context, user gait direction, and map information. Due to this novel fusion, it performs localization with up to two orders of magnitude fewer particles than state-of-the-art approaches. Additionally, it extracts map information via existing open services, such as the Open Street Maps and it follows defined standards for the map handling. We evaluated all the components of our method in realtime in off-the-shelf smartphones and we find that it performs a median error of 2.3m, while using only 40 particles instead of 400 or up to 4000 particles that other methods require for the same accuracy.