A Novel Lightweight Particle Filter for Indoor Localization

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