Door Transition Detection for Long-Term Stability in Pedestrian Indoor Positioning

Many smartphone-based indoor positioning systems rely on pedestrian dead reckoning for fine-grained position tracking. In this paper, we show how one of its major shortcomings – the accumulation of errors over time – can be effectively overcome in a navigation setting by detecting door transitions along the route. Using only the sensors included in a handheld smartphone and different on-device machine learning techniques, door transitions can be classified correctly in up to 86% of all cases. The system runs in real-time on current smartphones and – when integrated into our baseline particle filter – improves overall positioning performance significantly, as the subsequent evaluation on a realistic navigation data set shows. A detailed analysis of several edge cases illustrates the concept and provides insights into the remaining challenges.