Abstract
This paper presents a numerical approach to the pedestrian map-matching problem using building plans. The proposed solution is based on a sequential Monte Carlo method, so called particle filtering. This algorithm can be adapted for implementation on real-time pedestrian navigation systems using low-cost MEMS gyroscopes and accelerometers as dead-reckoning sensors. The algorithm reliability and accuracy performance was investigated using simulated data typical for pedestrians walking inside building. The results show that this map-aided dead reckoning system is able to provide accurate indoor positioning for long periods of time without using GPS data.
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