Pedestrian indoor positioning using smartphone multi-sensing, radio beacons, user positions probability map and IndoorOSM floor plan representation

Position awareness in unknown and large indoor spaces represents a great advantage for people, everyday pedestrians have to search for specific places, products and services. In this work a positioning solution able to localize the user based on data measured with a mobile device is described and evaluated. The position estimate uses data from smartphone built-in sensors, WiFi (Wireless Fidelity) adapter and map information of the indoor environment (e.g. walls and obstacles). A probability map derived from statistical information of the users tracked location over a period of time in the test scenario is generated and embedded in a map graph, in order to correct and combine the position estimates under a Bayesian representation. PDR (Pedestrian Dead Reckoning), beacon-based Weighted Centroid position estimates, map information obtained from building OpenStreetMap XML representation and probability map users path density are combined using a Particle Filter and implemented in a smartphone application. Based on evaluations, this work verifies that the use of smartphone hardware components, map data and its semantic information represented in the form of a OpenStreetMap structure provide 2.48 meters average error after 1,700 travelled meters and a scalable indoor positioning solution. The Particle Filter algorithm used to combine various sources of information, its radio WiFi-based observation, probability particle weighting process and the mapping approach allowing the inclusion of new indoor environments knowledge show a promising approach for an extensible indoor navigation system.