Sensor Fusion based Indoor Positioning with iBeacons

Abstract

Regarding outdoor positioning, GPS has become de facto standard, however, there is no equivalent system in the indoor scenario. The literature considers promising solutions in the domain of indoor positioning, however, it does not use widely available hardware. This thesis considers a new map-based approach on indoor positioning that assumes the availability of an indoor map and affordable on-board sensors that are available on modern, off-the-shelf smartphones. By implementing a map matching algorithm, it is possible to reduce the uncertainty, arising from the use of affordable sensors, and improve the accuracy of the indoor positioning system. A pilot of the design has been implemented and the results from the validation showed an average improvement of 17.8 % in accuracy and also an average improvement of 3.33 % in room correctness compared to the same design without including the indoor map. However, developers who choose to implement the map-based approach should be aware of the increased costs in computational demand and power consumption of the design when developing applications.