For Wi-Fi positioning location fingerprinting is one of the most commonly employed localization technique. To achieve an acceptable level of positioning accuracy on the few meter level, i.e., to provide at least room resolution in buildings, such an approach is very labour consuming as it requires a high density of reference points. Thus, the novel approach developed aims at a significant reduction of workload for the training phase. The basic idea is to intelligently choose waypoints along possible users’ trajectories in the indoor environment. These waypoints are termed intelligent checkpoints (iCPs) and serve as reference points for the fingerprinting localization approach. They are selected along the trajectories in such a way that they define a logical sequence with their ascending order. Thereby, the iCPs are located, for instance, at doors at entrances to buildings, rooms, along corridors, etc., or in low density along the trajectory to provide a suitable absolute user localization. Continuous positioning between these iCPs is obtained with the help of the smartphones’ inertial sensors. While walking along a selected trajectory to the destination a dynamic recognition of the iCPs is performed and the drift of the inertial sensors is reduced as the iCP recognition serves as absolute position update. Conducted experiments in a multi-storey office building have shown that positioning accuracy of around 2.0 m are achievable which goes along with a reduction of workload by three quarter using this novel approach. The iCP concept and performance are presented and demonstrated in this paper.
Enhancing Positioning Accuracy in Urban Terrain by Fusing Data from a GPS Receiver, Inertial Sensors, Stereo-Camera and Digital Maps for Pedestrian Navigation
