Pedestrian Localisation inside buildings based on multi-sensor smartphones

Automatic real-time localisation of people inside of buildings (indoor) is a huge challenge. For demanding applications of mobile services, different sensors such as WLAN, RFID, UWB or ultrasound are currently used for real-time indoor positioning. However, a general solution like GNSS for outdoor environments still does not exist for indoor areas. Most systems are only suitable for specific applications or are deployed under certain conditions, since additional infrastructures and/or sensory mechanisms are needed. Smartphones, as widespread low-cost multi sensor device, seem to be a promising platform for mass-market indoor localisation applications. In order to determine the three-dimensional position, several integrated navigation sensors can be utilised: 3-axis accelerometer, 3-axis magnetic field sensor, 3-axis gyroscope or barometer. Furthermore, prevalent wireless technologies such as WLAN, Bluetooth, or GNSS are embedded into smartphones and can also be used. In this paper, an approach for real-time smartphone- based pedestrian indoor localisation is presented. The novelty of this approach refers to further investigations towards a robust real-time pedestrian indoor localisation inside of buildings based on multi-sensor smartphones. Several technologies contributing to higher precision and robustness are included and evaluated. For the fusion of different sensor data, an extended algorithm based on Sequential Monte Carlo methods is presented, which also integrates building models and external positioning systems.