Human Data Understanding - Sensors, Models, Knowledge, Bd. 1
This thesis examines pedestrian 3D indoor localization and navigation using commodity smartphones: A desirable target platform, always at hand and equipped with a multitude of sensors. The IMU (accelerometer, gyroscope, magnetometer) and barometer allow for pedestrian dead reckoning, that is, estimating relative location changes. Absolute whereabouts can be determined via Wi-Fi, an infrastructure present within most public buildings, or by using Bluetooth Low Energy Beacons as inexpensive supplement. The building's 3D floorplan not only enables navigation, but also increases accuracy by preventing impossible movements, and serves as a visual reference for the pedestrian. All aforementioned information is fused by recursive density estimation based on a particle filter.
The conducted experiments cover both, theoretical backgrounds and real-world use-cases. All discussed approaches utilize the infrastructure found within most public buildings, are easy to set up, and maintain. Overall, this thesis results in an indoor localization and navigation system that can be easily deployed, without requiring any special hardware components.
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