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TY  - THES
AU  - Real Ehrlich, Catia Maria
TI  - Echtzeit-Positionierung für Fußgänger innerhalb von Gebäuden auf Basis von Smartphone-Sensoren
VL  - 69
PB  - Rheinisch-Westfälische Technische Hochschule Aachen
VL  - Dissertation
CY  - Aachen
M1  - RWTH-2018-231487
T2  - Veröffentlichung des Geodätischen Instituts der Rheinisch-Westfälischen Technischen Hochschule Aachen
SP  - 1 Online-Ressource (XXVIII, 260 Seiten) : Illustrationen, Diagramme
PY  - 2018
N1  - Veröffentlicht auf dem Publikationsserver der RWTH Aachen University 2019
N1  - Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2018
AB  - Knowing the position of an object or person inside buildings would be useful for many fields of application such as pedestrian navigation (e.g. visitor guidance), facility management (e.g. supporting maintenance), disaster management (e.g. localization of rescue teams) or mobile robotics. In outdoor environments, satellite-based positioning systems (Global Navigation Satellite System, GNSS) are typically used for localization purposes. Inside of buildings, the satellite signals are reflected or attenuated by building components (e.g. walls or ceilings), consequently, it is hardly feasible for continuous and reliable indoor positioning. For selected applications, the positioning inside of buildings is already based on various technologies (e.g. WLAN, RFID, ultrasound or UWB), but a standard solution does not exist. Many indoor positioning systems are only suitable for specific applications or can only be used under certain conditions, for example with additional infrastructures and / or sensor technology. The smartphone, a widely used low-cost multi sensor system, appears to be a promising platform for indoor localization for the mass market and is increasingly coming into focus. Today's end devices have a variety of sensors that can be used for indoor positioning with low technical effort. In this work, a real-time indoor pedestrian tracking system based on smartphone sensors is presented which is independent from any additional infrastructure in the basic set up. The idea is to use the sensors embedded in the smartphones to estimate the 2,5D position in real-time with a positional deviation of less than five meters. For this purpose, measurements concerning the barometric altitude estimation are carried out in order to derive the floor on which the user resides. After the floor determination, the 2D position is estimated using the principle of dead reckoning based on the users' movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information such as building models is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas. For the fusion of different information (linear and nonlinear) a sequential Monte Carlo method is used. In addition to the actual building structure, information from other positioning systems based on e.g. BLE beacons, magnetic anomalies or WLAN access points, is integrated to support or optimize the real-time position estimation respectively.
LB  - PUB:(DE-HGF)11 ; PUB:(DE-HGF)3
DO  - DOI:10.18154/RWTH-2018-231487
UR  - https://publications.rwth-aachen.de/record/751606
ER  -