Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

ABSTRACT

The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS). However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today’s devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multi-sensor smartphones and easy-to-install local positioning systems. For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information 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 pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented.

KEYWORDS:

• Indoor positioning
• MEMS
• recursive Bayesian estimation
• particle filter
• sensor fusion
• Bluetooth beacons
• WLAN fingerprinting
• magnetic anomalies

Additional information

Notes on contributors

Catia Real Ehrlich

Catia Real Ehrlich is a research assistant at the Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems at the RWTH Aachen University, Germany. Her research is focused on developing a pedestrian indoor positioning system using smartphone sensors and easy-to-install local positioning systems, such as WLAN fingerprinting and RSS based Bluetooth localization. The goal is to determine the position of the smartphone user in real-time.

Jörg Blankenbach

Jörg Blankenbach is full professor and director of the Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems at the RWTH Aachen University, Germany. He received his doctoral degree (Dr.-Ing.) from Technische Universität Darmstadt, Germany. His research interests are indoor positioning using multiple technologies, 3d data capturing and digital modelling (BIM) and distributed geospatial information systems.