Abstract
Indoor positioning could provide interesting services and applications. As one of the most popular indoor positioning methods, location fingerprinting determines the location of mobile users by matching the received signal strength (RSS) which is location dependent. However, fingerprinting-based indoor positioning requires calibration and updating of the fingerprints which is labor-intensive and time-consuming. In this paper, we propose a visual-based approach for the construction of radio map for anonymous indoor environments without any prior knowledge. This approach collects multi-sensors data, e.g. video, accelerometer, gyroscope, Wi-Fi signals, etc., when people (with smartphones) walks freely in indoor environments. Then, it uses the multi-sensor data to restore the trajectories of people based on an integrated structure from motion (SFM) and image matching method, and finally estimates location of sampling points on the trajectories and construct Wi-Fi radio map. Experiment results show that the average location error of the fingerprints is about 0.53 m.
Highlights
With the great increment of mobile devices, people pay more attention to mobile navigation services
This study aims to provide an approach for automatic construction of radio map based on the combination of pedestrian dead reckoning (PDR) and image matching methods
This study aims to provide an approach for the construction of radio map in indoor environments using three steps:
Summary
With the great increment of mobile devices (e.g. smartphone), people pay more attention to mobile navigation services. The commonly used indoor positioning technologies include WIFI (Bahl, 2000), Bluetooth (Kotanen, 2003), radio-frequency identification (RFID) (Ni, 2004), and Ultrawide Band (UWB) (Fontana, 2002). Most of these methods are limited availability as the need for pre-installed infrastructure and built radio map (Liu, 2007). This study aims to provide an approach for automatic construction of radio map based on the combination of pedestrian dead reckoning (PDR) and image matching methods. Integrating the motion information from video to PDR can increase the location accuracy of radio map. We use these data to restore the trajectories and construct Wi-Fi radio map
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