A robust method for indoor localization using Wi-Fi and SURF based image fingerprint registration

Abstract

This paper introduces a method for the accurate indoor localization for mobile users when they are surrounded by unknown environments in places like airports, hospitals, libraries, museums, and supermarkets. Our system makes use of the combined data comprising two kinds: indoor Wi-Fi signals and the images of surroundings taken by users. We use Wi-Fi registration based on IEEE 802.11 to determine Access Point location according to the Received Signal Strength (RSS) as a distance function. Our fingerprinting method gives probability of signal strengths histogram at a given location. We use the Received Signal Strength Indicator (RSSI) data in to data collection to determine the overage area estimation and the mode of RSSI in localization. Next, we utilize the Speed Up Robust Features (SURF) descriptor to match the user-captured images with the image repository containing pre-captured images of the environment. Our method is accurate and less time consuming as compared to different approaches. © 2014 Springer International Publishing Switzerland.