Flash-Loc: Flashing mobile phones for accurate indoor localization

Accurate indoor localization is a key enabling technology for numerous applications such as indoor navigation, mobile social networking, and augmented reality. Despite major effort from the research community, state-of-the-art indoor localization performance remains unsatisfactory. Current approaches using radio frequency entail tedious site surveys and have limited accuracy. While vision-based localization techniques are promising, they struggle with human recognition and changing environments. This paper proposes Flash-Loc, an accurate indoor localization system leveraging flashes of light to localize people carrying mobile phones in areas with deployed surveillance cameras. A person's mobile phone emits a sequence of flashes that uniquely “represents” the person from the cameras' view. Flash-Loc develops three key mechanisms that distinguish people while avoiding long irritating flashes: adaptive-length flash coding, pulse width modulation based flash generation, and image subtraction based flash localization. Further, we design a system in which Flash-Loc cooperates with fingerprinting and dead reckoning for accurate human localization. We implement Flash-Loc on commercial off-the-shelf equipment. Our real-world experiments show Flash-Loc achieves accurate indoor localization by itself and in cooperation with other localization technology. In particular, Flash-Loc can localize a user 45 m away from the camera with sub-meter accuracy.

I. Introduction

Accurate indoor localization enables a wide range of important applications including indoor navigation, mobile social networking, augmented reality, etc. [1], [2] Recently, significant effort has been devoted to this important enabling technology [3]–[10]. In addition, indoor localization is a promising global market for industry [11], which is estimated to exceed $4 billion by 2019 [12]. However, even with major effort from the research community and strong industrial demand, indoor localization remains very challenging and state-of-the-art performance remains unsatisfactory [7].