Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2015

Abstract

Indoor localization is of great importance to a wide range of applications in the era of mobile computing. Current mainstream solutions rely on Received Signal Strength (RSS) of wireless signals as fingerprints to distinguish and infer locations. However, those methods suffer from fingerprint ambiguity that roots in multipath fading and temporal dynamics of wireless signals. Though pioneer efforts have resorted to motion-assisted or peer-assisted localization, they neither work in real time nor work without the help of peer users, which introduces extra costs and constraints, and thus degrades their practicality. To get over these limitations, we propose Argus, an image-assisted localization system for mobile devices. The basic idea of Argus is to extract geometric constraints from crowdsourced photos, and to reduce fingerprint ambiguity by mapping the constraints jointly against the fingerprint space. We devise techniques for photo selection, geometric constraint extraction, joint location estimation, and build a prototype that runs on commodity phones. Extensive experiments show that Argus triples the localization accuracy of classic RSS-based method, in time no longer than normal WiFi scanning, with negligible energy consumption.

Keywords

Indoor Localization; Smart Phone, Photogrammetry

Discipline

Digital Communications and Networking | Information Security

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015, Osaka, Japan, September 7-11

First Page

963

Last Page

974

ISBN

9781450335744

Identifier

10.1145/2750858.2807516

Publisher

ACM

City or Country

Osaka, Japan

Additional URL

https://doi.org/10.1145/2750858.2807516

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