Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2016

Abstract

Indoor localization is of great importance to a wide range ofapplications in shopping malls, office buildings and publicplaces. The maturity of computer vision (CV) techniques andthe ubiquity of smartphone cameras hold promise for offering sub-meter accuracy localization services. However, pureCV-based solutions usually involve hundreds of photos andpre-calibration to construct image database, a labor-intensiveoverhead for practical deployment. We present ClickLoc, anaccurate, easy-to-deploy, sensor-enriched, image-based indoor localization system. With core techniques rooted insemantic information extraction and optimization-based sensor data fusion, ClickLoc is able to bootstrap with few images. Leveraging sensor-enriched photos, ClickLoc also enables user localization with a single photo of the surroundingplace of interest (POI) with high accuracy and short delay.Incorporating multi-modal localization with Manifold Alignment and Trapezoid Representation, ClickLoc not only localizes efficiently, but also provides image-assisted navigation.Extensive experiments in various environments show that the80-percentile error is within 0.26m for POIs on the floor pla

Keywords

Indoor Localization, Smart Phone, Multi-Modal Data

Discipline

Data Storage Systems | Information Security | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, September 12-16

First Page

208

Last Page

219

Identifier

10.1145/2971648.2971668

City or Country

Heidelberg, Germany

Additional URL

https://doi.org/10.1145/2971648.2971668

Download

Share

COinS