Indoor localization via multi-modal sensing on smartphones
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-ba...
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sg-smu-ink.sis_research-55132019-12-19T05:52:41Z Indoor localization via multi-modal sensing on smartphones XU, Han YANG, Zheng ZHOU, Zimu SHANGGUAN, Longfei YI, Ke LIU, Yunhao 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 2016-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4510 info:doi/10.1145/2971648.2971668 https://ink.library.smu.edu.sg/context/sis_research/article/5513/viewcontent/ubicomp16_xu.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Indoor Localization Smart Phone Multi-Modal Data Data Storage Systems Information Security Software Engineering |
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Indoor Localization Smart Phone Multi-Modal Data Data Storage Systems Information Security Software Engineering XU, Han YANG, Zheng ZHOU, Zimu SHANGGUAN, Longfei YI, Ke LIU, Yunhao Indoor localization via multi-modal sensing on smartphones |
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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 |
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XU, Han YANG, Zheng ZHOU, Zimu SHANGGUAN, Longfei YI, Ke LIU, Yunhao |
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XU, Han YANG, Zheng ZHOU, Zimu SHANGGUAN, Longfei YI, Ke LIU, Yunhao |
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XU, Han |
title |
Indoor localization via multi-modal sensing on smartphones |
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Indoor localization via multi-modal sensing on smartphones |
title_full |
Indoor localization via multi-modal sensing on smartphones |
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Indoor localization via multi-modal sensing on smartphones |
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Indoor localization via multi-modal sensing on smartphones |
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indoor localization via multi-modal sensing on smartphones |
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Institutional Knowledge at Singapore Management University |
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2016 |
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https://ink.library.smu.edu.sg/sis_research/4510 https://ink.library.smu.edu.sg/context/sis_research/article/5513/viewcontent/ubicomp16_xu.pdf |
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