Indoor localization via multi-modal sensing on smartphones

• Main Authors: XU, Han, YANG, Zheng, ZHOU, Zimu, SHANGGUAN, Longfei, YI, Ke, LIU, Yunhao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Indoor Localization; Smart Phone; Multi-Modal Data; Data Storage Systems; Information Security; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4510; https://ink.library.smu.edu.sg/context/sis_research/article/5513/viewcontent/ubicomp16_xu.pdf
• Institution: Singapore Management University

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