Indoor localization via multi-modal sensing on smartphones
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Detail(s)
Original language | English |
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Title of host publication | UbiComp'16 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing |
Place of Publication | New York |
Publisher | Association for Computing Machinery (ACM) |
Pages | 208-219 |
ISBN (print) | 9781450344616 |
Publication status | Published - 2016 |
Externally published | Yes |
Publication series
Name | UbiComp - Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing |
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Conference
Title | 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) |
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Place | Germany |
City | Heidelberg |
Period | 12 - 16 September 2016 |
Link(s)
Abstract
Indoor localization is of great importance to a wide range of applications in shopping malls, office buildings and public places. The maturity of computer vision (CV) techniques and the ubiquity of smartphone cameras hold promise for offering sub-meter accuracy localization services. However, pure CV-based solutions usually involve hundreds of photos and pre-calibration to construct image database, a labor-intensive overhead for practical deployment. We present ClickLoc, an accurate, easy-to-deploy, sensor-enriched, image-based indoor localization system. With core techniques rooted in semantic 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 surrounding place 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 the 80-percentile error is within 0.26m for POIs on the floor plan, which sheds light on sub-meter level indoor localization. © 2016 ACM.
Research Area(s)
- Indoor localization, Multi-modal data, Smart phone
Citation Format(s)
Indoor localization via multi-modal sensing on smartphones. / Xu, Han; Yang, Zheng; Zhou, Zimu et al.
UbiComp'16 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York: Association for Computing Machinery (ACM), 2016. p. 208-219 (UbiComp - Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing).
UbiComp'16 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York: Association for Computing Machinery (ACM), 2016. p. 208-219 (UbiComp - Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review