Extensive experiments in various environments show that the 80-percentile error is within 0.26mfor POIs on the floor plan, which sheds light on sub-meter level indoor localization. Incorporating multi-modal localization with Manifold Alignment and Trapezoid Representation, ClickLoc not only localizes efficiently, but also provides image-assisted navigation. Second Life allows us to meet and share knowledge in a Virtual classroom setting.1 Video Games: Games can help players learn/hone strategic planning & develop the ability to lead and work well with others. 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. Were currently in a prime example of using a Visual Game as a medium of learning. With core techniques rooted in semantic information extraction and optimization-based sensor data fusion, ClickLoc is able to bootstrap with few images. We present ClickLoc, an accurate, easy-to-deploy, sensor-enriched, image-based indoor localization system. However, pure CV-based solutions usually involve hundreds of photos and pre-calibration to construct image database, a labor-intensive overhead for practical deployment. The maturity of computer vision (CV) techniques and the ubiquity of smartphone cameras hold promise for offering sub-meter accuracy localization services. Indoor localization is of great importance to a wide range of applications in shopping malls, office buildings and public places. UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, September 2016, p. We devise techniques for photo selection, geometric constraint extraction, joint location estimation, and build a prototype that runs on commodity phones. Indoor localization via multi-modal sensing on smartphones. Google Scholar Digital Library Han Xu, Zheng Yang, Zimu Zhou, Longfei Shangguan, Ke Yi, and Yunhao Liu. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. © ACM 978-1-4503-3574-4/15/09.15.00.Please use this identifier to cite or link to this item: Indoor Localization via Multi-modal Sensing on Smartphones Bibliographic Details Author Enhancing wifi-based localization with visual clues. 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. We devise techniques for photo selection, geometric constraint extraction, joint location estimation, and build a prototype that runs on commodity phones. 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. To get over these limitations, we propose Argus, an image-Assisted localization system for mobile devices. 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. Among numerous indoor localization systems proposed during the past decades, WiFi fingerprint-based localization has been one of the most attractive solutions, which is known to be free of extra infrastructure and specialized hardware. However, those methods suffer from fingerprint ambiguity that roots in multipath fading and temporal dynamics of wireless signals. Current mainstream solutions rely on Received Signal Strength (RSS) of wireless signals as fingerprints to distinguish and infer locations. Indoor localization is of great importance to a wide range of applications in the era of mobile computing.
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