![]() ![]() 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 purpose of this paper is to demonstrate an absolute visual localization method working in the challenging environment of an automated warehouse with low intensity of light, massively changing. ![]() ![]() 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. Abstract Pioneer efforts to improve WiFi-based localization have resorted to motion-assisted or. 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. Argus is an indoor localization system that estimates users distance and direction via combining WiFi with visual clues, which extracts geometric constraints. 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. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing /ACM. © 2016 ACM.Please use this identifier to cite or link to this item: Enhancing Wifi-based Localization with Visual Clues Bibliographic Details Author 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. 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. 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. 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. The maturity of computer vision (CV) techniques and the ubiquity of smartphone cameras hold promise for offering sub-meter accuracy localization services. Pioneer efforts to improve WiFi-based localization have resorted to motion-assisted or peer-assisted localization. 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. Expand View on ACM. Please use this identifier to cite or link to this item: Indoor Localization via Multi-modal Sensing on Smartphones Bibliographic Details Author Argus is an image-assisted localization system for mobile devices that triples the localization accuracy of classic RSS-based method, in time no longer than normal WiFi scanning, with negligible energy consumption. ![]()
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