文章基本信息

标题：Crowd-Sourced Mobility Mapping for Location Tracking Using Unlabeled Wi-Fi Simultaneous Localization and Mapping
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作者：Mu Zhou ; Kunjie Xu ; Zengshan Tian 等
期刊名称：Mobile Information Systems
印刷版ISSN：1574-017X
出版年度：2015
卷号：2015
DOI：10.1155/2015/416197
出版社：Hindawi Publishing Corporation
摘要：Due to the increasing requirements of the seamless and round-the-clock Location-based services (LBSs), a growing interest in Wi-Fi network aided location tracking is witnessed in the past decade. One of the significant problems of the conventional Wi-Fi location tracking approaches based on received signal strength (RSS) fingerprinting is the time-consuming and labor intensive work involved in location fingerprint calibration. To solve this problem, a novel unlabeled Wi-Fi simultaneous localization and mapping (SLAM) approach is developed to avoid the location fingerprinting and additional inertial or vision sensors. In this approach, an unlabeled mobility map of the coverage area is first constructed by using the crowd-sourcing from a batch of sporadically recorded Wi-Fi RSS sequences based on the spectral cluster assembling. Then, the sequence alignment algorithm is applied to conduct location tracking and mobility map updating. Finally, the effectiveness of this approach is verified by the extensive experiments carried out in a campus-wide area.