With the expansion of the underground parking lot, reverse vehicle-searching has become urgent problems that need to be solved. In this paper, we proposed a Wi-Fi and vision integrated reverse vehicle-searching method. We transform the reverse vehicle-searching into indoor self-positioning. And we proposed a multi-scale indoor positioning method which includes coarse localization with Wi-Fi matching and image-level localization with ORB based visual matching. Once the position of the host of the vehicle is located, we exploit A∗ algorithm to plan the route from the host to the position of the vehicle that has been recorded in advance. The proposed method had been tested in two different datasets of 6,000 square meters and 12,000 square meters underground parking lot. From the results, the proposed Wi-Fi and vision integrated method can achieve at least 97% reverse vehicle-searching success rate. In both test scenarios, the localization errors are sub-meter in average.


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    Titel :

    Wi-Fi and vision integrated localization for reverse vehicle-searching in underground parking lot


    Beteiligte:
    Huang, Gang (Autor:in) / Hu, Zhaozheng (Autor:in) / Cai, Hao (Autor:in)


    Erscheinungsdatum :

    01.08.2017


    Format / Umfang :

    389211 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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