With the fast development of Geographic Information Systems, visual global localization has gained a lot of attention due to the low price of a camera and the practical implications. In this paper, we leverage Google Street View and a monocular camera to develop a refined and continuous positioning in urban environments: namely a topological visual place recognition and then a 6 DoF pose estimation by local bundle adjustment. In order to avoid discrete localization problems, augmented Street View data are virtually synthesized to render a smooth and metric localization. We also demonstrate that this approach significantly improves the sub-meter accuracy and the robustness to important viewpoint changes, illumination and occlusion.


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    Titel :

    Improving robustness of monocular urban localization using augmented Street View


    Beteiligte:
    Yu, Li (Autor:in) / Joly, Cyril (Autor:in) / Bresson, Guillaume (Autor:in) / Moutarde, Fabien (Autor:in)


    Erscheinungsdatum :

    2016-11-01


    Format / Umfang :

    2777126 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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