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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    Title :

    Improving robustness of monocular urban localization using augmented Street View


    Contributors:


    Publication date :

    2016-11-01


    Size :

    2777126 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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