This paper presents two variants of a Bayesian algorithm for vehicle localization which use vehicle motion data, a low-cost GNSS receiver, a gray scale camera, and different digital map data. The key idea of the algorithm is not to extract features like points or lines from the camera image for the Bayes update, but to predict entire images. While the first variant performs this image prediction based on explicit landmark information of a digital map, the second variant predicts camera images directly based on aerial images. In doing so, no conversion step from aerial images to feature maps is necessary. Finally, the paper presents results for both approaches based on extensive test drive data with highly accurate reference data.


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    Title :

    Vehicle localization in urban environments using feature maps and aerial images


    Contributors:
    Mattern, N. (author) / Wanielik, G. (author)


    Publication date :

    2011-10-01


    Size :

    1291819 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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