One of the fundamental requirements of an autonomous vehicle is the ability to determine its location on a map. Frequently, solutions to this localization problem rely on GPS information or use expensive three dimensional (3D) sensors. In this paper, we describe a method for long-term vehicle localization based on visual features alone. Our approach utilizes a combination of topological and metric mapping, which we call topometric localization, to encode the coarse topology of the route as well as detailed metric information required for accurate localization. A topometric map is created by driving the route once and recording a database of visual features. The vehicle then localizes by matching features to this database at runtime. Since individual feature matches are unreliable, we employ a discrete Bayes filter to estimate the most likely vehicle position using evidence from a sequence of images along the route. We illustrate the approach using an 8.8 km route through an urban and suburban environment. The method achieves an average localization error of 2.7 m over this route, with isolated worst case errors on the order of 10 m.


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    Title :

    Visual topometric localization


    Contributors:
    Badino, H. (author) / Huber, D. (author) / Kanade, T. (author)


    Publication date :

    2011-06-01


    Size :

    1704826 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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