This paper compares model-based and artificial neural network approaches to computer vision based road following. Representative techniques of each approach, dynamic vision and radial basis function networks, were implemented in C/C++ on the same outdoor mobile robot using a commercially available workstation. Their performance was evaluated using the rules of the 1995 3rd Annual International Unmanned Ground Robotics Competition. Both methods were able to navigate a simple course; however, the model-based method performed more consistently and was found to have several practical advantages.


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    Title :

    Implementation and comparison of two computer vision based road following approaches


    Additional title:

    Implementierung und Vergleich von zwei computervisionbasierten Straßenfolgeverfahren


    Contributors:
    Murphy, R. (author) / Hoff, W. (author) / Hoffmann, J. (author) / Blitch, J. (author) / Hawkins, D. (author) / Gough, V. (author) / Krosley, R. (author)


    Publication date :

    1995


    Size :

    8 Seiten, 2 Bilder, 12 Quellen


    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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