This paper presents an unconventional approach to vision-guided autonomous navigation. The system recalls information about scenes and navigational experience using content-based retrieval from a visual database. To achieve a high applicability and adaptability to various road types, we do not impose a priori scene features, such as road edges, that the system must use, but rather the system automatically selects features from images during supervised learning. A new self-organizing scheme called recursive partition tree (RPT) is used for automatic construction of a vision-and-control database, which quickly prunes the data set in the content-based search and results in a low time complexity of log(n) for retrieval from a database of size n. Experimental results are reported in both indoor and outdoor navigation.


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    Titel :

    Autonomous navigation through case-based learning


    Beteiligte:
    Weng, J.J. (Autor:in) / Shaoyun Chen (Autor:in)


    Erscheinungsdatum :

    01.01.1995


    Format / Umfang :

    744208 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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