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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Autonomous navigation through case-based learning


    Contributors:
    Weng, J.J. (author) / Shaoyun Chen (author)


    Publication date :

    1995-01-01


    Size :

    744208 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Autonomous Navigation Through Case-Based Learning

    Weng, J. J. / Chen, S. / IEEE; Computer Society; Technical Committee for Pattern Analysis and Machine Intelligence | British Library Conference Proceedings | 1995


    Learning for autonomous navigation

    Angelova, Anelia / Howard, Andrew / Matthies, Larry et al. | NTRS | 2005


    Learning for Autonomous Navigation

    Angelova, Anelia / Howard, Andrew / Matthies, Larry et al. | NTRS | 2005


    Deep learning-based heterogeneous system for autonomous navigation

    Sellers, Timothy / Lei, Tingjun / Carruth, Daniel et al. | SPIE | 2023


    Hybrid Reinforcement Learning based controller for autonomous navigation

    Joglekar, Ajinkya / Krovi, Venkat / Brudnak, Mark et al. | IEEE | 2022