In this paper, the performance of a topological-metric visual-path-following framework is investigated in different environments. The framework relies on a monocular camera as the only sensing modality. The path is represented as a series of reference images such that each neighboring pair contains a number of common landmarks. Local 3-D geometries are reconstructed between the neighboring reference images to achieve fast feature prediction. This condition allows recovery from tracking failures. During navigation, the robot is controlled using image-based visual servoing. The focus of this paper is on the results from a number of experiments that were conducted in different environments, lighting conditions, and seasons. The experiments with a robot car show that the framework is robust to moving objects and moderate illumination changes. It is also shown that the system is capable of online path learning.


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    Titel :

    Experimental Evaluation of Autonomous Driving Based on Visual Memory and Image-Based Visual Servoing


    Beteiligte:
    Diosi, A. (Autor:in) / Segvic, S. (Autor:in) / Remazeilles, A. (Autor:in) / Chaumette, F. (Autor:in)


    Erscheinungsdatum :

    2011-09-01


    Format / Umfang :

    1518716 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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