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    Localization and Reconstruction of Mobile Robots Using a Camera Ring

    TitleLocalization and Reconstruction of Mobile Robots Using a Camera Ring
    Publication TypeJournal Article
    Año de publicación2009
    AutoresPizarro, D, Mazo, M, Santiso, E, Marron, M, Fernández, I
    Idioma de publicaciónEnglish
    JournalIEEE Transaction on Instrumentation and Measurements
    Volumen58
    Número8
    Páginas2396 - 2409
    Fecha de publicación08/2009
    EditorialIEEE Instrumentation and Measurements Society
    Rank in category27/58
    JCR CategoryINSTRUMENTS & INSTRUMENTATION
    Palabras clavecomputer vision, Intelligent spaces, robotics
    JCR Impact Factor1.025
    ISSN0018-9456
    URLhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4967948
    DOI10.1109/TIM.2009.2016380
    Abstract

    In this paper a system capable of obtaining the 3D pose of a mobile robot using a ring of calibrated cameras attached to the environment is proposed. The system robustly tracks point fiducials in the image plane of the set of cameras generated by the robot’s rigid shape in motion. Each fiducial is identified with a point belonging to a sparse 3D geometrical model of robot’s structure. Such model allows direct pose estimation from image measurements and it can be easily enriched at each iteration with new points as the robot motion evolves. The process is divided in an initialization step, where the structure of the robot is obtained and an online step, which is solved using sequential Bayesian inference. The approach allows to model properly uncertainty in measurements and estimations, at the same time it serves as a regularization step in pose estimation. The proposed system is verified using simulated and real data.

    Resumen

    In this paper a system capable of obtaining the 3D pose of a mobile robot using a ring of calibrated cameras attached to the environment is proposed. The system robustly tracks point fiducials in the image plane of the set of cameras generated by the robot’s rigid shape in motion. Each fiducial is identified with a point belonging to a sparse 3D geometrical model of robot’s structure. Such model allows direct pose estimation from image measurements and it can be easily enriched at each iteration with new points as the robot motion evolves. The process is divided in an initialization step, where the structure of the robot is obtained and an online step, which is solved using sequential Bayesian inference. The approach allows to model properly uncertainty in measurements and estimations, at the same time it serves as a regularization step in pose estimation. The proposed system is verified using simulated and real data.

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