To perform simultaneous localization and mapping (SLAM) in dynamic environments, static background objects must first be determined. This condition can be achieved using a priori information in the form of a map of background objects. Such an approach exhibits a causality dilemma, because such a priori information is the ultimate goal of SLAM. In this paper, we propose a background foreground segmentation method that overcomes this issue. Localization is achieved using a robust iterative closest point implementation and vehicle odometry. Background objects are modeled as objects that are consistently located at a given spatial location. To improve robustness, classification is performed at the object level through the integration of a new segmentation method that is robust to partial object occlusion.


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    Titel :

    Background Foreground Segmentation for SLAM


    Beteiligte:
    Corcoran, P. (Autor:in) / Winstanley, A. (Autor:in) / Mooney, P. (Autor:in) / Middleton, R. (Autor:in)


    Erscheinungsdatum :

    01.12.2011


    Format / Umfang :

    456339 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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