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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Background Foreground Segmentation for SLAM


    Contributors:
    Corcoran, P. (author) / Winstanley, A. (author) / Mooney, P. (author) / Middleton, R. (author)


    Publication date :

    2011-12-01


    Size :

    456339 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Flexible background mixture models for foreground segmentation

    Cheng, J. / Yang, J. / Zhou, Y. et al. | British Library Online Contents | 2006


    Smooth Foreground-Background Segmentation for Video Processing

    Schindler, K. / Wang, H. | British Library Conference Proceedings | 2006


    An Adaptive Background Modeling Method for Foreground Segmentation

    Zhong, Zuofeng / Zhang, Bob / Lu, Guangming et al. | IEEE | 2017



    Foreground Segmentation Using Adaptive 3 Phase Background Model

    Roy, Sujoy Madhab / Ghosh, Ashish | IEEE | 2020