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    Extending Bayesian RFS SLAM to multi-vehicle SLAM

    Access Status
    Fulltext not available
    Authors
    Moratuwage, D.
    Vo, Ba-Ngu
    Wang, D.
    Wang, H.
    Date
    2012
    Type
    Conference Paper
    
    Metadata
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    Citation
    Moratuwage, D. and Vo, B. and Wang, D. and Wang, H. 2012. Extending Bayesian RFS SLAM to multi-vehicle SLAM, in Proceedings of 2012 12th International Conference on Control, Automation, Robotics and Vision (ICARCV 2012), pp. 638-643, Dec 5-7 2012. Guangzhou, China: IEEE.
    Source Title
    2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012
    DOI
    10.1109/ICARCV.2012.6485232
    ISBN
    9781467318716
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/10263
    Collection
    • Curtin Research Publications
    Abstract

    In this paper we present a novel solution to the Multi-Vehicle SLAM (MVSLAM) problem by extending the random finite set (RFS) based SLAM filter framework using two recently developed multi-sensor information fusion approaches. Our solution is based on the modelling of the measurements and the landmark map as RFSs and factorizing the MVSLAM posterior into a product of the joint vehicle trajectories posterior and the landmark map posterior conditioned the vehicle trajectories. The joint vehicle trajectories posterior is propagated using a particle filter while the landmark map posterior conditioned on the vehicle trajectories is propagated using a Gaussian Mixture (GM) implementation of the probability hypothesis density (PHD) filter. © 2012 IEEE.

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