Rao-Blackwellised PHD SLAM

J. Mullane, Ba-Ngu Vo, M.D. Adams

    Research output: Chapter in Book/Conference paperConference paperpeer-review

    30 Citations (Scopus)


    This paper proposes a tractable solution to feature-based (FB) SLAM in the presence of data association uncertainty and uncertainty in the number of features. By modeling the feature map as a random finite set (RFS), a rigorous Bayesian formulation of the FB-SLAM problem that accounts for uncertainty in the number of features and data association is presented. As such, the joint posterior distribution of the set-valued map and vehicle trajectory is propagated forward in time as measurements arrive. A first order solution, coined the PHD-SLAM filter, is derived, which jointly propagates the posterior PHD or intensity function of the map and the posterior distribution of the trajectory of the vehicle. A Rao-Blackwellised implementation of the PHD-SLAM filter is proposed based on the Gaussian mixture PHD filter for the map and a particle filter for the vehicle trajectory. Simulated results demonstrate the merits of the proposed approach, particularly in situations of high clutter and data association ambiguity.
    Original languageEnglish
    Title of host publicationProceedings of the 2010 IEEE International Conference on Robotics and Automation
    Place of PublicationPiscataway, NJ, USA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Publication statusPublished - 2010
    EventRao-Blackwellised PHD SLAM - Anchorage, AK
    Duration: 1 Jan 2010 → …


    ConferenceRao-Blackwellised PHD SLAM
    Period1/01/10 → …


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