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    An RFS theoretic for Bayesian feature-based robotic mapping

    Access Status
    Fulltext not available
    Authors
    Mullane, J.
    Vo, Ba-Ngu
    Adams, M.
    Vo, B.
    Date
    2011
    Type
    Book Chapter
    
    Metadata
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    Citation
    Mullane J. and Vo B.N. and Adams M., Vo B.T. (2011). An RFS theoretic for Bayesian feature-based robotic mapping, in Random Finite Sets for Robot Mapping and SLAM. Springer Tracts in Advanced Robotics, vol 72, pp. 45-76. Berlin: Springer.
    Source Title
    Springer Tracts in Advanced Robotics
    DOI
    10.1007/978-3-642-21390-8_4
    School
    School of Electrical Engineering and Computing
    URI
    http://hdl.handle.net/20.500.11937/60053
    Collection
    • Curtin Research Publications
    Abstract

    Estimating a FB map requires the joint propagation of the FB map density encapsulating uncertainty in feature number and location. This chapter addresses the joint propagation of the FB map density and leads to an optimal map estimate in the presence of unknown map size, spurious measurements, feature detection and data association uncertainty. The proposed framework further allows for the joint treatment of error in feature number and location estimates. As a proof of concept, the first-order moment recursion, the PHD filter, is implemented using both simulated and real experimental data. The feasibility of the proposed framework is demonstrated, particularly in situations of high clutter density and large data association ambiguity. This chapter establishes new tools for a more generalised representation of the FB map, which is a fundamental component of the more challenging SLAM problem, to follow in Part II.

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