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    Mobile Robotics in a Random Finite Set Framework

    Access Status
    Fulltext not available
    Authors
    Mullane, J.
    Vo, Ba-Ngu
    Adams, M.
    Vo, Ba Tuong
    Date
    2014
    Type
    Book Chapter
    
    Metadata
    Show full item record
    Citation
    Mullane, J. and Vo, B. and Adams, M. and Vo, B.T. 2011. Mobile Robotics in a Random Finite Set Framework, in Tan, Y. and Shi, Y. and Chai, Y. and Wang, G. (ed), Advances in Swarm Intelligence: Lecture Notes in Computer Science Part 2. 6729: pp. 519-528. Berlin, Heidelberg: Springer.
    Source Title
    Advances in Swarm Intelligence Lecture Notes in Computer Science Volume 6729
    DOI
    10.1007/978-3-642-21524-7_64
    ISBN
    978-3-642-21523-0
    URI
    http://hdl.handle.net/20.500.11937/24990
    Collection
    • Curtin Research Publications
    Abstract

    This paper describes the Random Finite Set approach to Bayesian mobile robotics, which is based on a natural multi-object filtering framework, making it well suited to both single and swarm-based mobile robotic applications. By modeling the measurements and feature map as random finite sets (RFSs), joint estimates the number and location of the objects (features) in the map can be generated. In addition, it is shown how the path of each robot can be estimated if required. The framework differs dramatically from existing approaches since both data association and feature management routines are integrated into a single recursion. This makes the framework well suited to multi-robot scenarios due to the ease of fusing multiple map estimates from swarm members, as well as mapping robustness in the presence of other mobile robots which may induce false map measurements. An overview of developments thus far is presented, with implementations demonstrating the merits of the framework on simulated and experimental datasets.

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