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  • Hierarchical Monte-Carlo Localisation Balances Precision and Speed

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    Estivill-Castro127141-Published.pdf (294.5Kb)
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    Version of Record (VoR)
    Author(s)
    Estivill-Castro, Vladimir
    McKenzie, Blair
    Griffith University Author(s)
    McKenzie, Blair S.
    Estivill-Castro, Vladimir
    Year published
    2004
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    Abstract
    Localisation is a fundamental problem for mobile robots. In dynamic environments (robotic soccer) it is imperative that the process be very efficient. Techniques like Monte-Carlo Localisation or Markov Models have been shown to be effective in dealing with partial recognition of landmarks, errors in odometry and the kidnap problem. But they are particularly CPU intensive. However, many times decision-making does not need high accuracy, and thus, we have developed a hierarchical version that allows us to balance real-time efficiency of computation with precision in localisation.Localisation is a fundamental problem for mobile robots. In dynamic environments (robotic soccer) it is imperative that the process be very efficient. Techniques like Monte-Carlo Localisation or Markov Models have been shown to be effective in dealing with partial recognition of landmarks, errors in odometry and the kidnap problem. But they are particularly CPU intensive. However, many times decision-making does not need high accuracy, and thus, we have developed a hierarchical version that allows us to balance real-time efficiency of computation with precision in localisation.
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    Conference Title
    Australasian Conference on Robotics and Automation 2004
    Publisher URI
    https://www.araa.asn.au/conference/acra-2004/
    Copyright Statement
    © 2004 Australian Robotics and Automation Association. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
    Publication URI
    http://hdl.handle.net/10072/2174
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    • Conference outputs

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