Hierarchical Monte-Carlo Localisation Balances Precision and Speed
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Author(s)
Estivill-Castro, Vladimir
McKenzie, Blair
Year published
2004
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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
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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.