Robust Monte Carlo Localization for Mobile Robots

Robust Monte Carlo Localization for Mobile Robots (2001)

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by Sebastian Thrun , Dieter Fox , Wolfram Burgard , Frank Dellaert

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@MISC{Thrun01robustmonte,
    author = {Sebastian Thrun and Dieter Fox and Wolfram Burgard and Frank Dellaert},
    title = {Robust Monte Carlo Localization for Mobile Robots},
    year = {2001}
}

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Abstract

Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm to mobile robots equipped with range finders, a kernel density tree is learned that permits fast sampling. Systematic empirical results illustrate the robustness and computational efficiency of the approach.

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