Robust Monte Carlo localization for mobile robots

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), whi...

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Bibliographic Details
Published in:Artificial intelligence 2001-05, Vol.128 (1), p.99-141
Main Authors: Thrun, Sebastian, Fox, Dieter, Burgard, Wolfram, Dellaert, Frank
Format: Article
Language:English
Subjects:
Artificial intelligence
Kernel density trees
Localization
Mobile robots
Particle filters
Position estimation
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Summary: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 Mixture-MCL, 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.
ISSN:0004-3702
1872-7921
DOI:10.1016/S0004-3702(01)00069-8