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Monte Carlo Localization with Mixture Proposal Distribution

9 years 12 months ago
Monte Carlo Localization with Mixture Proposal Distribution
Monte Carlo localization (MCL) is a Bayesian algorithm for mobile robot localization based on particle filters, which has enjoyed great practical success. This paper points out a limitation of MCL which is counter-intuitive, namely that better sensors can yield worse results. An analysis of this problem leads to the formulation of a new proposal distribution for the Monte Carlo sampling step. Extensive experimental results with physical robots suggest that the new algorithm is significantly more robust and accurate than plain MCL. Obviously, these results transcend beyond mobile robot localization and apply to a range of particle filter applications.
Sebastian Thrun, Dieter Fox, Wolfram Burgard
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where AAAI
Authors Sebastian Thrun, Dieter Fox, Wolfram Burgard
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