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