Markov Chain Monte-Carlo Localization
نویسنده
چکیده
MCL is an extremely general framework for localization that can be used with almost any sort of sensor and map. Its power comes mainly from two aspects: • The use of a probability model that reformulates the problem of global localization as a tractable local conditional probability. This allows position information to be gleaned from sensor inputs that at any given time provide only very vague constraints on our global position. • The use of a weighted sample to represent the distribution of the position estimate. This gives an efficient sparse representation of an arbitrary probability distribution.
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