详细介绍MCL算法，是由Sebastian Thrun a, Dieter Fox, Wolfram Burgard, Frank Dellaert所著的论文，发表于Artificial Intelligence上。-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. 　2001 Published by Elsevier Science B.V.

Keywords: Mobile robots　Localization　Position estimation　Particle filters　Kernel density trees