Description: 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
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