Abstract
Mobile robot localization is one of the crucial problems as far as autonomous mobile robots are concerned. Among many proposed techniques, the Monte Carlo localization algorithm has been reported as an adequate approach with respect to the solution of problems involving pose estimation for mobile robots. This article presents a Monte Carlo-based localization algorithm coupled to a genetic algorithm, whose main function is to compensate for localization errors caused by deficiencies of the probabilistic models for sensing and acting considered in the standard Monte Carlo method. In the context of Mobile Robotics, those modelling problems are caused by misreadings from sonar sensors facing obstacle corners and edges.
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