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Autonomous mobile robots can be applied to perform activities that should not, or cannot, be performed by humans due to inhospitable conditions or high level of danger. An autonomous mobile robot must be able to navigate safely in unfamiliar environments by reconstructing information from its sensors so as to plan and execute routes. Simultaneous Localization And Mapping, SLAM, technique allows the gradual creation of a map using data obtained from sensors while estimating the robot localization, and the Iterative Closest Point, ICP, algorithm is one of the approaches adopted for SLAM. This work proposes and evaluates an ICP-based algorithm for simultaneous localization and mapping of a robot. The algorithm was implemented in a simulated environment using Microsoft® Robotics Developer Studio, MRDS. Experimental results show that, in the evaluated trajectory, the method presented in this work has a better performance than the one obtained by the original ICP algorithm.