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Learning models of the environment is one of the fundamental tasks of mobile robots since maps are needed for a wide range of robotic applications, such as navigation and transportation tasks, service robotic applications, and several others. In the past, numerous efficient approaches to map learning have been proposed. Most of them, however, assume that the robot lives on a plane. In this paper, the authors present a highly efficient maximum likelihood approach that is able to solve 3D as well as 2D problems.
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