A genetic algorithm for simultaneous localization and mapping

Duckett, Tom (2003) A genetic algorithm for simultaneous localization and mapping. In: IEEE International Conference on Robotics and Automation (ICRA 2003), 14-19 September 2003, Taipei, Taiwan.

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Abstract

This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. The SLAM problem is defined as a global optimization problem in which the objective is to search the space of possible robot maps. A genetic algorithm is described for solving this problem, in which a population of candidate solutions is progressively refined in order to find a globally optimal solution. The fitness values in the genetic algorithm are obtained with a heuristic function that measures the consistency and compactness of the candidate maps. The results show that the maps obtained are very accurate, though the approach is computationally expensive. Directions for future research are also discussed.

Keywords:SLAM, robotic mapping, Mobile Robot Navigation
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:College of Science > School of Computer Science
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ID Code:29845
Deposited On:31 Jan 2018 16:51

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