Abstract
Limitation of computational resources is a challenging problem for moving agents that launch such algorithms as simultaneous localization and mapping (SLAM). To increase the accuracy on limited resources one may add more computing agents that might explore the environment quicker than one and thus to decrease the load of each agent. In this article, the state-of-the-art in multi-agent SLAM algorithms is presented, and an approach that extends laser 2D single hypothesis SLAM for multiple agents is introduced. The article contains a description of problems that are faced in front of a developer of such approach including questions about map merging, relative pose calculation, and roles of agents.
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