A Robust Monte-Carlo Algorithm for Multi-Robot Localization

This paper presents a new algorithm for the problem of multi-robot localization in a known environment. The approach is based on the mutual refinement by robots of their beliefs about the global poses, whenever they detect each other’s paths. The robots try to detect the paths of other robots by comparing their own perception sensor information to that “seen” by other robots. This way the relative poses of the robots can be determined, which in turn enables the exchange of the beliefs about the global poses.

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