Cooperative multi-robot localization: using communication to reduce localization error

This paper presents a statistical algorithm for cooperative multi-robot localization based on a propagating detection model. The problem of multi-robot localization consists of localizing each robot in a group within the same environment, when robots share information to improve localization accuracy. Our approach is based on a well-known probabilistic localization approach, the Markov localization, that was originally designed to a single robot. A detection model can be incorporated in order to accommodate multi-robot cooperation in Markov localization. In this model, two robots exchange their pose beliefs whenever one robot detects another. We propose a novel detection model in that all robots in the group can benefit from a meeting of two robots through detection propagation. The technique has been implemented and tested in simulated environments. Experiments illustrate improvements in localization accuracy when compared with a previous multi-robot localization approach.