Distributed multi-robot mapping

Coordinated exploration of an unknown environment is one of the most fundamental problems in multi-robot coordination. We propose a novel, distributed approach which addresses this problem in its most general way. Key features of our approach are the consideration of limited communication between robots, no assumptions about relative start locations of the robots, and dynamic assignment of processing tasks. We apply efficient, statistical methods to determine hypotheses for the relative locations of robots. To achieve maximal robustness, these hypotheses are generated using a hierarchical Bayesian model and subsequently verified before maps are merged. Once robots know their relative locations, they form exploration clusters so as to coordinate their actions.The overall team communication and coordination structure of our distributed mapping and exploration approach is discussed in more detail in (Konolige et al., 2003). Here wefocus on pair-wise relations between robots. As shown in Figure 1, each pair of robots can have four different types of interactions: none, hypothesis generation, hypothesis verification, and coordination.