A Decentralized Junction Tree Approach to Mobile Robots Cooperative Localization

This paper presents a decentralized solution to the cooperative localization of mobile robot teams. The problem is cast as inference on a dynamic Bayesian network (DBN) of Gaussian distribution, which is implemented incrementally by decomposing the DBN into a sequence of chain graphs connected by the interfaces. The proposed inference scheme can make use of the sparsity of the chain graphs and achieve efficient communication. In our decentralized formulation, the local sensor data at each robot are organized as potentials of the cliques of junction trees; message passing between robots updates the clique potentials to realize information sharing. Each robot can get optimal estimates of its own states. The method is optimal in the sense that it makes no approximations apart from the usual model liberalization. The performance of the proposed algorithm is evaluated with simulation experiments.