Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Heterogeneous Teams of Modular Robots for Mapping and Exploration
Autonomous Robots
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Distributed localization of networked cameras
Proceedings of the 5th international conference on Information processing in sensor networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Consistent cooperative localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Coordinated multi-robot exploration
IEEE Transactions on Robotics
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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.