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Journal of Robotics
Volume 2016 (2016), Article ID 2560573, 18 pages
Research Article

Decentralized Cooperative Localization Approach for Autonomous Multirobot Systems

IS Lab, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada A1B 3X5

Received 4 November 2015; Revised 5 February 2016; Accepted 11 February 2016

Academic Editor: Giovanni Muscato

Copyright © 2016 Thumeera R. Wanasinghe et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study proposes the use of a split covariance intersection algorithm (Split-CI) for decentralized multirobot cooperative localization. In the proposed method, each robot maintains a local cubature Kalman filter to estimate its own pose in a predefined coordinate frame. When a robot receives pose information from neighbouring robots, it employs a Split-CI based approach to fuse this received measurement with its local belief. The computational and communicative complexities of the proposed algorithm increase linearly with the number of robots in the multirobot systems (MRS). The proposed method does not require fully connected synchronous communication channels between robots; in fact, it is applicable for MRS with asynchronous and partially connected communication networks. The pose estimation error of the proposed method is bounded. As the proposed method is capable of handling independent and interdependent information of the estimations separately, it does not generate overconfidence state estimations. The performance of the proposed method is compared with several multirobot localization approaches. The simulation and experiment results demonstrate that the proposed algorithm outperforms the single-robot localization algorithms and achieves approximately the same estimation accuracy as the centralized cooperative localization approach, but with reduced computational and communicative cost.