Decentralized Cooperative Localization for Heterogeneous Multi-robot System Using Split Covariance Intersection Filter

This study proposes the use of a split covariance intersection filter (Split-CIF) for decentralized multi-robot cooperative localization. In the proposed method each robot maintains a local extended Kalman filter to estimate its own pose in a pre-defined reference frame. When a robot receives pose information from neighbouring robots it employs a Split-CIF-based approach to fuse this received measurement with its local belief. For a team of N mobile robots, the processing and communication complexity of the proposed method is linear, O(N), with respect to the number of robots in the team. The proposed method does not demand for fully connected synchronous communication channels between robots and can work with any asynchronous and partially connected communication network. Additionally, the proposed method gives consistent state updates and is capable of handling independent and interdependent parts of the estimations separately. The numerical simulations presented validate the proposed algorithm. The simulation results demonstrate that the proposed algorithm is outperformed compared to single-robot localization algorithms and also demonstrate approximately the same estimation accuracy as a centralized cooperative localization approach but with reduced computational cost.