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This paper introduces D-CLIPSE, a distributed filtering framework for multi-robot localization that enhances accuracy and consistency while minimizing communication overhead. By leveraging preintegrated odometry and shared state exchange among robots, the method achieves near-centralized performance in both simulated and experimental settings. The results indicate significant improvements in localization consistency compared to existing decentralized approaches, making it a viable alternative for real-world applications constrained by hardware and communication limitations.
Achieving near-centralized localization performance in multi-robot systems without the need for extensive communication could revolutionize how teams of robots operate in real-world environments.
Multi-robot localization that is accurate and consistent is imperative for downstream tasks such as planning and control. Centralized filtering approaches optimally fuse all available sensor measurements of the team. However, a centralized solution is rarely implementable due to hardware, communication, and computational constraints. Distributed approaches deploy a filter on each robot to estimate their own state and neighbours'states using inter-robot communication. This paper proposes a consistent, communication-efficient, and consensus-based distributed filtering framework that shares both preintegrated odometry and relevant shared states among communicating robots. The proposed method is validated in simulated and experimental scenarios, showing near centralized performance in accuracy, and especially in consistency, compared to the current state-of-the-art decentralized approach.
