D-CLIPSE: Distributed Consensus-based Localization with Passive Listening on Shared State Exchange
For multi-robot teams needing accurate and consistent localization under communication constraints, this method offers a practical distributed solution that matches centralized performance.
This paper proposes a distributed consensus-based localization framework for multi-robot systems that shares preintegrated odometry and shared states among robots, achieving near centralized performance in accuracy and consistency while being communication-efficient.
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.