Resilient Sequential Fusion on Lie Groups for Consistent Collaborative Localization

Abstract

Collaborative localization (CL) in multiagent systems has become an increasingly prominent research area, particularly under an uncertain and partially connected dynamic communication environment. This article presents a CL framework based on a resilient sequential fusion approach that guarantees consistency in matrix Lie groups. The proposed method is inherited from the pipeline of the distribution Kalman filter, which utilizes invariant error defined on a manifold to establish the global state propagation and update process for estimating the pose of all agents in the predefined reference frame. Furthermore, the communication update process is treated separately by generalizing the covariance intersection (CI) fusion into the designed geometric group structure, enabling flexible updates while maintaining the consistency of estimates and ensuring the independence of the filter update process. Specifically, to address the potential issues in agents’ communication, a weighted fusion criterion with an analytical form is proposed, allowing communication fusion to be performed on the manifold with arbitrary information fusion order and structure. Extensive validation through simulations and real-world experiments demonstrates that the proposed method is resilient to varying communication conditions and achieves superior performance compared with state-of-the-art methods.