Covariance Intersection-based Invariant Kalman Filtering for Distributed Pose Estimation

This paper presents a novel approach to distributed pose estimation in the multi-agent system based on invariant Kalman filter with covariance intersection. Our method models uncertainties using Lie algebra and applies object-level observations within Lie groups, which have practical application value. We integrate covariance intersection to handle correlated estimates and use the invariant Kalman filter to merge independent data sources. This strategy allows us to effectively tackle the complex correlations of cooperative localization among agents, ensuring our estimates are neither too conservative nor overly confident. Additionally, we examine the consistency and stability of our algorithm, providing evidence of its reliability and effectiveness in managing multi-agent systems.