文献类型：期刊文献

英文题名：Covariance Intersection-based Invariant Kalman Filtering(DInCIKF) for Distributed Pose Estimation

作者：Li, Haoying[1]; Li, Xinghan[2]; Huang, Shuaiting[2]; Yang, Chao[3]; Wu, Junfeng[1]

机构：[1] School of Data Science, The Chinese University of Hong Kong, Shenzhen, China; [2] College of Control Science and Engineering, Zhejiang University, Hangzhou, China; [3] Department of Automation, East China University of Science and Technology, Shanghai, China

年份：2024

外文期刊名：arXiv

收录：EI(收录号：20240402011)

语种：英文

外文关键词：Kalman filters - Lie groups

摘要：This paper presents a novel approach to distributed pose estimation in the multi-agent system based on an 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. ? 2024, CC0.