Covariance Intersection-based Invariant Kalman Filtering(DInCIKF) for Distributed Pose Estimation

TL;DR

This paper introduces a covariance intersection-based invariant Kalman filter for distributed pose estimation in multi-agent systems, effectively managing correlations and uncertainties for reliable cooperative localization.

Contribution

It proposes a novel fusion approach combining invariant Kalman filtering with covariance intersection, modeling uncertainties via Lie algebra and object-level observations within Lie groups.

Findings

Effective handling of correlated estimates in multi-agent localization

Maintains consistency and stability in pose estimation

Improves accuracy over traditional methods

Abstract

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 estimates that are correlated and use the invariant Kalman filter for merging 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.