Distributed Invariant Kalman Filter for Object-level Multi-robot Pose SLAM

TL;DR

This paper introduces a distributed invariant Kalman filter for multi-robot pose estimation that leverages object-level measurements and Lie group modeling to improve accuracy, reduce communication, and enhance robustness in cooperative localization.

Contribution

It presents a novel distributed invariant Kalman filter based on covariance intersection and Lie group modeling for efficient multi-robot pose estimation with reduced communication and improved consistency.

Findings

Validated through simulation and real data experiments

Demonstrated improved accuracy and robustness over existing methods

Reduced communication burden via object-level measurement models

Abstract

Cooperative localization and target tracking are essential for multi-robot systems to implement high-level tasks. To this end, we propose a distributed invariant Kalman filter based on covariance intersection for effective multi-robot pose estimation. The paper utilizes the object-level measurement models, which have condensed information further reducing the communication burden. Besides, by modeling states on special Lie groups, the better linearity and consistency of the invariant Kalman filter structure can be stressed. We also use a combination of CI and KF to avoid overly confident or conservative estimates in multi-robot systems with intricate and unknown correlations, and some level of robot degradation is acceptable through multi-robot collaboration. The simulation and real data experiment validate the practicability and superiority of the proposed algorithm.