D-GVIO: A Buffer-Driven and Efficient Decentralized GNSS-Visual-Inertial State Estimator for Multi-Agent Systems

TL;DR

D-GVIO introduces a decentralized, buffer-driven GNSS-Visual-Inertial Odometry framework that enhances efficiency, robustness, and real-time performance for multi-agent systems through innovative buffering, fusion, and outlier detection strategies.

Contribution

The paper presents a novel decentralized GVIO framework with a buffering strategy, covariance segmentation, and buffer-based re-propagation, improving efficiency and robustness over existing methods.

Findings

Reduces computational and communication burdens significantly.

Achieves superior state estimation with L-IEKF over traditional EKF.

Enhances robustness by dynamic GNSS outlier detection.

Abstract

Cooperative localization is essential for swarm applications like collaborative exploration and search-and-rescue missions. However, maintaining real-time capability, robustness, and computational efficiency on resource-constrained platforms presents significant challenges. To address these challenges, we propose D-GVIO, a buffer-driven and fully decentralized GNSS-Visual-Inertial Odometry (GVIO) framework that leverages a novel buffering strategy to support efficient and robust distributed state estimation. The proposed framework is characterized by four core mechanisms. Firstly, through covariance segmentation, covariance intersection and buffering strategy, we modularize propagation and update steps in distributed state estimation, significantly reducing computational and communication burdens. Secondly, the left-invariant extended Kalman filter (L-IEKF) is adopted for information fusion, which exhibits superior state estimation performance over the traditional extended Kalman filter (EKF) since its state transition matrix is independent of the system state. Thirdly, a buffer-based re-propagation strategy is employed to handle delayed measurements efficiently and accurately by leveraging the L-IEKF, eliminating the need for costly re-computation. Finally, an adaptive buffer-driven outlier detection method is proposed to dynamically cull GNSS outliers, enhancing robustness in GNSS-challenged environments.