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

TL;DR

D-GVIO is proposed, 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.

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.

Figures (10)

• Figure 1: Decentralized GVIO system with two agents ($\textrm{Agent}_1$, $\textrm{Agent}_2$). Each agent runs a local filter for propagation and private updates using IMU, GNSS, and camera data. Collaborative updates occur when both agents observe common landmarks via feature matching.
• Figure 2: Block diagram of the proposed system. Each agent maintains local SLAM features and MSCKF tracks for measurement updates. When receiving messages from other agents, the system finds feature correspondences and performs collaborative MSCKF or SLAM updates.
• Figure 3: Coordinate frames in the GVIO system and their transformations between each other. The transformation of frame $\{C\}$ into the frame $\{I\}$ is $\left(\bm{R}^I_c,\bm{p}^I_c\right)$ and the transformation of frame $\{I\}$ into the frame $\{W\}$ is $\left(\bm{R}^w_I,\bm{p}^w_I\right)$.
• Figure 4: Cross-covariance propagation is delayed until the next update step (at time $t_k$), and is propagated using the state transition matrix and correction term stored in the buffer.
• Figure 5: Delayed measurement scenario: Sensor 1 performs update step at time $t_k$. Due to the communication and computational delays, the update step is not completed until time $t_m$.