D-GVIO: 一种基于缓冲的高效分布式GNSS-视觉-惯性状态估计器用于多智能体系统
D-GVIO: A Buffer-Driven and Efficient Decentralized GNSS-Visual-Inertial State Estimator for Multi-Agent Systems
AI总结：
D-GVIO通过基于缓冲的分布式框架和L-IEKF实现高效鲁棒的GNSS-视觉-惯性状态估计，适用于多智能体系统的协同定位任务。
摘要：
协同定位对于蜂群应用如协同探索和救援任务至关重要。然而，在资源受限平台上维持实时能力、鲁棒性和计算效率带来了重大挑战。为了解决这些挑战，我们提出了D-GVIO，一种基于缓冲的完全分布式GNSS-视觉-惯性里程计（GVIO）框架，利用新颖的缓冲策略来支持高效且鲁棒的分布式状态估计。所提出的框架具有四个核心机制。首先，通过协方差分割、协方差交叠和缓冲策略，我们模块化了分布式状态估计中的传播和更新步骤，显著减少了计算和通信负担。其次，采用左不变扩展卡尔曼滤波器（L-IEKF）进行信息融合，其状态转移矩阵与系统状态无关，因此在传统扩展卡尔曼滤波器（EKF）之上表现出更优的状态估计性能。第三，采用基于缓冲的重新传播策略来高效准确地处理延迟测量，利用L-IEKF，避免了昂贵的重新计算。最后，提出了一种自适应缓冲驱动的异常检测方法，动态剔除GNSS异常值，提高了GNSS挑战环境下的鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
