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

Transporting heavy or oversized slung loads using rotorcraft has traditionally relied on single-aircraft systems, which limits both payload capacity and control authority. Cooperative multilift using teams of rotorcraft offers a scalable and efficient alternative, especially for infrequent but challenging "long-tail" payloads without the need of building larger and larger rotorcraft. Most prior multilift research assumes GPS availability, uses centralized estimation architectures, or relies on controlled laboratory motion-capture setups. As a result, these methods lack robustness to sensor loss and are not viable in GPS-denied or operationally constrained environments. This paper addresses this limitation by presenting a distributed and decentralized payload state estimation framework for vision-based multilift operations. Using onboard monocular cameras, each UAV detects a fiducial marker on the payload and estimates its relative pose. These measurements are fused via a Distributed and Decentralized Extended Information Filter (DDEIF), enabling robust and scalable estimation that is resilient to individual sensor dropouts. This payload state estimate is then used for closed-loop trajectory tracking control. Monte Carlo simulation results in Gazebo show the effectiveness of the proposed approach, including the effect of communication loss during flight.

Reliable localization is a fundamental requirement for multi-robot systems operating in GPS-denied environments. Visual-inertial odometry (VIO) provides lightweight and accurate motion estimation but suffers from cumulative drift in the absence of global references. Ultra-wideband (UWB) ranging offers complementary global observations, yet most existing UWB-aided VIO methods are designed for single-robot scenarios and rely on pre-calibrated anchors, which limits their robustness in practice. This paper proposes a distributed collaborative visual-inertial-ranging odometry (DC-VIRO) framework that tightly fuses VIO and UWB measurements across multiple robots. Anchor positions are explicitly included in the system state to address calibration uncertainty, while shared anchor observations are exploited through inter-robot communication to provide additional geometric constraints. By leveraging a right-invariant error formulation on Lie groups, the proposed approach preserves the observability properties of standard VIO, ensuring estimator consistency. Simulation results with multiple robots demonstrate that DC-VIRO significantly improves localization accuracy and robustness, while simultaneously enabling anchor self-calibration in distributed settings.

Decentralized cooperative localization (DCL) is a promising approach for nonholonomic mobile robots operating in GPS-denied environments with limited communication infrastructure. This paper presents a DCL framework in which each robot performs localization locally using an Extended Kalman Filter, while sharing measurement information during update stages only when communication links are available and companion robots are successfully detected by LiDAR. The framework preserves cross-correlation consistency among robot state estimates while handling asynchronous sensor data with heterogeneous sampling rates and accommodating accelerations during dynamic maneuvers. Unlike methods that require pre-aligned coordinate systems, the proposed approach allows robots to initialize with arbitrary reference-frame orientations and achieves automatic alignment through transformation matrices in both the prediction and update stages. To improve robustness in feature-sparse environments, we introduce a dual-landmark evaluation framework that exploits both static environmental features and mobile robots as dynamic landmarks. The proposed framework enables reliable detection and feature extraction during sharp turns, while prediction accuracy is improved through information sharing from mutual observations. Experimental results in both Gazebo simulation and real-world basement environments show that DCL outperforms centralized cooperative localization (CCL), achieving a 34% reduction in RMSE, while the dual-landmark variant yields an improvement of 56%. These results demonstrate the applicability of DCL to challenging domains such as enclosed spaces, underwater environments, and feature-sparse terrains where conventional localization methods are ineffective.