HAR-GVIO: A Hierarchically Adaptive Robust GNSS/Visual/IMU Navigation System for Vehicular Positioning With ZUPT Aiding

GNSS/Visual/Inertial Odometry (GVIO) has been widely applied in positioning systems for Internet of Things (IoT) applications. However, achieving robust and precise multi-sensor fusion in complex, dynamic environments remains a significant challenge, often hampered by unreliable zero-velocity detection and the inability of static robust filters to handle time-varying noise. To address these limitations, this paper proposes a Hierarchically Adaptive Robust GVIO (HAR-GVIO) framework. Our approach introduces two synergistically co-designed components. First, we propose a novel adaptive Zero Velocity Update (ZUPT) mechanism that moves beyond simple thresholding. It features a high-level adaptive fusion mechanism that dynamically arbitrates between three heterogeneous detectors based on their real-time inter-consistency, ensuring reliable ZUPT decisions even when individual sensor cues are ambiguous. Second, we propose an adaptive generalized robust filtering architecture that autonomously tunes the robust kernel’s shape according to residual distribution characteristics, enabling real-time suppression of sensor outliers. Experimental results demonstrate that this synergistic framework significantly outperforms traditional methods, offering superior estimation accuracy and robustness in environments with degraded sensor performance.