PO-GVINS: A Tightly Coupled GNSS-Visual-Inertial Navigation Framework Using Pose-Only Representation

Accurate and reliable positioning is essential for perception, decision-making, and other high-level applications in autonomous driving, unmanned aerial vehicles, and intelligent robotics. Due to the inherent limitations of standalone sensors, integrating heterogeneous sensors with complementary capabilities is an effective approach to achieving this goal. The visual-inertial navigation system (VINS) fuses visual cameras and inertial measurement units (IMUs) to explore unknown environments. It requires a priori knowledge of 3D features and jointly estimates camera poses and feature positions, which inevitably introduces feature linearization errors. Meanwhile, the dimensionality of the system state increases with abundant textures, degrading real-time performance. To eliminate accumulated errors from VINS, frameworks further fuse measurements from the Global Navigation Satellite System (GNSS), but still suffer from similar limitations. To address the aforementioned issues, we propose a filtering-based, tightly coupled GNSS-visual-inertial positioning framework with a pose-only formulation applied to VINS, termed PO-GVINS. We first apply the PO formulation to our VINS (PO-VINS). GNSS raw measurements are subsequently incorporated, with integer ambiguities resolved, to achieve accurate and drift-free state estimation. Extensive experiments demonstrate that the proposed PO-VINS significantly outperforms the multi-state constraint Kalman filter (MSCKF) and achieves accuracy comparable to that of optimization-based VINS. By further incorporating GNSS measurements, PO-GVINS achieves accurate, drift-free state estimation, making it a robust solution for positioning in challenging environments. Datasets used in this research are available at https://gitee.com/lv-jiarui/SmartPNT-MSF-Datasets.git.