GNSS-Aided Online Calibration of Camera Intrinsic and Extrinsic Parameters for Visual–Inertial Navigation Systems

Online camera calibration is essential for maintaining navigation stability by compensating for drift or unknown calibration parameters in Visual-Inertial Navigation Systems (VINS). However, it remains challenging due to the limited availability of valid information during motion. In this paper, we present a novel online camera calibration method for VINS by tightly coupling Global Navigation Satellite System (GNSS) information. Specifically, the proposed approach enables stable and accurate online calibration of both camera intrinsic and camera-IMU extrinsic parameters by incorporating GNSS pseudorange and carrier-phase measurement constraints into a centralized Extended Kalman Filter (EKF). The multi-sensor fusion mechanism enhances state estimation consistency through sensor complementarity, enabling robust navigation performance alongside dynamic parameter calibration. The results from both simulations and real-world experiments demonstrate that GNSS assistance enhances state observability, accelerates convergence, and improves the correction of calibration errors at various scales. In a 230-meter outdoor navigation task, the calibration results from different methods were treated as fixed parameters for evaluation. The proposed method achieved an altitude drift of 1.95 m and an absolute pose error (APE) root mean square error (RMSE) of 3.7 m, outperforming both Kalibr offline calibration (2.75 m drift, 3.98 m RMSE) and GNSS-absent online calibration (7.98 m drift, 4.98 m RMSE).