A unified state estimation framework integrating IMU data and visual information is proposed, enabling real-time dynamic estimation of zoom camera intrinsic parameters with high precision and fast convergence.
The improved ORB feature extraction and LK optical flow tracking strategies enhance the robustness of visual observations, while EKF-based fusion of high-frequency IMU motion constraints and visual geometric constraints effectively suppresses parameter drift during continuous zooming.
