In recent years, deep learning methodologies have been increasingly applied to the intricate challenges of visual-inertial odometry (VIO), especially in scenarios with rapid movements and scenes lacking clear structure. This paper introduces a novel hybrid approach that leverages the inherent strengths of traditional VIO techniques, while harnessing the potential of advanced machine learning technologies. By seamlessly integrating an iterated extended Kalman filter with deep learning techniques, our approach systematically takes into account uncertainties, thereby enhancing the overall reliability and robustness of the system. The proposed algorithm has been rigorously evaluated on the KITTI and EuroC datasets, outperforming other deep learning VIO methods. It achieved a translation error of 2.28% and a rotation error of 0.226 degrees per 100 meters on the KITTI odometry dataset.
