Research on visual odometry scheme based on semi-feature method

This paper introduces a visual odometry scheme based on a semi-feature method aimed at enhancing the efficiency of front-end tracking in visual SLAM systems. By integrating direct and feature-based methods, our approach improves tracking speed while maintaining pose estimation accuracy. We replace traditional pose estimation with a direct method for regular frames and utilize feature-based methods for keyframes, optimizing computational resources without significant loss in performance. Experimental validation on the EuRoC dataset shows substantial improvements in real-time tracking efficiency compared to conventional feature-based methods.