基于新型匹配神经网络的单目视觉定位与建图系统

his paper proposes a new monocular vision localization and mapping system (SOF-VSLAM) based on feature point method and optical flow fusion. The system can estimate the 6-DOF attitude of the vehicle more robustly than the traditional monocular vision positioning system and establish a reusable point cloud map. This method uses a feature point optical flow tracking network based on deep learning-SOF to provide the matching relationship between frames for the visual positioning system, and at the same time, it can output the feature vector of the feature point and apply it to the loopback process. Experiments of the system on TUM and Euroc datasets show that the system is more robust and accurate than the traditional monocular vision positioning system.