Aiming at the problems that the monocular visual SLAM （Simultaneous Localization and Mapping） algorithm could not recover the geographic scale information and the accumulation of pose estimation errors is large， a monocular vision SLAM algorithm based on GNSS fusion has been proposed. Based on the graph optimization theory， the algorithm built a similarity transformation estimation model in the visual odometry to solve the real scale， added the GNSS global location node in the back-end optimization， designed the optimization solution strategy， and improved the key frame pose and 3D map point position estimation. Finally， at the end of the sequence， the global map was iteratively optimized offline to ensure the global consistency of map construction. Experiments showed that in the outdoor environment， when the motion trajectory was within 5 km， the scale estimation error of the method proposed in this paper was within 0.1%， and the trajectory error was within 2 m， which could meet the needs of practical applications.