In order to optimize the problem of tracking failure during the operation of the UAV SLAM system,which leads to the failure of local mapping,and optimize the problem of large trajectory errors of the binocular camera in far and near complex scenes,this paper proposes a binocular SLAM.Firstly,new constraints are added to the system to optimize the disparity and depth of binocular matching,then the optical flow method is applied to remove the mismatches based on homography motion compensation and PROSAC algorithm,and finally new data associations are obtained by projection according to the homography compensation results.A new method is applied to the key frames and map points to improve the utilization rate of compensation results and ensure the real-time performance and robustness of the system.The proposed algorithm uses binocular parallax to optimize the calculation of the depth of key points to improve the positioning accuracy and robustness of the system,and achieves excellent accuracy improvement on the EuRoC data set of indoor UAVs.