For the localization and static semantic mapping problems in dynamic scenes， a Simultaneous Localization And Mapping （SLAM） algorithm in dynamic scenes based on semantic and optical flow constraints was proposed to reduce the impact of moving objects on localization and mapping. Firstly， for each frame of the input， the masks of the objects in the frame were obtained by semantic segmentation， then the feature points that do not meet the epipolar constraint were filtered out by the geometric method. Secondly， the dynamic probability of each object was calculated by combining the object masks with the optical flow， the feature points were filtered by the dynamic probabilities to obtain the static feature points， and the static feature points were used for the subsequent camera pose estimation. Then， the static point cloud was created based on RGB-D images and object dynamic probabilities， and the semantic octree map was built by combining the semantic segmentation. Finally， the sparse semantic map was created based on the static point cloud and the semantic segmentation. Test results on the public TUM dataset show that， in highly dynamic scenes， the proposed algorithm improves the performance on both the absolute trajectory error and relative pose error by more than 95% compared with ORB-SLAM2， and reduces the absolute trajectory error by 41% and 11% compared with DS-SLAM and DynaSLAM respectively， which verifies that the proposed algorithm has better localization accuracy and robustness in highly dynamic scenes. The experimental results of mapping show that the proposed algorithm creates a static semantic map， and the storage space requirement of the sparse semantic map is reduced by 99% compared to that of the point cloud map.