Concerning the problem that the existence of moving objects in the application scenes will reduce the positioning accuracy and robustness of the visual Synchronous Localization And Mapping (SLAM) system, a semantic information based visual SLAM algorithm in dynamic environment was proposed. Firstly, the traditional visual SLAM front end was combined with the YOLOv4 object detection algorithm, during the extraction of ORB (Oriented FAST and Rotated BRIEF) features of the input image, the image was semantically segmented. Then, the object type was judged to obtain the area of the dynamic object in the image, and the feature points distributed on the dynamic object were eliminated. Finally, the camera pose was solved by using inter-frame matching between the processed feature points and the adjacent frames. The test results on TUM dataset show that, the accuracy of the pose estimation of this algorithm is 96.78% higher than that of ORB-SLAM2 (Orient FAST and Rotated BRIEF SLAM2) in a high dynamic environment, and the average consumption time per frame of tracking thread of the algorithm is 0.065 5 s, which is the shortest time consumption compared to those of the other SLAM algorithms used in dynamic environment. The above experimental results illustrate that the proposed algorithm can realize real-time precise positioning and mapping in dynamic environment.