Aiming at the problem that most classic visual SLAMs are not robust enough in indoor dynamic environments, a visual SLAM that can distinguish between high and low dynamic environments is proposed based on the ORB-SLAM3 algorithm framework. First, an algorithm is proposed to distinguish whether the prior dynamic objects in indoor environments are in high or low dynamics based on the reprojection error of the pose transformation between multiple consecutive frames. Then, according to the high and low dynamics of the environment, it is decided whether to combine the YOLOv8-Seg instance segmentation network to remove the dynamic features in the dynamic environment to ensure the tracking accuracy of the SLAM system. Finally, in order to deal with the repeated map points in the map caused by dynamic features, a repeated map point elimination algorithm is added to the local map tracking to delete the repeated map points in the dynamic environment, further ensuring the stable tracking of the system. Experimental results on the public dataset TUM RGB-D show that the improved algorithm has improved the positioning accuracy compared with the ORB-SLAM3 algorithm, with a maximum improvement of 60.41% in low dynamic environments and a maximum improvement of 94.65% in high dynamic environments. Compared with other dynamic feature removal algorithms, higher positioning accuracy is achieved in most sequences, and it is also more advantageous in real-time performance. The proposed algorithm effectively solves the problem of SLAM coping with indoor dynamic environments and improves the positioning accuracy of SLAM.