Abstract: Traditional visual simultaneous localization and mapping (SLAM) algorithms function well when the environmental objects are stationary or moving at low speeds, but their precision and robustness are low when dynamic disturbances such as personnel walking and vehicle moving are present. To address this problem, a dynamic SLAM system is proposed based on the Oriented FAST and Rotated BRIEF-SLAM3 (ORB-SLAM3) framework, which integrates the You Only Look At CoefficienTs (YOLACT + +) deep learning network with the ORB-SLAM3 framework for detecting dynamic targets. A dense optical flow field is extracted and incorporated with visual geometry to discover the motion attributes. A motion-level transfer strategy that integrates an instance segmentation network and dense optical flow field to achieve joint optimization of SLAM system efficiency and accuracy is proposed. The test results on the public dataset TUM present that the proposed system has an outstanding performance in dynamic scenarios. Compared with ORB-SLAM3, the root mean square error, mean error, median error, and standard deviation in low dynamic scenarios are enhanced by approximately 60% and over 90% in high dynamic scenarios. The actual experiments in a corridor scene reveal that the proposed system can effectively eliminate feature points on dynamic targets while extracting features, thus guaranteeing system accuracy.