In the actual environment, the localization accuracy of the simultaneous localization and mapping (SLAM) system mounted on the mobile robot is often low due to the influence of dynamic objects, and the camera orientation position will fail when it is serious. On this basis, a RDFP-SLAM algorithm combining you only look once (YOLO) dynamic object detection network and LK optical flow method was proposed. In the visual odometry thread, the object detection network YOLOv5 was used to detect the dynamic target in the image acquired by the camera, then the LK optical flow method was used to determine the real dynamic feature points in the expected dynamic target detection box and remove them, and the remaining static feature points were involved in pose estimation and mapping. Finally, the experimental test was carried out in the public data set TUM, KITTI and the real dynamic environment. Experimental results show that under the influence of multiple visual sensors and different indoor and outdoor environments, the RDFP-SLAM algorithm still has a significant reduction in time consumption compared with the same type of algorithms, and effectively improves the accuracy of feature extraction in dynamic environment, so that the robustness, real-time performance and positioning results of the system are optimized.