The Simultaneous Localization And Mapping （SLAM） algorithm suffers from a decrease in localization accuracy when moving targets appear. Introducing instance segmentation and other algorithms can handle dynamic scenes， but it is difficult to ensure the real-time performance of SLAM algorithm. Additionally， camera shake during motion may lead to inaccurate keyframe selection and tracking loss. In response to the issues， an ORB-SLAM2 algorithm based on dynamic feature point filtering and optimization of keyframe selection was proposed to ensure the real-time performance of SLAM algorithm， and reduce the influence of dynamic feature points on the positioning accuracy of SLAM algorithm effectively. And simultaneously， the issue of inaccurate keyframe selection caused by camera shake was addressed. In the proposed algorithm， YOLOv5 algorithm was introduced on the basis of ORB-SLAM2 algorithm to identify moving targets. In the tracking thread， dynamic target feature points were filtered out， thereby achieving a balance between real-time performance and positioning accuracy of the algorithm. At the same time， a discriminative criterion based on inter-frame relative motion quantity was proposed for keyframe selection， thereby enhancing the accuracy of keyframe selection. Experimental results on freiburg3_walking_xyz dataset indicate that compared to ORB-SLAM2 algorithm， the proposed algorithm has a 38.54% reduction in average processing time and a 95.2% improvement in Root Mean Square Error （RMSE） accuracy of absolute trajectory error. It can be seen that the proposed algorithm can address the issues mentioned above effectively， enhance the positioning accuracy and precision of SLAM algorithm， and then improve the usability of the maps.