In order to solve the problem that the simultaneous localization and mapping (SLAM) algorithm has low positioning accuracy and cannot generate effective dense maps in dynamic scenes, a visual SLAM algorithm based on dynamic feature culling and dense mapping is proposed. Based on the ORB-SLAM3 algorithm, a feature point screening thread is added, and the lightweight YOLOV8 network is used to detect dynamic objects in the environment, and the dynamic feature points in the environment are eliminated by combining the optical flow method and the polar geometric constraint. The dense point cloud map is constructed by using the generated keyframes and calculated poses in the newly added dense mapping thread. Compared with the original ORB-SLAM3, the positioning errors are reduced by 90%. At the same time, the ghosting caused by dynamic objects is removed from the dense mapping results. The new algorithm effectively solves the problem that the visual SLAM algorithm cannot locate and establish an effective map in the dynamic environment by adding the feature point screening thread and dense mapping thread, and greatly enhances the accuracy and robustness of the SLAM system in dynamic scenes.