In order to reduce the interference of dynamic objects on visual SLAM, a robust dynamic RGB-D SLAM that combines the motion probability of feature point and weighted pose estimation is proposed. First, the instance segmentation network Yolact is used to obtain semantic information of scene, combine semantic information and depth information to restore the dynamic mask boundaries, and calculate the semantic dynamic probability according to the magnitude of the prior motion probability. Then, a semantically guided method is used to calculate the geometric dynamic probability of feature point, and the semantic dynamic probability, the geometric dynamic probability and their confidence are combined to construct the motion probability of the feature point, and a feature point screening strategy with adaptive probability threshold is designed. Finally, in the process of pose tracking, local map optimization, and global optimization of the system, a weighted cost function based on the motion probability of feature point is designed to distinguish the contribution of different feature points to pose optimization. In addition, after removing the dynamic objects, a global point cloud map is established for static scenes. Experimental results on the public datasets demonstrate that, compared with ORB-SLAM2, the Root Mean Square Error of Absolute Trajectory Error of the proposed algorithm on the TUM RGB-D and Bonn datasets is reduced on average by 69.16% and 91.94%, respectively. Moreover, compared with other state-of-the-art dynamic SLAM algorithms, the proposed method exhibits noticeable improvements in both pose estimation accuracy and robustness. In real-world experiments, compared with ORB-SLAM2 and Dyna-SLAM, the trajectory endpoint drift error is reduced by an average of 52.20% and 19.15% respectively.