In the context of simultaneous localization and mapping （SLAM）， the accuracy of positioning is significantly affected by interference caused by dynamic objects. This paper addresses the challenges of SLAM in dynamic environments through the removal of dynamic objects and restoration of empty regions. Semantic information is obtained using Mask-RCNN， while a polar geometry approach is employed to eliminate dynamic objects. Keyframe pixel weighted mapping enables precise recovery of void regions in both RGB and depth maps at a pixel-by-pixel level. Experimental results on the TUM dataset demonstrate an average improvement of 85.26% in pose estimation accuracy compared to ORB-SLAM2， as well as a 28.54% enhancement over DynaSLAM performance. The proposed method exhibits robust performance even in real-world scenarios.