Visual Simultaneous Location And Mapping （VSLAM） technology is commonly used for indoor robot navigation and perception. However， the pose estimation method of VSLAM aims at static environment， and might lead to the location and mapping failure when moving objects exist in the scene. To solve this problem， an Instance Segmentation and Clustering SLAM （ISC-SLAM） system was proposed. In this system， the instance segmentation network was used to generate the possibility masks of dynamic objects in the scene， and the dynamic points in the scene were detected by using the multi-view geometry method during the segmentation. After matching the obtained possibility masks and the detected dynamic points， the accurate dynamic masks of moving objects were determined. The feature points of the dynamic objects were able to be deleted by using the dynamic masks and then the position of camera was estimated accurately by using the remained static feature points. To solve the under-segmentation problem of the instance segmentation network， the depth filling algorithm and clustering algorithm were applied to ensure the completed deletion elimination of dynamic feature points. Finally， the moving objects obscured background was reconstructed， and the static point cloud map was built with the correct camera pose. Experimental results on Technical University of Munich （TUM） dataset demonstrate that the proposed system can achieve robust positioning and mapping while ensuring real-time performance in dynamic environment.