To address the low robustness and positioning accuracy of the traditional visual simultaneous localization and mapping (SLAM) system in a dynamic environment, this study proposed a robust visual SLAM algorithm in an indoor dynamic environment based on the ORB-SLAM2 algorithm framework. First, a semantic segmentation thread uses the improved lightweight semantic segmentation network YOLOv5 to obtain the semantic mask of the dynamic object and selects the ORB feature points through the semantic mask. Simultaneously, the geometric thread detects the motion-state information of the dynamic objects using weighted geometric constraints. Then, an algorithm is proposed to assign weights to semantic static feature points and local bundle adjustment (BA) joint optimization is performed on camera pose and feature point weights, effectively reducing the influence of the dynamic feature points. Finally, experiments are conducted on a TUM dataset and a genuine indoor dynamic environment. Compared with the ORB-SLAM2 algorithm before improvement, the proposed algorithm effectively improves the positioning accuracy of the system on highly dynamic datasets, showing improvements of root mean square error (RMSE) of the absolute and relative trajectory errors by more than 96.10% and 92.06%, respectively.