Aiming at the problem that in indoor dynamic environments, traditional visual SLAM algorithms are affected by a large amount of meaningless information, which leads to a decrease in localization accuracy and poor robustness, a visual SLAM algorithm PLYO-SLAM for indoor dynamic scenes based on YOLOv8 is proposed. In this paper, the algorithm introduces EDLines line segment detection algorithm in the tracking thread of ORB-SLAM3 algorithm, and adds a new Dynamic region detection thread. The dynamic region detection thread consists of a YOLOv8n-seg instance segmentation network, where the instance segmentation empowers the dynamic scene semantic information and generates dynamic masks, and at the same time eliminates the point and line features of dynamic regions, and utilizes the geometric constraints to further filter the missing dynamic feature points outside the segmentation masks. Experimental validation using the publicly available dataset TUM shows that compared to the ORB-SLAM3 algorithm, the PLYO-SLAM algorithm improves the localization accuracy in dynamic environments by an average of 75.99% and reaches a maximum of 96.75%.