Objective Visual SLAM one of the core technologies for autonomous driving and mobile robots is currently unable to cope with highly dynamic environments with traditional algorithms and lacks semantic information in maps. Addressing the impact of dynamic objects on SLAM systems is the main objective of this study which is also one of the current hot topics. Methods A novel method based on YOLOv8 synchronous dynamic detection and local semantic segmentation was proposed to realize the position estimation and local semantic map building in dynamic environments. Firstly synchronous dynamic detection and semantic segmentation were performed on input images using YOLOv8. Dynamic feature points were eliminated using the target frame of the target detection result and then static feature points were applied for pose estimation. Then an expansion mask was added to the semantically segmented image in the semantic mapping thread of the system. Finally a point cloud library was used for the construction of semantic maps thereby generating semantic maps applicable to real-world scenarios. Results Comparative tests were conducted in the TUM dataset and the data showed that this method can improve the position accuracy by 98. 1% compared with traditional methods. Moreover the speed of the proposed algorithm was superior in real-time testing compared with similar algorithms and it can create local semantic maps simultaneously. Conclusion The method based on YOLOv8 for synchronous dynamic detection and local semantics is highly effective in addressing the impact of dynamic objects on SLAM systems in typical scenes with high real-time performance. However in some special scenarios such as significant camera rotation the failure of object detection leads to the failure of dynamic feature removal resulting in reduced system accuracy.