Human-computer interaction requires accurate localization and effective mapping, while dynamic objects can influence the accuracy of localization and mapping. State-of-the-art SLAM algorithms assume that the environment is static. This paper proposes a new SLAM method that uses mask R-CNN to detect dynamic ob-jects in the environment and build a map containing semantic information. In our method, the reprojection error, photometric error and depth error are used to assign a robust weight to each keypoint. Thus, the dynamic points and the static points can be separated, and the geometric segmentation of the dynamic objects can be realized by using the dynamic keypoints. Each pixel is assigned a semantic label to rebuild a semantic map. Finally, our proposed method is tested on the TUM RGB-D dataset, and the experimental results show that the proposed method outperforms state-of-the-art SLAM algorithms in dynamic environments.

人机交互需要精确的定位和有效的映射，而动态对象可能会影响定位和映射的准确性。最新的SLAM算法假定环境是静态的。本文提出了一种新的SLAM方法，该方法使用掩码R-CNN来检测环境中的动态对象并构建包含语义信息的地图。在我们的方法中，重投影误差，光度误差和深度误差用于为每个关键点分配可靠的权重。因此，可以将动态点和静态点分开，并且可以通过使用动态关键点来实现动态对象的几何分割。为每个像素分配一个语义标签以重建语义图。最后，我们在TUM RGB-D数据集上测试了我们提出的方法，