DRG-SLAM: A Semantic RGB-D SLAM using Geometric Features for Indoor Dynamic Scene

Visual SLAM methods based on point features have achieved acceptable results in texture-rich static scenes, but they often suffer from a deficiency of texture and the existence of dynamic objects in real indoor scenes, which limits the application of these methods. In this paper, we have presented DRG-SLAM, which combines line features and plane features into point features to improve the robustness of the system. We tested the proposed algorithm on publicly available datasets, and the results demonstrate that the algorithm has superior accuracy and robustness in indoor dynamic scenes compared with the state-of-the-art methods.