Visual SLAM Based on Semantic Segmentation and Geometric Constraints for Dynamic Indoor Environments

Simultaneous localization and mapping (SLAM), a core technology of mobile robots and autonomous driving, has received more and more attention in recent years. However, most of the existing visual SLAM algorithms do not consider the impact of dynamic objects on the visual SLAM system, resulting in significant system positioning errors and high map redundancy. Based on the ORB-SLAM2 algorithm, this paper combines semantic information and a geometric constraint algorithm based on feature point homogenization to improve the positioning accuracy of the SLAM system. Aiming at the problem that the feature points extracted by the ORB-SLAM2 algorithm are easily concentrated, and the extraction rate is low in the weak texture area, a feature point homogenization algorithm based on quadtree and adaptive threshold is proposed to improve the uniformity of feature point extraction. In addition, in view of the impact of dynamic targets on the SLAM system, the dynamic information in the scene is filtered out through the semantic segmentation network and the motion consistency detection algorithm, and the static feature points obtained after filtering are used to estimate the camera pose. Then, a semantic map is constructed after filtering out the dynamic point cloud according to the semantic information. Finally, the test results on Oxford and TUM datasets show that the uniformity of feature points extracted by the improved algorithm is increased by 56.3%. The positioning error of visual SLAM is reduced by 68.8% on average, and the constructed semantic map has rich semantic information and less redundancy.