An RGB-D SLAM Algorithm Based on Adaptive Semantic Segmentation in Dynamic Environment

When the existing visual SLAM (simultaneous localization and mapping) algorithms are applied to dynamic environments, the pose error estimated by the system often increases sharply or even the algorithm fails due to the interference of dynamic objects. In order to solve the above problems, a visual SLAM system is proposed in this paper for indoor dynamic environments. By adaptively judging whether the current frame needs semantic segmentation according to the motion level information of the feature points in the current frame, the cross-frame detection of semantic information is realized. According to the prior information provided by the semantic segmentation network and the motion state of the object in the previous scene, each feature point is assigned a motion level and is classified as static point, movable static point or dynamic point. Some appropriate feature points (static points) are selected for initial pose estimation, and then secondary optimization of the pose is performed according to the results of weighted static constraints. In order to verify the effectiveness of the proposed algorithm, experiments are carried out on the TUM RGB-D dynamic scene dataset, and compared with ORB-SLAM2 and other SLAM algorithms for dynamic environments. The results show that the proposed algorithm performs well on most datasets, and the positioning accuracy in indoor dynamic environments can be improved by 90.57% compared with the ORBSLAM algorithm without the improvement.