Dynamic Feature Rejection Based on Geometric Constraint for Visual SLAM in Autonomous Driving

Autonomous driving has gradually become a research hotspot in recent years, but the robustness of loopback detection in complex environments such as dynamic and weak textures need to be improved. For the problem that traditional visual SLAM doesn’t make sufficient use of semantic information in the environment, a dynamic feature rejection method for visual SLAM is proposed based on geometric constraints in autonomous driving. This paper proposes to combine image segmentation and visual SLAM to build a semantic SLAM system and also proposes a data association method that combines semantic and geometric information to improve the traditional loopback detection method by using semantic information to increase the accuracy of loopback detection. The result of experiment on the TUM public dataset shows that the loopback detection accuracy of the improved loopback detection method is higher than that of the bag-of-words method in all four datasets; therefore, the method in this paper improves the accuracy of loopback detection of the SLAM system in general.