Semantic Texture Complexity Model for Feature Generation and Selection in Visual SLAM

Yichi Zhang, Xina Cheng, Licheng Jiao

Abstract: Simultaneous Localization And Mapping (SLAM) plays an irreplaceable role in autonomous driving, robotics and augmented reality for whose ability of locating and providing the map of the unknown environment. The traditional SLAM algorithms describe the environment by extracting, tracking and matching the local feature points, which often leads to the feature points gathering in certain areas and affects the performance of pose detection. In order to obtain a SLAM environment map with accurate representative feature points, this paper proposes a semantic texture complexity model for feature generation and selection. The semantic texture complexity model respectively evaluates the texture complexity of each semantic area so that even the area with few corners or edges can be represented by feature points. For different semantic regions, different feature point generation and selection schemes in the Visual-Inertial Odometry (VIO) module are adopted according to the texture complexity, which avoids the concentration of feature points and effectively improves the positioning accuracy. In this paper, we use the uHumans2 dataset to test the positioning accuracy. The test results show that the proposed algorithm reduces the error by 10%-30% compared with the related work.