Research on Vision-based Semantic SLAM towards Indoor Dynamic Environment

Abstract

Most of the maps constructed based on traditional visual SLAM technology are sparse maps, which only contain geometric information and do not contain semantic information, which limits the robot to complete the tasks of understanding. In this paper, we propose a vision-based semantic SLAM method. The visual odometry is optimized by using semantic information to remove the influence of dynamic objects in the scene. Based on the proposed method, we can finally construct a semantic map. Experiments show that, our system effectively improves the positioning and mapping accuracy.