YS-SLAM: YOLACT++ based semantic visual SLAM for autonomous adaptation to dynamic environments of mobile robots
Jiajie Li, Jingwen Luo
Abstract
Abstract Aiming at the problem of poor autonomous adaptability of mobile robots to dynamic environments, this paper propose a YOLACT++ based semantic visual SLAM for autonomous adaptation to dynamic environments of mobile robots. First, a light-weight YOLACT++ is utilized to detect and segment potential dynamic objects, and Mahalanobis distance is combined to remove feature points on active dynamic objects, also, epipolar constraint and clustering are employed to eliminate feature points on passive dynamic objects. Then, in terms of the semantic labels of dynamic and static components, the global semantic map is divided into three parts for construction. The semantic overlap and uniform motion model are chose to track moving objects and the dynamic components are added to the background map. Finally, a 3D semantic octree map is constructed that is consistent with the real environment and updated in real time. A series of simulations and experiments demonstrated the feasibility and effectiveness of the proposed approach.