YOLO-SLAM: A semantic SLAM system towards dynamic environment with geometric constraint

Abstract

Simultaneous localization and mapping (SLAM), as one of the core prerequisite technologies for intelligent mobile robots, has attracted much attention in recent years. However, the traditional SLAM systems rely on the static environment assumption, which becomes unstable for the dynamic environment and further limits the real-world practical applications. To deal with the problem, this paper presents a dynamic-environment-robust visual SLAM system named YOLO-SLAM. In YOLO-SLAM, a lightweight object detection network named Darknet19-YOLOv3 is designed, which adopts a low-latency backbone to accelerate and generate essential semantic information for the SLAM system. Then, a new geometric constraint method is proposed to filter dynamic features in the detecting areas, where dynamic features can be distinguished by utilizing the depth difference with Random Sample Consensus (RANSAC). YOLO-SLAM composes the object detection approach and the geometric constraint method in a tightly coupled manner, which is able to effectively reduce the impact of dynamic objects. Experiments are conducted on the challenging dynamic sequences of TUM dataset and Bonn dataset to evaluate the performance of YOLO-SLAM. The results demonstrate that the RMSE index of absolute trajectory error can be significantly reduced to 98.13% compared with ORB-SLAM2 and 51.28% compared with DS-SLAM, indicating that YOLO-SLAM is able to effectively improve stability and accuracy in the highly dynamic environment.