摘要
A dynamic visual Simultaneous Localization and Mapping(SLAM)algorithm is proposed in this paper,which combines the YOLACT network with the geometric method to design a dynamic point detection module for eliminating dynamic points.The dense optical flow-based dynamic point detection scheme is adopted to make up for the problem that the elimination algorithm based on the instance segmentation network overrelies on object prior information.Aiming at the low accuracy of the original output mask of YOLACT,a mask post-processing method based on image processing and morphology is proposed to repair the dynamic point mask output by the YOLACT network.Finally,this module is integrated into the Oriented FAST and Rotated BRIEF SLAM 2(ORB-SLAM2)framework to construct a visual SLAM system adapted to dynamic scenes.The proposed algorithm is tested and verified on the public TUM dataset,which proves the effectiveness of the proposed module.Compared with the ORB-SLAM2 system,the localization accuracy of the proposed algorithm is improved by 93.4%in indoor dynamic scenes.
