空间控制技术与应用2023，Vol.49Issue(6)：58-67,10.DOI:10.3969/j.issn.1674-1579.2023.06.006
基于深度学习的动态环境视觉里程计研究
Visual Odometry of Dynamic Environment Based on Deep Learning
摘要
Abstract
This paper proposes a dynamic scene visual odometry method based on deep learning.The C3Ghost module is built using the lightweight Ghost module combined with the target detection network YOLOv5s,and the CA(coordinate attention mechanism)is introduced to improve the network detection speed while ensuring detection accuracy.It is combined with the motion consistency algorithm to eliminate dynamic feature points and only use static feature points for pose estimation.Experimental results show that compared with the traditional ORB-SLAM3(orient FAST and rotated BRIEF-simultaneous localization and mapping 3)algorithm,the ATE(absolute trajectory error)and RPE(relative pose error)on the TUM(technical university of Munich)RGB-D(RGB-depth)high dy-namic data set has improved by more than 90％on average.Compared with the advanced SLAM algorithm,it is al-so relatively improved.Therefore,this algorithm effectively improves the stability and robustness of visual SLAM in dynamic environments.关键词
视觉里程计/目标检测/注意力机制/轻量级/运动一致性Key words
visual odometry/object detection/attention mechanism/lightweight/motion consistency分类
航空航天引用本文复制引用
崔立志,杨啸乾,杨艺..基于深度学习的动态环境视觉里程计研究[J].空间控制技术与应用,2023,49(6):58-67,10.基金项目
国家自然科学基金-联合基金项目(U1804147)National Natural Science Foundation of China-Joint Fund Project(U1804147) （U1804147）
