Overview of visual pose estimation methods for space missions

被引：0
|
作者：
Zhou R. [1 ]
Liu Y. [1 ]
Qi N. [1 ]
She J. [1 ]
机构：
[1] School of Astronautics, Harbin Institute of Technology, Harbin
来源：
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2022年 / 30卷 / 20期
关键词：
computer vision; deep learning; image processing; pose estimation; spatial task;
D O I：
10.37188/OPE.20223020.2538
中图分类号：
学科分类号：
摘要：
With the development of artificial intelligence，target recognition and pose estimation based on computer vision have received widespread attention. At present，computer-vision-based pose estimation technology for cooperative targets is being widely used in space missions，such as in rendezvous and docking. However，for noncooperative targets，complex environments，such as stray-light backgrounds，surface-coating reflections，and dramatic light changes，cause difficulties in feature extraction and pose estimation. In this paper，the methods and applications of visual-based pose estimation in space missions are summarized. Various target recognition and pose estimation algorithms，based on deep-learning algorithms，are systematically outlined. Moreover，current deep-learning algorithms in the context of space missions are discussed. Finally，the task demand of space tasks is analyzed to present some future development trends. © 2022 Chinese Academy of Sciences. All rights reserved.
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页码：2538 / 2553
页数：15
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