面向空间应用的视觉位姿估计技术综述  

Overview of visual pose estimation methods for space missions

作　　者：周芮 刘延芳[1] 齐乃明[1] 佘佳宇 ZHOU Rui;LIU Yanfang;QI Naiming;SHE Jiayu(School of Astronautics,Harbin Institute of Technology,Harbin 150090,China)

机构地区：[1]哈尔滨工业大学航天学院,黑龙江哈尔滨150090

出　　处：《光学精密工程》2022年第20期2538-2553,共16页Optics and Precision Engineering

基　　金：国家自然科学基金联合基金项目(No.U1737207);哈尔滨工业大学科研创新基金学科发展新方向。

摘　　要：随着人工智能的发展,基于计算机视觉的目标识别、位姿估计技术受到了广泛关注。目前,在交会对接等空间任务中,基于计算机视觉的合作目标位姿估计技术已取得广泛应用。但在非合作目标的视觉位姿估计问题上,由于空间中存在杂散光背景、表面包覆层反射、光照强度变化剧烈等因素,导致特征提取困难、位姿估计易发散、存在累计误差等难题,这些也是亟待解决的热点问题。本文首先总结了空间任务中计算机视觉技术的发展及应用;然后,对视觉位姿估计技术进行概述,以深度学习算法作为切入点,系统地归纳了目标识别及位姿估计算法;最后,综述了深度学习在空间任务中的研究进展,并在任务需求和研究现状分析的基础上,针对空间任务的特殊性,讨论了一些未来的发展趋势。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.

关 键 词：计算机视觉 图像处理 位姿估计 深度学习 空间任务 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] V446.5[自动化与计算机技术—计算机科学与技术]