Review and prospect of intelligent perception for non-cooperative targets

被引:0
作者
Mu Jinzhen [1 ,2 ]
Hao Xiaolong [3 ]
Zhu Wenshan [1 ,2 ]
Li Shuang [1 ]
机构
[1] Nanjing Univ, Coll Astronaut, Nanjing 211106, Peoples R China
[2] Shanghai Key Lab Aerosp Intelligent Control Techn, Shanghai 201109, Peoples R China
[3] Beijing Inst Tracking & Telecommun Technol, Beijing 100094, Peoples R China
关键词
non-cooperative target; spacecraft intelligence; intelligent perception; on-orbit services; pose estimation; component recognition; SLAM;
D O I
10.16708/j.cnki.1000-758X.2021.0076
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Intelligent perception is the key technology to realizing the fine control process of spacecraft on-orbit, which is one of the major development directions of on-orbit intelligence service. The key technologies of space target intelligent perception include pose measurement, three-dimensional reconstruction and component recognition, which involve the issues such as few-shot, multi-modality, model adaptation and high-dimensional data. From the perspective of engineering application, the latest research progress in the non-cooperative intelligent perception technique was systematically summarized. Firstly, the research status of the representative non-cooperative on-orbit perception systems and optical sensors were reviewed. Secondly, the key technologies involved in the intelligence perception of non-cooperative targets were analyzed. Finally, according to the analysis of the research status and critical techniques, the main issues of non-cooperative target intelligent perception were discussed, and the recommendations for the further development were presented.
引用
收藏
页码:1 / 16
页数:16
相关论文
共 117 条
[21]  
[郝刚涛 Hao Gangtao], 2015, [宇航学报, Journal of Astronautics], V36, P706
[22]  
HAO Y M, 2018, UNMANNED SYSTEMS TEC, V1, P62
[23]  
[郝云彩 Hao Yuncai], 2017, [空间控制技术与应用, Aerospace Control and Application], V43, P9
[24]  
HE L, SOSD NET JOINT SEMAN
[25]  
HE Y., 2017, Modeling and pose measuring of non-cooperative target based on point cloud in close range, P5
[26]   2D-3D Pose Estimation of Heterogeneous Objects Using a Region Based Approach [J].
Hexner, Jonathan ;
Hagege, Rami R. .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2016, 118 (01) :95-112
[27]  
HOANG D A, SPACECRAFT DATASET D
[28]  
HONG Y Z, 2017, RES POSE ESTIMATION, P12
[29]   Parameter estimations of uncooperative space targets using novel mixed artificial neural network [J].
Hou, Xianghao ;
Yuan, Jianping ;
Ma, Chuan ;
Sun, Chong .
NEUROCOMPUTING, 2019, 339 :232-244
[30]  
Huan WX, 2020, CHIN CONTR CONF, P3339, DOI [10.23919/ccc50068.2020.9189253, 10.23919/CCC50068.2020.9189253]