Non-cooperative target pose estimation based on improved iterative closest point algorithm

doi:10.23919/JSEE.2022.000001

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (1): 1-10.doi: 10.23919/JSEE.2022.000001

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收稿日期:2021-01-28 接受日期:2021-11-09 出版日期:2022-01-18 发布日期:2022-02-22

Non-cooperative target pose estimation based on improved iterative closest point algorithm

Zijian ZHU¹(), Wenhao XIANG³(), Ju HUO^1,^2,*(), Ming YANG¹(), Guiyang ZHANG¹(), Liang WEI¹()

¹ Department of Astronautics, Harbin Institute of Technology, Harbin 150006, China
² Department of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150006, China
³ Systems Engineering Research Institute, China State Shipbuilding Corporation Limited, Beijing 100036, China

Received:2021-01-28 Accepted:2021-11-09 Online:2022-01-18 Published:2022-02-22
Contact: Ju HUO E-mail:hit_zzj@163.com;xiangwh2018@163.com;torch@hit.edu.cn;myang_csc@163.com;dr_gyzhang@163.com;weiliangts@163.com
About author:|ZHU Zijian was born in 1998. He received his B.S. degree in artificial intelligence and automation from Harbin Engineering University, Harbin, China, in 2020. He is currently pursuing his M.S. degree in control science and technology with the Department of Astronautics, Harbin Institute of Technology. His current research interests include vision measurement, modern optics, and machine vision. E-mail: hit_zzj@163.com||XIANG Wenhao was born in 1981. He received his B.S. degree in electronic and information engineering and his M.S. degree in man-machine-environment engineering from Harbin Institute of Technology, Harbin, China, in 2007. He is currently pursuing his Ph.D. degree in electronic and information engineering at Tsinghua University. He is working in the Systems Engineering Research Institute of China State Shipbuilding Corporation Limited. His current research interests include artificial intelligence, information processing, and machine vision. E-mail: xiangwh2018@163.com||HUO Ju was born in 1977. He received his B.S. and M.S. degrees in electric engineering from Harbin Institute of Technology, in 1999 and 2001, respectively. He received his Ph.D. degree in control science and engineering from Harbin Institute of Technology, in 2007, where he is currently a professor and a doctoral supervisor with the Simulation and Control Center. His current research interests include vision measurement, camera calibration, and system simulation. E-mail: torch@hit.edu.cn||YANG Ming was born in 1963. He received his B.S., M.S., and Ph.D. degrees in control science and engineering from Harbin Institute of Technology, in 1985, 1988, and 1997, respectively. Since 1988, he has been with Harbin Institute of Technology, where he is currently a professor and a doctoral supervisor with the Simulation and Control Center. His current research interests include stereoscopic and multi-view video coding and modern optics. E-mail: myang_csc@163.com||ZHANG Guiyang was born in 1990. He received his B.S. degree in detection guidance and control technology from Changchun University of Science and Technology, Jilin, China, in 2014, and M.S. degree in control science and engineering from Harbin Institute of Technology, in 2016. He is currently pursuing his Ph.D. degree in control science and technology with Harbin Institute of Technology. His current research interests include vision measurement, modern optics and machine vision. E-mail: dr_gyzhang@163.com||WEI Liang was born in 1993. He received his B.S. degree in packaging engineering from Hebei Agricultural University, Hebei, China, in 2015, and M.S. degree in electric engineering from Harbin Institute of Technology, in 2019. He is currently pursuing his Ph.D. degree in electric engineering with Harbin Institute of Technology. His current research interests include vision measurement, modern optics, and machine vision. E-mail: weiliangts@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (51875535) and the Natural Science Foundation for Young Scientists of Shanxi Province (201901D211242; 201701D221017).

摘要/Abstract

Abstract:

For localisation of unknown non-cooperative targets in space, the existence of interference points causes inaccuracy of pose estimation while utilizing point cloud registration. To address this issue, this paper proposes a new iterative closest point (ICP) algorithm combined with distributed weights to intensify the dependability and robustness of the non-cooperative target localisation. As interference points in space have not yet been extensively studied, we classify them into two broad categories, far interference points and near interference points. For the former, the statistical outlier elimination algorithm is employed. For the latter, the Gaussian distributed weights, simultaneously valuing with the variation of the Euclidean distance from each point to the centroid, are commingled to the traditional ICP algorithm. In each iteration, the weight matrix ${\boldsymbol{W}} $ in connection with the overall localisation is obtained, and the singular value decomposition is adopted to accomplish high-precision estimation of the target pose. Finally, the experiments are implemented by shooting the satellite model and setting the position of interference points. The outcomes suggest that the proposed algorithm can effectively suppress interference points and enhance the accuracy of non-cooperative target pose estimation. When the interference point number reaches about 700, the average error of angle is superior to 0.88°.

Key words: non-cooperative target, pose estimation, iterative closest point (ICP), Gaussian weight

. [J]. Journal of Systems Engineering and Electronics, 2022, 33(1): 1-10.

Zijian ZHU, Wenhao XIANG, Ju HUO, Ming YANG, Guiyang ZHANG, Liang WEI. Non-cooperative target pose estimation based on improved iterative closest point algorithm[J]. Journal of Systems Engineering and Electronics, 2022, 33(1): 1-10.

图/表 13

参考文献 30

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Result	Ideal value	ICP algorithm	DWICP algorithm
Angle/(°)	$ \alpha = 5 $	$\alpha = 6.016\;4$	$ \alpha = 5.021\;6 $
	$ \beta = 5 $	$\beta = 5.869\;7$	$ \beta = 5.100\;3 $
	$ \gamma = 5 $	$ \gamma = 4.862\;4 $	$ \gamma = 4.930\;5 $
Translation /cm	$ {t_x} = 20 $	$ {t_x} = 22.561\;2 $	$ {t_x} = 21.032\;1 $
	$ {t_y} = 20 $	$ {t_y} = 21.957\;2 $	$ {t_y} = 20.058\;6 $
	$ {t_z} = 20 $	$ {t_z} = 19.462\;3 $	$ {t_z} = 19.864\;7 $

Non-cooperative target pose estimation based on improved iterative closest point algorithm

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