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

• ELECTRONICS TECHNOLOGY • Next Articles

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).

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

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.

Figures/Tables 13

Fig 1

Fig 2

Fig 3

Fig 4

Fig 5

Fig 6

Fig 7

Table 1

Fig 8

Fig 9

Fig 10

Fig 11

References 30

1	AKHLOUMADI M, IVANOV D Influence of satellite motion control system parameters on performance of space debris capturing. Aerospace, 2020, 7 (11): 160. doi: 10.3390/aerospace7110160
2	AN J P, LI X H, ZHANG Z B, et al. Joint trajectory planning of space modular reconfigurable satellites based on kinematic model. International Journal of Aerospace Engineering, 2020. DOI: 10.1155/2020/8872788.
3	MONTENBRUCK O, STEIGENBERGER P, AICHER M A long-term broadcast ephemeris model for extended operation of GNSS satellites. Navigation, 2020, 68 (1): 199- 215.
4	DENDORFER P, OSEP A, MILAN A, et al MOTChallenge: a benchmark for single-camera multiple target tracking. International Journal of Computer Vision, 2020, 129 (4): 845- 881.
5	NEELAKANTAN R, RAMANAN R V Design of multi-revolution orbits in the framework of elliptic restricted three-body problem using differential evolution. Journal of Astrophysics and Astronomy, 2021, 42 (1): 5. doi: 10.1007/s12036-020-09651-w
6	LING Q, DONG T S, TAN Z Y, et al New attitude control accuracy assessment method for agile remote sensing satellite. Journal of Spacecraft Engineering, 2019, 28 (6): 8- 14.
7	AYOUB K, ABDELMALEK T, ALI K, et al Multivariate copula statistical model and weighted sparse classification for radar image target recognition. Computers and Electrical Engineering, 2020, 84, 106633. doi: 10.1016/j.compeleceng.2020.106633
8	KIM H U, KOH Y J, KIM C S Online multiple object tracking based on open-set few-shot learning. IEEE Access, 2020, 8, 190312- 190326.
9	AL-AHMED S A, SHAKIR M Z, ZAIDI S A R Optimal 3D UAV base station placement by considering autonomous coverage hole detection, wireless backhaul and user demand. Journal of Communications and Networks, 2020, 22 (6): 467- 475. doi: 10.23919/JCN.2020.000034
10	GOLOVAN A, CEPE A Determination of trajectory parameters for a small satellite using raw satellite measurements. Moscow University Mechanics Bulletin, 2016, 71 (1): 19- 22. doi: 10.3103/S0027133016010040
11	WU Z H, FAN B X, WANG F L Automatic attitude measurement algorithm of satellite sensor. Journal of Surveying and Mapping Science and Technology, 2018, 35 (6): 574- 578.
12	SUN Z Y, GAO Y Relative position and attitude measurement for non-cooperative spacecraft based on binocular vision. Journal of Aerospace Metrology and Measurement, 2017, 37 (4): 1- 6.
13	SHU A, PEI H D, DING L, et al Binocular visual position and attitude measurement method for spatial non-cooperative target. Acta Optics, 2020, 40 (17): 1712003. doi: 10.3788/AOS202040.1712003
14	WU Z H. Research on vision-based relative position and attitude measurement technology for close-range spacecraft. Nanjing, China: Nanjing University of Posts and Telecommunications, 2019. (in Chinese)
15	ZHU L Y. Research on pose estimation and 3D reconstruction of non-cooperative target based on structured light vision. Harbin, China: Harbin Institute of Technology, 2019. (in Chinese)
16	ZHANG Z Y. Research on vision based satellite motion analysis pose measurement and capture position detection. Harbin, China: Harbin Institute of Technology, 2019. (in Chinese)
17	ZHANG Z. Research on non-cooperative target reconstruction and posture measurement based on active and passive fusion. Anhui, China: University of Chinese Academy of Sciences, 2019. (in Chinese)
18	LU R R, SUN H B, FU S F, et al Point cloud registration based satellite motion parameter identification method. Laser and Optoelectronics Progress, 2019, 56 (14): 141503. doi: 10.3788/LOP56.141503
19	QI J T, SONG D L, WU C, et al. KF-based adaptive UKF algorithm and its application for rotorcraft UAV actuator failure estimation. International Journal of Advanced Robotic Systems, 2012. DOI: http://doi.org/10.5772/51893.
20	AGHILI F, KURYLLO M, OKOUNEVA G, et al. Robust vision-based pose estimation of moving objects for automated rendezvous & docking. Proc. of the IEEE International Conference on Mechatronics and Automation, 2010, 305–311.
21	DU S Y, XU Y T, WAN T, et al Robust iterative closest point algorithm based on global reference point for rotation invariant registration. PLoS One, 2017, 12 (11): e0188039. doi: 10.1371/journal.pone.0188039
22	HAMIDIAN H, ZHONG Z C, FOTOUHI F, et al Surface registration with eigenvalues and eigenvectors. IEEE Trans. on Visualization and Computer Graphics, 2019, 26 (11): 3327- 3339.
23	WANG X, ZHANG M M, YU X, et al Point cloud registration based on improved iterative closest point method. Optics and Precision Engineering, 2012, 20 (9): 2068- 2077. doi: 10.3788/OPE.20122009.2068
24	KISANTAL M, SHARMA S, PARK T H, et al Satellite pose estimation challenge: dataset, competition design and results. IEEE Trans. on Aerospace and Electronic Systems, 2020, 56 (5): 4083- 4098. doi: 10.1109/TAES.2020.2989063
25	SCHNITZER F, JANSCHEK K, WILLICH G, et al. Robust feature detection, acquisition and tracking for relative navigation in space with a known target. Proc. of the AIAA Guidance Navigation and Control Conference, 2013. DOI: https://doi.org/10.2514/6.2013-5197.
26	NOH K, PANG K S Theoretical consideration of the properties of intestinal flow models on route-dependent drug removal: segregated flow (SFM) vs. traditional (TM). Biopharmaceutics Drug Disposition, 2019, 40 (5/6): 195- 213.
27	SUN D Z, SHEN J H, LI Y R, et al. Initial registration method of cloud based on sequence images. Journal of Mechanical Engineering, 2020, 56(9): 215–222. (in Chinese)
28	CHEN H, FENG Y, YANG J, et al 3D reconstruction approach for outdoor scene based on multiple point cloud fusion. Springer India, 2019, 47 (10): 1761- 1772.
29	WANG Y L, DENG N, XIN B J Investigation of 3D surface profile reconstruction technology for automatic evaluation of fabric smoothness appearance. Measurement, 2020, 166, 108264. doi: 10.1016/j.measurement.2020.108264
30	XIE D X, XU Y C, WANG R D. Obstacle detection and tracking method for autonomous vehicle based on three-dimensional LiDAR. International Journal of Advanced Robotic Systems, 2019, 16(2): 172988141983158.

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

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 13

References 30

Related Articles 1

Recommended Articles

Metrics

Comments