Accurate 3D geometry measurement for non-cooperative spacecraft with an unfocused light-field camera

doi:10.23919/JSEE.2022.000002

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (1): 11-21.doi: 10.23919/JSEE.2022.000002

• ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

Accurate 3D geometry measurement for non-cooperative spacecraft with an unfocused light-field camera

Shengming XU¹(), Shan LU²(), Yueyang HOU²(), Shengxian SHI^1,*()

¹ School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
² Shanghai Key Laboratory of Space Intelligent Control Technology, Shanghai Aerospace Control Technology Institute, Shanghai 201109, China

Received:2020-04-14 Accepted:2021-11-26 Online:2022-01-18 Published:2022-02-22
Contact: Shengxian SHI E-mail:xsm0911@sjtu.edu.cn;9175393@qq.com;houyueyang_hit@163.com;kirinshi@sjtu.edu.cn
About author:|XU Shengming was born in 1994. He received his B.S. degree in Shanghai Jiao Tong University, Shanghai, China, in 2017. He is currently a Ph.D. candidate in Shanghai Jiao Tong University, Shanghai, China. His research interests are experimental fluid mechanics, advanced optical diagnosis in fluid mechanics, light-field particle imaging velocimetry and its applications in turbomachinery. E-mail: xsm0911@sjtu.edu.cn||LU Shan was born in 1982. He received his B.S. degree from the School of Astronautics, Beihang University, Beijing, China, in 2004, and his Ph.D. degree from the School of Astronautics, Beihang University, Beijing, China, in 2009. Currently, he is a research fellow and Deputy Chief Designer of Shanghai Aerospace Control Technology Institute, Shanghai, China. He is the Deputy Director of Shanghai Key Laboratory of Aerospace Intelligent Control Technology, Shanghai, China. His research interests include on-orbit manipulation and intelligent control of spacecraft. E-mail: 9175393@qq.com||HOU Yueyang was born in 1983. He received his B.S. degree from the School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China, in 2006, M.S. degree from the Humanoid and Gorilla Robot & Its Intelligent Motion Control Lab, Harbin Institute of Technology, Harbin, China, in 2008, and Ph.D. degree from the Humanoid asnd Gorilla Robot & Its Intelligent Motion Control Lab, Harbin Institute of Technology, Harbin, China in 2014. Currently, he is a senior engineer of Shanghai Aerospace Control Technology Institute, Shanghai, China. His research interests include on-orbit manipulation and robotics. E-mail: houyueyang_hit@163.com||SHI Shengxian was born in 1980. He received his B.S. degree from National University of Defense Technology, Changsha, China, in 2002, M.S. degree from Central South University, Changsha, China, in 2005, and Ph.D. degree from University of Liverpool, Liverpool, UK, in 2010. He is an associate professor and doctoral supervisor in the School of Mechanical Engineering, Shanghai Jiao Tong University, China. His research interests include non-contact optical flow field diagnosis technology, light-field imaging-based 3D flow field measurement, tomographic 3D flow field measurement, and jet flow. E-mail: kirinshi@sjtu.edu.cn

Abstract

Abstract:

This work explores an alternative 3D geometry measurement method for non-cooperative spacecraft guiding navigation and proximity operations. From one snapshot of an unfocused light-field camera, the 3D point cloud of a non-cooperative spacecraft can be calculated from sub-aperture images with the epipolar plane image (EPI) based light-field rendering algorithm. A Chang’e?3 model (7.2 cm×5.6 cm×7.0 cm) is tested to validate the proposed technique. Three measurement distances (1.0 m, 1.2 m, 1.5 m) are considered to simulate different approaching stages. Measuring errors are quantified by comparing the light-field camera data with a high precision commercial laser scanner. The mean error distance for the three cases are 0.837 mm, 0.743 mm, and 0.973 mm respectively, indicating that the method can well reconstruct 3D geometry of a non-coope-rative spacecraft with a densely distributed 3D point cloud and is thus promising in space-related missions.

Key words: 3D geometry measurement, non-cooperative spacecraft, unfocused light-field camera

Shengming XU, Shan LU, Yueyang HOU, Shengxian SHI. Accurate 3D geometry measurement for non-cooperative spacecraft with an unfocused light-field camera[J]. Journal of Systems Engineering and Electronics, 2022, 33(1): 11-21.

Figures/Tables 13

Fig 1

Fig 2

Fig 3

Fig 4

Fig 5

Fig 6

Fig 7

Fig 8

Fig 9

Table 1

Fig 10

Fig 11

References 37

1	CelesTrak. SATCAT Boxscore. http://www.celestrak.com/satcat/boxscore.php. Accessed on 14th Feb, 2020.
2	HOWARD R T, BRYAN T C DART AVGS flight results. Sensors and Systems for Space Applications, 2007, 6555, 65550L. doi: 10.1117/12.720242
3	BODIN P, NOTEBORN R, LARSSON R, et al The prisma formation flying demonstrator: overview and conclusions from the nominal mission. Advances in the Astronautical Sciences, 2012, 144, 441- 460.
4	KAISER C, SJOlBERG F, DELCURA J, et al SMART-OLEV—an orbital life extension vehicle for servicing commercial spacecrafts in GEO. Acta Astronautica, 2008, 63, 400- 410. doi: 10.1016/j.actaastro.2007.12.053
5	SHAN M H, GUO J, GILL E Review and comparison of active space debris capturing and removal methods. Progress in Aerospace Sciences, 2016, 80, 18- 32. doi: 10.1016/j.paerosci.2015.11.001
6	BONNAL C, RUAULT J, DESJEAN M Active debris removal: recent progress and current trends. Acta Astronautica, 2013, 85, 51- 60. doi: 10.1016/j.actaastro.2012.11.009
7	OPROMOLLA R, FASANO G, RUFINO G, et al A review of cooperative and uncooperative spacecraft pose determination techniques for close-proximity operations. Progress in Aerospace Sciences, 2017, 93, 53- 72. doi: 10.1016/j.paerosci.2017.07.001
8	ALDOMA A, VINCZE M, BLODOW N, et al. CAD-model recognition and 6DOF pose estimation using 3D cues. Proc. of the IEEE International Conference on Computer Vision Workshops, 2011: 585−592.
9	ALDOMA A, MARTON Z, TOMBARI F, et al Three-dimensional object recognition and 6 DOF pose estimation. IEEE Robotics & Automation Magazine, 2012, 19 (3): 80- 91.
10	KEHOE B, MATSUKAWA A, CANDIDO S, et al. Cloud-based robot grasping with the Google object recognition engine. Proc. of the IEEE International Conference on Robotics and Automation, 2013: 4263−4270.
11	MATTHIES L, KANADE T, SZELISKI R Kalman filter-based algorithms for estimating depth from image sequences. International Journal of Computer Vision, 1989, 3, 209- 238. doi: 10.1007/BF00133032
12	YE M, WANG X W, YANG R G, et al. Accurate 3D pose estimation from a single depth image. Proc. of the International Conference on Computer Vision, 2011: 731−738.
13	CHRISTIAN J A, CRYAN S. A survey of LIDAR technology and its use in spacecraft relative navigation. Proc. of the AIAA Guidance, Navigation, and Control Conference, 2013: 4641.
14	DURRANT-WHYTE H, BAILEY T Simultaneous localization and mapping: part I. IEEE Robotics & Automation Magazine, 2006, 13 (2): 99- 110.
15	SUBBARAO M, SURYA G Depth from defocus: a spatial domain approach. International Journal of Computer Vision, 1994, 13 (3): 271- 294. doi: 10.1007/BF02028349
16	SAXENA A, CHUNG S H, NG A Y Learning depth from single monocular images. Neural Information Processing Systems, 2005, 18, 1- 8.
17	XIAN K, SHEN C H, CAO Z G, et al. Monocular relative depth perception with web stereo data supervision. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 311−320.
18	LUKAC R. Computational photography: methods and applications. Boca Raton: CRC Press, 1991.
19	LINGENAUBER M, STROBL K H, OUMER N W, et al. Benefits of plenoptic cameras for robot vision during close range on-orbit servicing maneuvers. Proc. of the IEEE Aerospace Conference, 2017: 1−18.
20	YANG L, WANG B Q, ZHANG R H, et al Analysis on location accuracy for the binocular stereo vision system. IEEE Photonics Journal, 2017, 10 (1): 1- 16.
21	GEORGIEV T, LUMSDAINE A. Superresolution with plenoptic 2.0 cameras. Signal Recovery & Synthesis, 2009. DOI: 10.1364/SRS.2009.STuA6.
22	NG R, LEVOY M, BREDIF M, et al. Light field photography with a hand-held plenoptic camera. Stanford, US: Stanford University, 2005.
23	GEORGIEV T G, ZHENG K C, CURLESS B, et al. Spatio-angular resolution tradeoffs in integral photography. Rendering Techniques, 2016, 21: 263−272.
24	ADELSON E H, JAMES R. The plenoptic function and the elements of early vision. Massachusetts: Massachusetts Institute of Technology, 1991.
25	MCMILLAN L, BISHOP G. Plenoptic modeling: an image-based rendering system. Proc. of the 22nd annual Conference on Computer graphics and interactive techniques, 1995: 39−46.
26	SHI S X, WANG J H, DING J F, et al Parametric study on light field volumetric particle image velocimetry. Flow Measurement and Instrumentation, 2016, 49, 70- 88. doi: 10.1016/j.flowmeasinst.2016.05.006
27	BAY H, ESS A, TUYTEKAARS T, et al Speeded-up robust features (SURF). Computer Vision and Image Understanding, 2008, 110 (3): 346- 359. doi: 10.1016/j.cviu.2007.09.014
28	XU W F, LIANG B, LI C, et al Autonomous rendezvous and robotic capturing of non-cooperative target in space. Robotica, 2010, 28 (5): 705- 718. doi: 10.1017/S0263574709990397
29	LIU J, DING H H, SHAHROUDY A, et al Feature boosting network for 3D pose estimation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2019, 42 (2): 494- 501.
30	ZHU K, XUE Y J, FU Q, et al Hyperspectral light field stereo matching. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2018, 41 (5): 1131- 1143.
31	WU G C, ZHAO M D, WANG L Y, et al. Light field reconstruction using deep convolutional network on EPI. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 6319−6327.
32	LIN H T, CHEN C, KANG S B, et al. Depth recovery from light field using focal stack symmetry. Proc. of the IEEE International Conference on Computer Vision, 2015: 3451−3459.
33	JEON H G, PARK J, CHOE G M, et al Depth from a light field image with learning-based matching costs. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2018, 41 (2): 297- 310.
34	RUZON M A, TOMASI C. Color edge detection with the compass operator. Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, 2: 160−166.
35	ZHANG S, SHENG H, LI C, et al Robust depth estimation for light field via spinning parallelogram operator. Computer Vision and Image Understanding, 2016, 145, 148- 159. doi: 10.1016/j.cviu.2015.12.007
36	WERMAN M, PELEG S, ROSENFELD A A distance metric for multidimensional histograms. Computer Vision, Graphics, and Image Processing, 1985, 32 (3): 328- 336. doi: 10.1016/0734-189X(85)90055-6
37	SHI S X, DING J F, NEW T H, et al Volumetric calibration enhancements for single-camera light-field PIV. Experiments in Fluids, 2019, 60 (1): 21. doi: 10.1007/s00348-018-2670-5

$ {s}_{o} $ /m	FOV /mm	Repeated times	Number of points	$ {d}_{{\rm{mean}}} $ /mm
1.0	69×52	1	57 000	0.837
1.2	102×77	1	68 000	0.743
1.2	102×77	30	70 000 Average	0.750 $ \pm $ 0.011
1.5	131×99	1	37 000	0.973

Accurate 3D geometry measurement for non-cooperative spacecraft with an unfocused light-field camera

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 13

References 37

Related Articles 0

Recommended Articles

Metrics

Comments