Citation: | LU Tingting, LI Xiao, ZHANG Yao, et al. A technology for generation of space object optical image based on 3D point cloud model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(2): 274-286. doi: 10.13700/j.bh.1001-5965.2019.0189(in Chinese) |
The lack of the prior image data in the space exploration tasks makes it difficult to quantitatively test and evaluate the situation awareness and navigation algorithms based on the optical images. Accordingly, in this paper, we present an algorithm for generating the synthetic space object optical image based on the 3D point cloud model and the basic theory of the projective transformation. First, the 3D point cloud model of the space object and the optical camera model were constructed. Then, the corresponding pairs between all the pixels in the image plane and the space points of the 3D point cloud model were obtained via the basic theory of projective transformation, and subsequently the intensity of each pixel in the image plane was calculated by the lighting direction of its corresponding space point and the Lambertian reflection model, and finally the simulated image was generated. A great deal of simulation experiments demonstrate that the proposed algorithm can produce the more vivid simulated images rapidly than the traditional analytical image generation algorithm, and the generated images can be applied to testing and evaluating the typical space application algorithms qualitatively and quantitatively, such as ellipse fitting, crater detection, optical navigation landing on the planet, automated rendezvous and docking of spacecraft, 3D tracking of spacecraft, and so on.
[1] |
XIANG Y, SCHMIDT T, NARAYANAN V, et al.PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes[EB/OL].(2017-11-01)[2019-04-01].http://export.arxiv.org/abs/1711.00199. https://www.researchgate.net/publication/320796892_PoseCNN_A_Convolutional_Neural_Network_for_6D_Object_Pose_Estimation_in_Cluttered_Scenes
|
[2] |
LIU C, HU W.Real-time geometric fitting and pose estimation for surface of revolution[J].Pattern Recognition, 2019, 85:90-108. doi: 10.1016/j.patcog.2018.08.002
|
[3] |
CRIVELLARO A, RAD M, VERDIE Y, et al.Robust 3D object tracking from monocular images using stable parts[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(6):1465-1479. doi: 10.1109/TPAMI.2017.2708711
|
[4] |
YU M, CUI H, TIAN Y.A new approach based on crater detection and matching for visual navigation in planetary landing[J].Advances in Space Research, 2014, 53(12):1810-1821. doi: 10.1016/j.asr.2013.04.011
|
[5] |
ZHANG H, JIANG Z, ELGAMMAL A.Satellite recognition and pose estimation using homeomorphic manifold analysis[J].IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(1):785-792. doi: 10.1109/TAES.2014.130744
|
[6] |
ESA.Pinpoint vision-based landings on moon, mars and asteroids[EB/OL].(2013-05-29)[2019-04-01].http://www.esa.int/Our_Activities/Space_Engineering_Technology/Pinpoint_vision-based_landings_on_Moon_Mars_and_asteroids. https://www.researchgate.net/publication/317345295_A_new_experimental_facility_for_testing_of_vision-based_GNC_algorithms_for_planetary_landing
|
[7] |
GSA.TRON-Testbed for robotic optical navigation[EB/OL].(2017-03-29)[2019-04-01].http://www.ngcaerospace.com/space-systems/test-validation-services.
|
[8] |
NGC.High-fidelity hardware-in-the-loop emulators[EB/OL].(2017-03-29)[2019-04-01].http://www.ngcaerospace.com/space-systems/test-validation-services/.
|
[9] |
PARKES S M, MARTIN I.Virtual lunar landscapes for testing vision-guided lunar landers[C]//IEEE International Conference on Information Visualization.Piscataway, NJ: IEEE Press, 1999: 122-127. https://www.researchgate.net/publication/3811947_Virtual_Lunar_Landscapes_for_Testing_Vision-Guided_Lunar_Landers
|
[10] |
STAR-Dundee.PANGU-Planet and asteroid natural scene generation utility[EB/OL].(2017-02-08)[2019-04-01].https://www.star-dundee.com/products/pangu-planet-and-asteroid-natural-scene-generation-utility.
|
[11] |
LU T, HU W, JIANG Z.An effective algorithm for generation of crater gray image[C]//IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications.Piscataway, NJ: IEEE Press, 2015: 1-6. https://www.researchgate.net/publication/290194684_An_effective_algorithm_for_generation_of_crater_gray_image
|
[12] |
SU Q, ZHAO Y, WU F, et al.Simulation of high resolution lunar's Sinus Iridum terrain[C]//IEEE Conference on Industrial Electronics and Applications.Piscataway, NJ: IEEE Press, 2011: 2589-2592.
|
[13] |
LI J S, LIU W M, LAN C Z, et al.Fast algorithm for lunar craters simulation[M].Berlin:Springer, 2011.
|
[14] |
张玥, 李清毅, 许晓霞.月球表面地形数学建模方法[J].航天器环境工程, 2007, 24(6):341-343. doi: 10.3969/j.issn.1673-1379.2007.06.002
ZHANG Y, LI Q Y, XU X X.Mathematical modeling of lunar surface terrain[J].Spacecraft Environment Engineering, 2007, 24(6):341-343(in Chinese). doi: 10.3969/j.issn.1673-1379.2007.06.002
|
[15] |
陈宝林.最优化理论与算法[M].2版.北京:清华大学出版社, 2005:10-23.
CHEN B L.Theory and algorithms of optimization[M].2rd ed.Beijing:Tsinghua University Press, 2005:10-23(in Chinese).
|
[16] |
吴福朝.计算机视觉中的数学方法[M].北京:科学出版社, 2008:255-266.
WU F C.Mathematics in computer vision[M].Beijing:Science Press, 2008:255-266(in Chinese).
|
[17] |
AKENINE-MOLLER T, HAINES E.实时计算机图形学[M].2版.普建涛, 译.北京: 北京大学出版社, 2004: 40-50.
AKENINE-MOLLER T, HAINES E.Real time graphics[M].2nd ed.PU J T, translated.Beijing: Peking University Press, 2004: 40-50(in Chinese).
|
[18] |
NASA.Vesta[EB/OL].(2017-04-28)[2019-04-01].https://nasa3d.arc.nasa.gov/detail/vesta.
|
[19] |
NASA.Eros[EB/OL].(2017-04-28)[2019-04-01].https://nasa3d.arc.nasa.gov/detail/eros.
|
[20] |
The Planetary Society.Mosaic of the asteroid Vesta from the Dawn spacecraft[EB/OL].(2017-04-28)[2019-04-01].http://www.planetary.org/multimedia/space-images/small-bodies/vesta_mosaic_0006121-6124.html/.
|
[21] |
Wikipedia.433 Eros[EB/OL].(2017-04-28)[2019-04-01].https://en.wikipedia.org/wiki/433_Eros.
|
[22] |
LU T, HU W, LIU C, et al.Effective ellipse detector with polygonal curve and likelihood ratio test[J].IET Computer Vision, 2015, 9(6):914-925. doi: 10.1049/iet-cvi.2014.0347
|
[23] |
LU C, HU W.Effective method for ellipse extraction and integration for spacecraft images[J].Optical Engineering, 2013, 52(5):057002. doi: 10.1117/1.OE.52.5.057002
|
[24] |
LU T, HU W, LIU C, et al.Relative pose estimation of a lander using crater detection and matching[J].Optical Engineering, 2016, 55(2):023102. doi: 10.1117/1.OE.55.2.023102
|
[25] |
LIU C, HU W.Relative pose estimation for cylinder-shaped spacecrafts using single image[J].IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(4):3036-3056. doi: 10.1109/TAES.2014.120757
|
[26] |
COHEN J P, LO H Z, LU T, et al.Crater detection via convolutional neural networks[EB/OL].(2016-01-05)[2019-04-01].https://arxiv.org/abs/1601.00978.
|
[27] |
DING W, STEPINSKI T F, MU Y, et al.Sub-kilometer crater discovery with boosting and transfer learning[J].ACM Transactions on Intelligent Systems and Technology, 2011, 2(4):39. http://sil.uc.edu/pdfFiles/tist11.pdf
|