Hostname: page-component-76d6cb85b7-7262s Total loading time: 0 Render date: 2026-07-15T15:04:51.961Z Has data issue: false hasContentIssue false
  • English
  • Français

Optical Image Generation and High-precision Line-of-Sight Extraction for Mars Approach Navigation

Published online by Cambridge University Press:  03 July 2018

Xiuqiang Jiang ,
Shuang Li Open the ORCID record for Shuang Li [Opens in a new window] ,
Long Gu ,
Jun Sun  and
Dongdong Xiao
Xiuqiang Jiang
Affiliation:
(College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China) (Advanced Space Technology Laboratory, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)
Shuang Li*
Affiliation:
(College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China) (Advanced Space Technology Laboratory, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)
Long Gu
Affiliation:
(College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China) (Advanced Space Technology Laboratory, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)
Jun Sun
Affiliation:
(Shanghai Aerospace Control Technology Institute, Shanghai 201109, China)
Dongdong Xiao
Affiliation:
(Shanghai Aerospace Control Technology Institute, Shanghai 201109, China)
*
(E-mail: lishuang@nuaa.edu.cn)
Get access Rights & Permissions [Opens in a new window]

Abstract

A high-precision line-of-sight extraction technique is essential for autonomous optical navigation during the Mars approach phase. To support future Mars exploration missions, an optical image simulation system is a necessary ground verification facility for Mars image generation and line-of-sight extraction algorithm tests. In this paper, an optical image generation procedure is first developed according to projection relationships, reference flight profiles and camera parameters. Next, a hybrid image processing and subpixel-level line-of-sight extraction algorithm is proposed through modification of moment-based sub-pixel edge detection and improvement of direct least-square fitting approaches. Finally, an optical image simulation system is established, and the experimental results show that the proposed procedure can effectively simulate the optical image in the field-of-view of a Mars spacecraft, and the hybrid extraction algorithm can obtain high-precision Mars centroid information.

Keywords

Autonomous optical navigationSimulated image generationLine-of-sight extractionSub-pixel edge detectionRobust ellipse fitting

Information

Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 1 , January 2019 , pp. 229 - 252
Copyright
Copyright © The Royal Institute of Navigation 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Balster, S.K. (2005). An earth image simulation and tracking system for the Mars Laser Communication Demonstration. Master Dissertation, Massachusetts Institute of Technology.Google Scholar
Bhaskaran, S., Riedel, J.E., Synnott, S.P. and Wang, T.C. (2000). The deep space 1 autonomous navigation system: a post-flight analysis. Proceedings of the Astrodynamics Specialist Conference, Guidance, Navigation, and Control and Co-located Conferences, AIAA-2000-3935.Google Scholar
Christian, J. and Lightsey, G., (2010). Integrated Performance of an Autonomous Optical Navigation System for Space Exploration. Proceedings of the AIAA SPACE 2010 Conference and Exposition, Anaheim, California, AIAA-2010-8786.Google Scholar
Christian, J.A. (2010). Optical navigation for a spacecraft in a planetary system. Doctoral Dissertation, Massachusetts Institute of Technology.Google Scholar
Christian, J.A. (2017). Accurate planetary limb localization for image-based spacecraft navigation. Journal of Spacecraft and Rockets, 54(3), 708–729.Google Scholar
Christian, J.A. and Lightsey, G. (2012). Onboard image-processing algorithm for a spacecraft optical navigation sensor system. Journal of Spacecraft and Rockets, 49(2), 337–352.Google Scholar
Du, S., Wang, M., Chen, X., Fang, S. and Su, H. (2016). A High-accuracy extraction algorithm of planet centroid image in deep-space autonomous optical navigation. Journal of Navigation, 69(4), 828–844.Google Scholar
Fitzgibbon, A., Pilu, M. and Fisher, R.B. (1999). Direct least square fitting of ellipses. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(5), 476–480.Google Scholar
Huang, A.Y., Cui, W. and Li, H.N. (2015). Semi-physical simulation system of autonomous optical navigation for Mars-approaching phase. Proceedings of the IEEE Guidance, Navigation and Control Conference, 1409–1414.Google Scholar
Huang, M., Yu, W. and Zhu, D. (2012). An improved image segmentation algorithm based on the Otsu method. Proceedings of the 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel & Distributed Computing (SNPD), IEEE, 135–139.Google Scholar
Jiang, X., Yang, B. and Li, S. (2018). Overview of China's 2020 Mars mission design and navigation. Astrodynamics, 2(1), 1–11.Google Scholar
Kubota, T., Hashimoto, T., Kawaguchi, J., Uo, M. and Shirakawa, K. (2006). Guidance and navigation of Hayabusa spacecraft for asteroid exploration and sample return mission. Proceedings of the SICE-ICASE International Joint Conference.Google Scholar
Li, S., Lu, R., Zhang, L. and Peng, Y. (2013). Image processing algorithms for deep-space autonomous optical navigation. Journal of Navigation, 66(4), 605–623.Google Scholar
Lu, R. (2013). Study on autonomous optical navigation technology for deep space probe. Master Dissertation, Nanjing University of Aeronautics and Astronautics.Google Scholar
Lyvers, E., Mitchell, O., Akey, M. and Reeves, A. (1989). Subpixel measurements using a moment-based edge operator. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(12), 1293–1309.Google Scholar
Ma, P., Wang, T., Jiang, F., Mu, J. and Baoyin, H. (2017). Autonomous navigation of Mars probes by single X-ray pulsar measurement and optical data of viewing Martian moons. Journal of Navigation, 70(1), 18–32.Google Scholar
Mastrodemos, N., Kubitschek, D.G. and Synnott, S.P. (2005). Autonomous navigation for the deep impact mission encounter with comet Tempel 1. Space Science Reviews, 117(1–2), 95–121.Google Scholar
Mortari, D., D'Souza, C.N. and Zanetti, R. (2016). Image processing of illuminated ellipsoid. Journal of Spacecraft and Rockets, 53(3), 448–456.Google Scholar
Mortari, D., de Dilectis, F. and Zanetti, R. (2015). Position estimation using the image derivative. Aerospace, 2(3), 435–460.Google Scholar
Owen, W.M. Jr., Wang, T.C., Harch, A., Bell, M. and Peterson, C. (2001). NEAR Optical Navigation at Eros. Proceedings of the AAS/AIAA Astrodynamics Specialist Conference, Quebec, Canada.Google Scholar
Quadrelli, M.B., Wood, L.J., Riedel, J.E., McHenry, M.C., Aung, M., Cangahuala, L.A., Volpe, R.A., Beauchamp, P.M. and Cutts, J.A. (2015). Guidance, navigation, and control technology assessment for future planetary science missions. Journal of Guidance, Control, and Dynamics, 38(7), 1165–1186.Google Scholar