Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-05T05:40:16.573Z Has data issue: false hasContentIssue false
  • English
  • Français

Synthetic photometric landmarks used for absolute navigation near an asteroid

Published online by Cambridge University Press:  13 May 2020

O. Knuuttila Open the ORCID record for O. Knuuttila [Opens in a new window] ,
A. Kestilä Open the ORCID record for A. Kestilä [Opens in a new window]  and
E. Kallio Open the ORCID record for E. Kallio [Opens in a new window]
O. Knuuttila*
Affiliation:
Department of Electronics and Nanoengineering, Aalto University School of Electrical Engineering, Espoo, Finland
A. Kestilä
Affiliation:
Finnish Meteorological Institute, Helsinki, Finland
E. Kallio
Affiliation:
Department of Electronics and Nanoengineering, Aalto University School of Electrical Engineering, Espoo, Finland
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The need for autonomous location estimation in the form of optical navigation is an essential requirement for forthcoming deep space missions. While crater-based navigation might work well with larger bodies littered with craters, small sub-kilometer bodies do not necessarily have them. We have developed a new pose estimation method for absolute navigation based on photometric local feature extraction techniques thus making it suitable for missions that cannot rely on craters. The algorithm can be used by a navigation filter in conjunction with relative pose estimation such as visual odometry for additional robustness and accuracy. To estimate the position and orientation of the spacecraft in the asteroid-fixed coordinate frame, it uses navigation camera images in combination with other readily available information, such as orientation relative to the stars and the current time for an initial estimate of the asteroid rotation state. Evaluation of the algorithm when using different feature extractors is performed, on one hand, using Monte Carlo simulations and, on the other hand, using actual images taken by the Rosetta spacecraft orbiting the comet 67P/Churyumov–Gerasimenko. Our analysis, where four different feature extraction methods (AKAZE, ORB, SIFT, SURF) were compared, showed that AKAZE is most promising in terms of stability and accuracy.

Keywords

Visual navigationVision-based navigationOptical navigationVision-based absolute navigationMonocular visual navigationAsteroid proximity navigationPhotometric feature-based navigation
Type
Research Article
Information
The Aeronautical Journal , Volume 124 , Issue 1279 , September 2020 , pp. 1281 - 1300
Copyright
© The Author(s) 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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.)

References

REFERENCES

ESA. Cubesats joining hera mission to asteroid system. [Online]. Available: http://www.esa.int/ Our_Activities/Space_Engineering_Technology/Hera/CubeSats_joining_Hera_mission_to_aster- oid_system (Accessed on 12 Feb 2019).Google Scholar
Kohout, T., Näsilä, A., Tikka, T., Granvik, M., Kestilä, A., Penttilä, A., Kuhno, J., Muinonen, K., Viherkanto, K. and Kallio, E.Feasibility of asteroid exploration using CubeSats—ASPECT case study, Adv. Space Res., 2018, 62, (8), pp 2239–2244.CrossRefGoogle Scholar
Michel, P., Kueppers, M., Sierks, H., Carnelli, I., Cheng, A.F., Mellab, K., Granvik, M., Kestilä, A., Kohout, T., Muinonen, K.et al. European component of the AIDA mission to a binary asteroid: Characterization and interpretation of the impact of the DART mission, Adv. Space Res., 2018, 62, (8), pp 2261–2272.CrossRefGoogle Scholar
Naidu, S., Benner, L., Brozovic, M., Ostro, S., Nolan, M., Margot, J., Giorgini, J., Magri, C., Pravec, P., Scheirich, P.et al. Observations and characterization of binary near-earth asteroid 65803 didymos, the target of the AIDA mission, in AGU Fall Meeting Abstracts, 2016.Google Scholar
ESA. Esa planetary science archive. [Online]. Available: https://imagearchives.esac.esa.int/ index.php?/page/navcam_3d_models (Accessed on 30 Jan 2019).Google Scholar
ESA. Esa archive image browser. [Online]. Available: http://imagearchives.esac.esa.int/index. php?/category/63 (Accessed on 30 Jan 2019).Google Scholar
Owen, W., Jr., Wang, T., Harch, A., Bell, M. and Peterson, C. NEAR optical navigation at Eros, Jet Propulsion Laboratory, California Institute of Technology, Technical report, 2001.Google Scholar
de Santayana, R.P. and Lauer, M. Optical measurements for rosetta navigation near the comet, in Proceedings of the 25th International Symposium on Space Flight Dynamics (ISSFD), Munich, 2015.Google Scholar
Mastrodemos, N., Rush, B., Vaughan, D. and Owen, B. Optical navigation for Dawn at Vesta, in 21st AAS/AIAA Space Flight Mechanics Meetings. Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2011. [Online]. Available: https://trs.jpl.nasa.gov/handle/2014/41960.Google Scholar
Lorenz, D.A., Olds, R., May, A., Mario, C., Perry, M.E., Palmer, E.E. and Daly, M. Lessons learned from OSIRIS-Rex autonomous navigation using natural feature tracking, in 2017 IEEE Aerospace Conference. IEEE, 2017, pp 1–12.CrossRefGoogle Scholar
Miso, T., Hashimoto, T. and Ninomiya, K.Optical guidance for autonomous landing of spacecraft, IEEE Trans. Aerosp. Electron. Syst., 1999, 35, (2), pp 459–473.CrossRefGoogle Scholar
Hashimoto, T., Kubota, T., Kawaguchi, J., Uo, M., Shirakawa, K., Kominato, T. and Morita, H.Vision-based guidance, navigation, and control of Hayabusa spacecraft-Lessons learned from real operation, IFAC Proc. Vol., 2010, 43, (15), pp 259–264.CrossRefGoogle Scholar
Lowe, D.G.Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vis., 2004, 60, (2), pp 91–110.CrossRefGoogle Scholar
Bay, H., Ess, A., Tuytelaars, T. and Van Gool, L. Speeded-up robust features (SURF), Comput. Vis. Image Understand., 2008, 110, (3), pp 346–359.CrossRefGoogle Scholar
Leutenegger, S., Chli, M. and Siegwart, R. BRISK: Binary robust invariant scalable keypoints, in 2011 IEEE international Conference on Computer Vision (ICCV). IEEE, 2011, pp 2548–2555.CrossRefGoogle Scholar
Rublee, E., Rabaud, V., Konolige, K. and Bradski, G.R. ORB: An efficient alternative to SIFT or SURF, in ICCV, vol. 11(1). Citeseer, 2011, p 2.CrossRefGoogle Scholar
Takeishi, N., Tanimoto, A., Yairi, T., Tsuda, Y., Terui, F., Ogawa, N. and Mimasu, Y.Evaluation of interest-region detectors and descriptors for automatic landmark tracking on asteroids, Trans. Japan Soc. Aeronaut. Space Sci., 2015, 58, (1), pp 45–53, 2015.CrossRefGoogle Scholar
Theil, S., Ammann, N., Andert, F., Franz, T., Krger, H., Lehner, H., Lingenauber, M., Ldtke, D., Maass, B., Paproth, C.et al. ATON (Autonomous Terrain-based Optical Navigation) for exploration missions: recent flight test results, CEAS Space J., 2018, 10, (3), pp 325–341.CrossRefGoogle Scholar
Lucas, B.D., Kanade, T.et al., An iterative image registration technique with an application to stereo vision, in Darpa Image Understanding Workshop. Vancouver, British Columbia, April 1981, pp 121–130.Google Scholar
Shi, J. and Tomasi, C. Good features to track, Cornell University, Technical report, 1993.Google Scholar
Strasdat, H., Montiel, J. and Davison, A.J. Real-time monocular SLAM: Why filter?, in 2010 IEEE International Conference on Robotics and Automation. IEEE, 2010, pp 2657–2664.CrossRefGoogle Scholar
Younes, G., Asmar, D. and Shammas, E., A survey on non-filter-based monocular visual SLAM systems, arXiv preprint arXiv:1607.00470, vol. 413, p. 414, 2016.Google Scholar
Kaufmann, H., Lingenauber, M., Bodenmueller, T. and Suppa, M. Shadow-based matching for precise and robust absolute self-localization during lunar landings, in 2015 IEEE Aerospace Conference. IEEE, 2015, pp 1–13.CrossRefGoogle Scholar
Ansar, A. and Cheng, Y. Vision technologies for small body proximity operations, in Proceedings of the International Symposium on Artificial Intelligence and Robotics & Automation in Space, 2010.Google Scholar
Wang, Y., Zhang, W. and An, P.A survey of simultaneous localization and mapping on unstructured lunar complex environment, in AIP Conference Proceedings, vol. 1890(1). AIP Publishing, 2017, p 030010.CrossRefGoogle Scholar
Ciarambino, M., Lunghi, P., Losi, L. and Lavagna, M., Development validation and test of optical based algorithms for autonomous planetary landing, in Proc. ICATT 2016, 2016.Google Scholar
Delaune, J., Le Besnerais, G., Voirin, T., Farges, J.-L. and Bourdarias, C.Visual–inertial navigation for pinpoint planetary landing using scale-based landmark matching, Robot. Autonom. Syst., 2016, 78, pp 63–82.CrossRefGoogle Scholar
Alcantarilla, P.F. and Solutions, T.Fast explicit diffusion for accelerated features in nonlinear scale spaces, IEEE Trans. Patt. Anal. Mach. Intell., 2011, 34, (7), pp 1281–1298.Google Scholar
OpenCV Foundation. Open source computer vision library. [Online]. Available: https:// opencv.org/about/ (Accessed on 20 May 2019).Google Scholar
Chien, H.-J., Chuang, C.-C., Chen, C.-Y. and Klette, R. When to use what feature? SIFT, SURF, ORB, or A-KAZE features for monocular visual odometry, in 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2016, pp 1–6.CrossRefGoogle Scholar
ESA. Esa archive image browser. [Online]. Available: http://imagearchives.esac.esa.int/index. php?/category/56 (Accessed on 30 Jan 2019).Google Scholar
Bate, R.R., Mueller, D.D. and White, J.E.Fundamentals of Astrodynamics. Courier Corporation, 1971.Google Scholar
Pravec, P., Harris, A., Vokrouhlickỳ, D., Warner, B., MKušniràk, P., Hornoch, K., Pray, D., Higgins, D., Oey, J., Gald, A.et al. Spin rate distribution of small asteroids, Icarus, 2008, 197, (2), pp 497–504.CrossRefGoogle Scholar
Rowell, N., Dunstan, M., Parkes, S., Gil-Fernàndez, J., Huertas, I. and Salehi, S. 2015. Autonomous visual recognition of known surface landmarks for optical navigation around asteroids, Aeronaut. J., 2015, 119, (1220), pp 1193–1222.Google Scholar
Pellacani, A., Cabral, F., Alcalde, A., Kicman, P., Lisowski, J., Gerth, I. and Burmann, B.Semi-autonomous attitude guidance using relative navigation based on line of sight measurements–AIM scenario, Acta Astronaut., 2018, 152, pp 496–508.CrossRefGoogle Scholar
Harris, A.W. and Pravec, P., Rotational properties of asteroids, comets and TNOs, Proc. Int. Astron. Union, 2005, 1, (S229), pp 439–447.CrossRefGoogle Scholar
Vokrouhlickỳ, D., Breiter, S., Nesvornỳ, D. and Bottke, W.Generalized YORP evolution: Onset of tumbling and new asymptotic states, Icarus, 2007, 191, (2), pp 636–650.CrossRefGoogle Scholar
Zheng, L., Yang, Y. and Tian, Q.SIFT meets CNN: A decade survey of instance retrieval, IEEE Trans. Patt. Anal. Mach. Intell., 2018, 40, (5), pp 1224–1244.CrossRefGoogle ScholarPubMed
McEwen, A.S.Photometric functions for photoclinometry and other applications, Icarus, 1991, 92, (2), 298–311.CrossRefGoogle Scholar
Grumpe, A., Schrer, C., Kauffmann, S., Fricke, T., Wöhler, C. and Mall, U.Refinement of stereo image analysis using photometric shape recovery as an alternative to bundle adjustment, Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci., 2016, 41.Google Scholar
Hapke, B. Theory of Reflectance and Emittance Spectroscopy. Cambridge University Press, 2012.Google Scholar
Fornasier, S., Hasselmann, P., Barucci, M., Feller, C., Besse, S., Leyrat, C., Lara, L., Gutierrez, P.J., Oklay, N., Tubiana, C.et al. Spectrophotometric properties of the nucleus of comet 67P/Churyumov-Gerasimenko from the OSIRIS instrument onboard the ROSETTA spacecraft, Astron. Astrophys., 2015, 583, p A30.CrossRefGoogle Scholar
Williams, L. Casting curved shadows on curved surfaces, in Seminal Graphics. ACM, 1998, pp 51–55.CrossRefGoogle Scholar
Muja, M. and Lowe, D.G.Fast approximate nearest neighbors with automatic algorithm configuration, VISAPP (1), 2009, 2, (331–340), p 2.Google Scholar
Fischler, M.A. and Bolles, R.C. (1981) Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Commun. ACM, 1981, 24, (6), pp 381–395.CrossRefGoogle Scholar
Ke, T. and Roumeliotis, S.I. An efficient algebraic solution to the perspective-three-point problem, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp 7225–7233.CrossRefGoogle Scholar
Bayard, D.S. and Brugarolas, P.B.On-board vision-based spacecraft estimation algorithm for small body exploration, IEEE Trans. Aerosp. Electron. Syst., 2008, 44, (1), pp 243–260.CrossRefGoogle Scholar
Knuuttila, O.J.M. Visual navigation algorithms and test framework. [Online]. Available: https://github.com/oknuutti/visnav-py/tree/55bee608648f377b5f4bfd8c241d893d6497d1d7 (Accessed on 15 May 2019).Google Scholar
Malmer, M. 67p/c-g shape model. [Online]. Available: http://mattias.malmer.nu/67pc-g-shapemodel/ (Accessed on 13 May 2019).Google Scholar
Fialho, M.A. and Mortari, D.Theoretical limits of star sensor accuracy, Sensors, 2019, 19, (24), p 5355.CrossRefGoogle ScholarPubMed
Bowell, E., Oszkiewicz, D., Wasserman, L., Muinonen, K., Penttilä, A. and Trilling, D.E.Asteroid spin-axis longitudes from the Lowell Observatory database, Meteorit. Planet. Sci., 2014, 49, (1), pp 95–102.CrossRefGoogle Scholar