Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-02T23:35:59.772Z Has data issue: false hasContentIssue false
  • English
  • Français

Pose estimation in runway end safety area using geometry structure features

Published online by Cambridge University Press:  20 April 2016

X. Wang ,
H. Yu  and
D. Feng
X. Wang
Affiliation:
School of Astronautics, Northwestern Polytechnical University, China
H. Yu*
Affiliation:
School of Aerospace Science and Technology, Xidian University, China
D. Feng
Affiliation:
School of Aerospace Science and Technology, Xidian University, China
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

A novel image-based method is presented in this paper to estimate the poses of commercial aircrafts in a runway end safety area. Based on the fact that similar poses of an aircraft will have similar geometry structures, this method first extracts features to describe the structure of an aircraft's fuselage and aerofoil by RANdom Sample Consensus algorithm (RANSAC), and then uses the central moments to obtain the aircrafts’ pose information. Based on the proposed pose information, a two-step feature matching strategy is further designed to identify an aircraft's particular pose. In order to validate the accuracy of the pose estimation and the effectiveness of the proposed algorithm, we construct a pose database of two common aircrafts in Asia. The experiments show that the designed low-dimension features can accurately capture the aircraft's pose information and the proposed algorithm can achieve satisfied matching accuracy.

Keywords

Pose estimationRANSACcentral momentrunway monitoring
Type
Research Article
Information
The Aeronautical Journal , Volume 120 , Issue 1226 , April 2016 , pp. 675 - 691
Copyright
Copyright © Royal Aeronautical Society 2016 

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

1. Van Es, G.W.H. A study of runway excursions from a European perspective, NLR-CR-2010-259, 2010, Amsterdam, Netherlands.Google Scholar
2. Pavlin, S. and Bračić, M. Runway end safety area, International Scientific Conference Modern Safety Technologies in Transport-MOSSAT, Zlata Idka, Kosice, Slovakia, 2011, pp 323–327.Google Scholar
3. Dumont, G., Berthiaume, F., St.Laurent, L., Debaque, B. and Prévost, D. AWARE: A video monitoring library applied to the air traffic control context, The 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, Krakow, Poland, 2013, pp 153–158.CrossRefGoogle Scholar
4. Bai, X., Latecki, L.J. and Liu, W.Y. Skeleton pruning by contour partitioning with discrete curve evolution, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29, (3), pp 449–462.Google ScholarPubMed
5. Marco, Z. RANSAC for dummies, Technical Report, URL: http://old.vision.ece.ucsb.edu/~zuliani/Research/RANSAC/docs/RANSAC4Dummies.pdf.Google Scholar
6. Ou, Z.C., Liu, W. and Su, J.H. A bilinear model based solution to object pose estimation with monocular vision for grasping, IEEE International Conference on Robotics and Automation, Beijing, China, 2011, pp 501–506.Google Scholar
7. Xu, H.L., Wand, S.A., Zhang, X.G. and Hua, G.R. Automatic estimation of the object pose for industrial robots, IEEE International Workshop on Imaging Systems and Techniques, Shenzhen, China, 2009, pp 353–358.Google Scholar
8. Zuffi, S., Learndini, A., Catani, F., Fantozzi, S. and Cappello, A. A model-based method for the reconstruction of total knee replacement kinematics, IEEE Transactions on Medical Imaging, 1999, 18, (10), pp 981–991.Google Scholar
9. Robinson, G.P., Tagara, H.D., Duncan, J.S. and Jaffe, C.C. Medical image collection indexing: Shape-based retrieval using KD-trees, Computerized Medical Imaging and Graphics, 1996, 20, (4), pp 209–217.Google Scholar
10. Leng, D.W. and Sun, W.D. Iterative three-dimensional rigid object pose estimation with contour correspondence, IET Image Processing, 2012, 6, (5), pp 569–579.Google Scholar
11. Mitrović, U., Spiclin, Z. and Likar, B.F. 3D-2D registration of cerebral algorithms: A method and evaluation on clinical images, IEEE Transactions on Medical Imaging, 2013, 32, (8), pp 1550–1563.Google Scholar
12. Iwashita, Y., Kurazume, R., Hasegawa, T. and Hara, K. Fast alignment of 3D geometrical models and 2D color images using 2D distance maps, Fifth International Conference on 3-D Digital Imaging and Modeling, Ottawa, Canada, 2005, pp 164–171.Google Scholar
13. Wunsch, P. and Hirzinger, G. Registration of CAD-models to images by iterative inverse perspective matching, Proceedings of the 13th International Conference on Pattern Recognition, Vienna, Austria, 1996, 1, pp 78–83.Google Scholar
14. Cross, A.D.J. and Hancock, E.R. Graph matching with a dual-step EM algorithm, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20, (11), pp 1236–1253.Google Scholar
15. David, P., Daniel, D., DuraiswamI, R. and Samet, H. Evaluation of the SoftPOSIT model to image registration algorithm, University of Maryland Technical Report CAR-TR-974, 2002.Google Scholar
16. Shan, G.L., Ji, B. and Zhou, Y.F. A review of 3D pose estimation from a monocular image sequence, The 2nd International Congress on Image and Signal Processing, Tianjin, China, 2009, pp 1–5.Google Scholar
17. Gao, X.S., Hou, X.R., Tang, J.L. and Cheng, H.F. Complete solution classification for the perspective-three-point problem, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25, (8), pp 930–943.Google Scholar
18. Moreno-Noguer, F., Lepetit, V. and Fua, P. Accurate non-iterative O(n) solution to the PnP problem, IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007, pp 1–8.CrossRefGoogle Scholar
19. Lu, C.P., Hager, G.D. and Mjolsness, E. Fast and globally convergent pose estimation from video images, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22, (6), pp 610–622.Google Scholar
20. Lowe, D.G. Local feature view clustering for 3D object recognition, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii USA, 2001, 1, pp 682–688.Google Scholar
21. Lowe, D.G. Distinctive image features from scale-invariant key points, International, J Computer Vision, 2004, 60, (2), pp 91–110.Google Scholar
22. Robert, S. and Klas, N. An invariant and compact representation for unrestricted pose estimation, Second Iberian Conference on Pattern Recognition and Image Analysis, Estoril, Portugal, 2005.Google Scholar
23. Fischler, M.A. and Bolles, R.C. Random sample consensus: A paradigm for model fitting with applications to image analysis, Communications of the ACM, 1981, 24, (6), pp 381–395.Google Scholar
24. Haralick, R.M. and Shapiro, L.G. Computer and Robot Vision Volume I, 1992, Addison-Wesley, Boston, USA.Google Scholar
25. Sun, J.X. Image Analysis, 2005, Science Press, Beijing, China.Google Scholar
26. Hu, M.K. Visual pattern recognition by moment invariants, IRE Transactions on Information Theory, 1962, 8, (2), pp 179–187.Google Scholar