Abstract

In the target recognition of laser radar (ladar), the accurate estimation of target pose can effectively simplify the recognition process. To achieve 3D pose estimation of rigid objects on the ground and simplify the complexity of the algorithm, a novel pose estimation method is proposed in this paper. In this approach, based on the feature that most rigid objects on the ground have large planar areas which are horizontal on the top of the targets and vertical sides and combined with the 3D geometric characteristics of ladar range images, the planar normals of rigid targets were adopted as the vectors in the positive direction of the axes in the model coordinate system to estimate the 3D pose angles of targets. The simulation experiments were performed with six military vehicle models and the performance in self-occlusion, occlusion, and noise was investigated. The results show that the estimation errors are less than 2° in self-occlusion. For the tank LECRERC model, as long as the upper and side planes of the target are not completely occluded, even though the occlusion reaches 80%, the pose angles can be estimated with the estimation error less than 2.5°. Moreover, the proposed method is robust to noise and effective.

© 2013 Optical Society of America

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  1. C. S. L. Chun and F. A. Sadjadi, “Target recognition study using polarimetric laser radar,” Proc. SPIE 5426, 274–284 (2004).
    [CrossRef]
  2. R. D. Nieves and W. D. Reynolds, “Three-dimensional transformation for automatic target recognition using LIDAR data,” Proc. SPIE 7684, 76840Y (2010).
    [CrossRef]
  3. Q. Wang, L. Wang, and J. F. Sun, “Rotation-invariant target recognition in LADAR range imagery using model matching approach,” Opt. Express 18, 15349–15360 (2010).
    [CrossRef]
  4. Z. J. Liu, Q. Li, Z. W. Xia, and Q. Wang, “Target recognition of ladar range images using even-order Zernike moments,” Appl. Opt. 51, 7529–7536 (2012).
    [CrossRef]
  5. J. Neulist and W. Armbruster, “Segmentation, classification, and pose estimation of military vehicles in low resolution laser radar images,” Proc. SPIE 5791, 218–225 (2005).
    [CrossRef]
  6. L. F. Ramon, F. Sira, D. C. Jose, and B. Xavier, “Target detection in Ladar using robust statistics,” Proc. SPIE 5988, 59880J (2005).
  7. C. Grönwall, F. Gustafsson, and M. Millnert, “Ground target recognition using rectangle estimation,” IEEE Trans. Image Process. 15, 3400–3408 (2006).
    [CrossRef]
  8. J. W. H. Tangelder and R. C. Veltkamp, “A survey of content based 3D shape retrieval methods,” in Proceedings of Shape Modeling Application (IEEE, 2004), pp. 145–156.
  9. M. A. A. Mhamdi, D. Ziou, A. Lachkar, and S. E. A. Ouatik, “An efficient method for 3D objects retrieval based on fixed number of 2D views approach,” in Proceedings of 5th International Symposium on Communications and Mobile Network (ISVC) (IEEE, 2010), pp. 1–4.
  10. J. L. Shih, C. H. Lee, and C. H. Chuang, “A 3D model retrieval approach based on the combination of PCA plane projections,” J. Info. Technol. Appl. 5, 46–56 (2011).
  11. K. Sfikas, T. Theoharis, and I. Pratikakis, “ROSy+: 3D object pose normalization based on PCA and reflective object symmetry with application in 3D object retrieval,” Int. J. Comput. Vis. 91, 262–279 (2011).
    [CrossRef]
  12. R. M. Haralick, H. Joo, C. N. Lee, X. Zhuang, V. G. Vaiday, and M. Baekim, “Pose estimation from corresponding point data,” IEEE Trans. Syst. Man Cybern. 19, 1426–1446 (1989).
    [CrossRef]
  13. F. Aghili, M. Kuryllo, G. Okouneva, and D. McTavish, “Robust pose estimation of moving objects using laser camera data for autonomous rendezvous & docking,” in Proceedings of International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (IAPRS) (2009), pp. 253–258.
  14. A. Adán, P. Merchán, and S. Salamanca, “3D scene retrieval and recognition with depth gradient images,” Pattern Recogn. Lett. 32, 1337–1353 (2011).
    [CrossRef]
  15. B. Taati and M. Greenspan, “Local shape descriptor selection for object recognition in range data,” Comput. Vis. Image Understanding 115, 681–694 (2011).
    [CrossRef]
  16. R. B. Rusu, G. Bradski, R. Thibaux, J. Hsu, and W. Garage, “Fast 3D recognition and pose using the viewpoint feature histogram,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2010), pp. 2155–2162.
  17. A. Aldoma and M. Vincze, “CAD-model recognition and 6DOF pose estimation using 3D cues,” in Proceedings of IEEE International Conference on Computer Vision Workshops (IEEE, 2011), pp. 585–592.
  18. W. E. L. Grimson, Object Recognition by Computer (MIT, 1990).
  19. R. P. Paul, Robot Manipulators Mathematics Programming and Control (MIT, 1981).
  20. H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, and W. Stuetzle, “Surface reconstruction from unorganized points,” in Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques (ACM, 1992), pp. 71–78.
  21. P. J. Besl and R. C. Jain, “Segmentation through variable-order surface fitting,” IEEE Trans. Pattern Anal. Mach. Intell. 10, 167–192 (1988).
    [CrossRef]
  22. C. Dorai and A. K. Jain, “COSMOS—a representation scheme for 3D free-form objects,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 1115–1130 (1997).
    [CrossRef]
  23. H. T. Ho and D. Gibbins, “Curvature-based approach for multi-scale feature extraction from 3D meshes and unstructured point clouds,” IET Comput. Vis. 3, 201–212 (2009).
  24. T. Wu, N. G. Lv, X. P. Lou, and P. Sun, “Automatic 3D point clouds registration method,” Proc. SPIE 7855, 785526 (2010).
    [CrossRef]

2012

2011

J. L. Shih, C. H. Lee, and C. H. Chuang, “A 3D model retrieval approach based on the combination of PCA plane projections,” J. Info. Technol. Appl. 5, 46–56 (2011).

K. Sfikas, T. Theoharis, and I. Pratikakis, “ROSy+: 3D object pose normalization based on PCA and reflective object symmetry with application in 3D object retrieval,” Int. J. Comput. Vis. 91, 262–279 (2011).
[CrossRef]

A. Adán, P. Merchán, and S. Salamanca, “3D scene retrieval and recognition with depth gradient images,” Pattern Recogn. Lett. 32, 1337–1353 (2011).
[CrossRef]

B. Taati and M. Greenspan, “Local shape descriptor selection for object recognition in range data,” Comput. Vis. Image Understanding 115, 681–694 (2011).
[CrossRef]

2010

T. Wu, N. G. Lv, X. P. Lou, and P. Sun, “Automatic 3D point clouds registration method,” Proc. SPIE 7855, 785526 (2010).
[CrossRef]

R. D. Nieves and W. D. Reynolds, “Three-dimensional transformation for automatic target recognition using LIDAR data,” Proc. SPIE 7684, 76840Y (2010).
[CrossRef]

Q. Wang, L. Wang, and J. F. Sun, “Rotation-invariant target recognition in LADAR range imagery using model matching approach,” Opt. Express 18, 15349–15360 (2010).
[CrossRef]

2009

H. T. Ho and D. Gibbins, “Curvature-based approach for multi-scale feature extraction from 3D meshes and unstructured point clouds,” IET Comput. Vis. 3, 201–212 (2009).

2006

C. Grönwall, F. Gustafsson, and M. Millnert, “Ground target recognition using rectangle estimation,” IEEE Trans. Image Process. 15, 3400–3408 (2006).
[CrossRef]

2005

J. Neulist and W. Armbruster, “Segmentation, classification, and pose estimation of military vehicles in low resolution laser radar images,” Proc. SPIE 5791, 218–225 (2005).
[CrossRef]

L. F. Ramon, F. Sira, D. C. Jose, and B. Xavier, “Target detection in Ladar using robust statistics,” Proc. SPIE 5988, 59880J (2005).

2004

C. S. L. Chun and F. A. Sadjadi, “Target recognition study using polarimetric laser radar,” Proc. SPIE 5426, 274–284 (2004).
[CrossRef]

1997

C. Dorai and A. K. Jain, “COSMOS—a representation scheme for 3D free-form objects,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 1115–1130 (1997).
[CrossRef]

1989

R. M. Haralick, H. Joo, C. N. Lee, X. Zhuang, V. G. Vaiday, and M. Baekim, “Pose estimation from corresponding point data,” IEEE Trans. Syst. Man Cybern. 19, 1426–1446 (1989).
[CrossRef]

1988

P. J. Besl and R. C. Jain, “Segmentation through variable-order surface fitting,” IEEE Trans. Pattern Anal. Mach. Intell. 10, 167–192 (1988).
[CrossRef]

Adán, A.

A. Adán, P. Merchán, and S. Salamanca, “3D scene retrieval and recognition with depth gradient images,” Pattern Recogn. Lett. 32, 1337–1353 (2011).
[CrossRef]

Aghili, F.

F. Aghili, M. Kuryllo, G. Okouneva, and D. McTavish, “Robust pose estimation of moving objects using laser camera data for autonomous rendezvous & docking,” in Proceedings of International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (IAPRS) (2009), pp. 253–258.

Aldoma, A.

A. Aldoma and M. Vincze, “CAD-model recognition and 6DOF pose estimation using 3D cues,” in Proceedings of IEEE International Conference on Computer Vision Workshops (IEEE, 2011), pp. 585–592.

Armbruster, W.

J. Neulist and W. Armbruster, “Segmentation, classification, and pose estimation of military vehicles in low resolution laser radar images,” Proc. SPIE 5791, 218–225 (2005).
[CrossRef]

Baekim, M.

R. M. Haralick, H. Joo, C. N. Lee, X. Zhuang, V. G. Vaiday, and M. Baekim, “Pose estimation from corresponding point data,” IEEE Trans. Syst. Man Cybern. 19, 1426–1446 (1989).
[CrossRef]

Besl, P. J.

P. J. Besl and R. C. Jain, “Segmentation through variable-order surface fitting,” IEEE Trans. Pattern Anal. Mach. Intell. 10, 167–192 (1988).
[CrossRef]

Bradski, G.

R. B. Rusu, G. Bradski, R. Thibaux, J. Hsu, and W. Garage, “Fast 3D recognition and pose using the viewpoint feature histogram,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2010), pp. 2155–2162.

Chuang, C. H.

J. L. Shih, C. H. Lee, and C. H. Chuang, “A 3D model retrieval approach based on the combination of PCA plane projections,” J. Info. Technol. Appl. 5, 46–56 (2011).

Chun, C. S. L.

C. S. L. Chun and F. A. Sadjadi, “Target recognition study using polarimetric laser radar,” Proc. SPIE 5426, 274–284 (2004).
[CrossRef]

DeRose, T.

H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, and W. Stuetzle, “Surface reconstruction from unorganized points,” in Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques (ACM, 1992), pp. 71–78.

Dorai, C.

C. Dorai and A. K. Jain, “COSMOS—a representation scheme for 3D free-form objects,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 1115–1130 (1997).
[CrossRef]

Duchamp, T.

H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, and W. Stuetzle, “Surface reconstruction from unorganized points,” in Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques (ACM, 1992), pp. 71–78.

Garage, W.

R. B. Rusu, G. Bradski, R. Thibaux, J. Hsu, and W. Garage, “Fast 3D recognition and pose using the viewpoint feature histogram,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2010), pp. 2155–2162.

Gibbins, D.

H. T. Ho and D. Gibbins, “Curvature-based approach for multi-scale feature extraction from 3D meshes and unstructured point clouds,” IET Comput. Vis. 3, 201–212 (2009).

Greenspan, M.

B. Taati and M. Greenspan, “Local shape descriptor selection for object recognition in range data,” Comput. Vis. Image Understanding 115, 681–694 (2011).
[CrossRef]

Grimson, W. E. L.

W. E. L. Grimson, Object Recognition by Computer (MIT, 1990).

Grönwall, C.

C. Grönwall, F. Gustafsson, and M. Millnert, “Ground target recognition using rectangle estimation,” IEEE Trans. Image Process. 15, 3400–3408 (2006).
[CrossRef]

Gustafsson, F.

C. Grönwall, F. Gustafsson, and M. Millnert, “Ground target recognition using rectangle estimation,” IEEE Trans. Image Process. 15, 3400–3408 (2006).
[CrossRef]

Haralick, R. M.

R. M. Haralick, H. Joo, C. N. Lee, X. Zhuang, V. G. Vaiday, and M. Baekim, “Pose estimation from corresponding point data,” IEEE Trans. Syst. Man Cybern. 19, 1426–1446 (1989).
[CrossRef]

Ho, H. T.

H. T. Ho and D. Gibbins, “Curvature-based approach for multi-scale feature extraction from 3D meshes and unstructured point clouds,” IET Comput. Vis. 3, 201–212 (2009).

Hoppe, H.

H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, and W. Stuetzle, “Surface reconstruction from unorganized points,” in Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques (ACM, 1992), pp. 71–78.

Hsu, J.

R. B. Rusu, G. Bradski, R. Thibaux, J. Hsu, and W. Garage, “Fast 3D recognition and pose using the viewpoint feature histogram,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2010), pp. 2155–2162.

Jain, A. K.

C. Dorai and A. K. Jain, “COSMOS—a representation scheme for 3D free-form objects,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 1115–1130 (1997).
[CrossRef]

Jain, R. C.

P. J. Besl and R. C. Jain, “Segmentation through variable-order surface fitting,” IEEE Trans. Pattern Anal. Mach. Intell. 10, 167–192 (1988).
[CrossRef]

Joo, H.

R. M. Haralick, H. Joo, C. N. Lee, X. Zhuang, V. G. Vaiday, and M. Baekim, “Pose estimation from corresponding point data,” IEEE Trans. Syst. Man Cybern. 19, 1426–1446 (1989).
[CrossRef]

Jose, D. C.

L. F. Ramon, F. Sira, D. C. Jose, and B. Xavier, “Target detection in Ladar using robust statistics,” Proc. SPIE 5988, 59880J (2005).

Kuryllo, M.

F. Aghili, M. Kuryllo, G. Okouneva, and D. McTavish, “Robust pose estimation of moving objects using laser camera data for autonomous rendezvous & docking,” in Proceedings of International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (IAPRS) (2009), pp. 253–258.

Lachkar, A.

M. A. A. Mhamdi, D. Ziou, A. Lachkar, and S. E. A. Ouatik, “An efficient method for 3D objects retrieval based on fixed number of 2D views approach,” in Proceedings of 5th International Symposium on Communications and Mobile Network (ISVC) (IEEE, 2010), pp. 1–4.

Lee, C. H.

J. L. Shih, C. H. Lee, and C. H. Chuang, “A 3D model retrieval approach based on the combination of PCA plane projections,” J. Info. Technol. Appl. 5, 46–56 (2011).

Lee, C. N.

R. M. Haralick, H. Joo, C. N. Lee, X. Zhuang, V. G. Vaiday, and M. Baekim, “Pose estimation from corresponding point data,” IEEE Trans. Syst. Man Cybern. 19, 1426–1446 (1989).
[CrossRef]

Li, Q.

Liu, Z. J.

Lou, X. P.

T. Wu, N. G. Lv, X. P. Lou, and P. Sun, “Automatic 3D point clouds registration method,” Proc. SPIE 7855, 785526 (2010).
[CrossRef]

Lv, N. G.

T. Wu, N. G. Lv, X. P. Lou, and P. Sun, “Automatic 3D point clouds registration method,” Proc. SPIE 7855, 785526 (2010).
[CrossRef]

McDonald, J.

H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, and W. Stuetzle, “Surface reconstruction from unorganized points,” in Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques (ACM, 1992), pp. 71–78.

McTavish, D.

F. Aghili, M. Kuryllo, G. Okouneva, and D. McTavish, “Robust pose estimation of moving objects using laser camera data for autonomous rendezvous & docking,” in Proceedings of International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (IAPRS) (2009), pp. 253–258.

Merchán, P.

A. Adán, P. Merchán, and S. Salamanca, “3D scene retrieval and recognition with depth gradient images,” Pattern Recogn. Lett. 32, 1337–1353 (2011).
[CrossRef]

Mhamdi, M. A. A.

M. A. A. Mhamdi, D. Ziou, A. Lachkar, and S. E. A. Ouatik, “An efficient method for 3D objects retrieval based on fixed number of 2D views approach,” in Proceedings of 5th International Symposium on Communications and Mobile Network (ISVC) (IEEE, 2010), pp. 1–4.

Millnert, M.

C. Grönwall, F. Gustafsson, and M. Millnert, “Ground target recognition using rectangle estimation,” IEEE Trans. Image Process. 15, 3400–3408 (2006).
[CrossRef]

Neulist, J.

J. Neulist and W. Armbruster, “Segmentation, classification, and pose estimation of military vehicles in low resolution laser radar images,” Proc. SPIE 5791, 218–225 (2005).
[CrossRef]

Nieves, R. D.

R. D. Nieves and W. D. Reynolds, “Three-dimensional transformation for automatic target recognition using LIDAR data,” Proc. SPIE 7684, 76840Y (2010).
[CrossRef]

Okouneva, G.

F. Aghili, M. Kuryllo, G. Okouneva, and D. McTavish, “Robust pose estimation of moving objects using laser camera data for autonomous rendezvous & docking,” in Proceedings of International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (IAPRS) (2009), pp. 253–258.

Ouatik, S. E. A.

M. A. A. Mhamdi, D. Ziou, A. Lachkar, and S. E. A. Ouatik, “An efficient method for 3D objects retrieval based on fixed number of 2D views approach,” in Proceedings of 5th International Symposium on Communications and Mobile Network (ISVC) (IEEE, 2010), pp. 1–4.

Paul, R. P.

R. P. Paul, Robot Manipulators Mathematics Programming and Control (MIT, 1981).

Pratikakis, I.

K. Sfikas, T. Theoharis, and I. Pratikakis, “ROSy+: 3D object pose normalization based on PCA and reflective object symmetry with application in 3D object retrieval,” Int. J. Comput. Vis. 91, 262–279 (2011).
[CrossRef]

Ramon, L. F.

L. F. Ramon, F. Sira, D. C. Jose, and B. Xavier, “Target detection in Ladar using robust statistics,” Proc. SPIE 5988, 59880J (2005).

Reynolds, W. D.

R. D. Nieves and W. D. Reynolds, “Three-dimensional transformation for automatic target recognition using LIDAR data,” Proc. SPIE 7684, 76840Y (2010).
[CrossRef]

Rusu, R. B.

R. B. Rusu, G. Bradski, R. Thibaux, J. Hsu, and W. Garage, “Fast 3D recognition and pose using the viewpoint feature histogram,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2010), pp. 2155–2162.

Sadjadi, F. A.

C. S. L. Chun and F. A. Sadjadi, “Target recognition study using polarimetric laser radar,” Proc. SPIE 5426, 274–284 (2004).
[CrossRef]

Salamanca, S.

A. Adán, P. Merchán, and S. Salamanca, “3D scene retrieval and recognition with depth gradient images,” Pattern Recogn. Lett. 32, 1337–1353 (2011).
[CrossRef]

Sfikas, K.

K. Sfikas, T. Theoharis, and I. Pratikakis, “ROSy+: 3D object pose normalization based on PCA and reflective object symmetry with application in 3D object retrieval,” Int. J. Comput. Vis. 91, 262–279 (2011).
[CrossRef]

Shih, J. L.

J. L. Shih, C. H. Lee, and C. H. Chuang, “A 3D model retrieval approach based on the combination of PCA plane projections,” J. Info. Technol. Appl. 5, 46–56 (2011).

Sira, F.

L. F. Ramon, F. Sira, D. C. Jose, and B. Xavier, “Target detection in Ladar using robust statistics,” Proc. SPIE 5988, 59880J (2005).

Stuetzle, W.

H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, and W. Stuetzle, “Surface reconstruction from unorganized points,” in Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques (ACM, 1992), pp. 71–78.

Sun, J. F.

Sun, P.

T. Wu, N. G. Lv, X. P. Lou, and P. Sun, “Automatic 3D point clouds registration method,” Proc. SPIE 7855, 785526 (2010).
[CrossRef]

Taati, B.

B. Taati and M. Greenspan, “Local shape descriptor selection for object recognition in range data,” Comput. Vis. Image Understanding 115, 681–694 (2011).
[CrossRef]

Tangelder, J. W. H.

J. W. H. Tangelder and R. C. Veltkamp, “A survey of content based 3D shape retrieval methods,” in Proceedings of Shape Modeling Application (IEEE, 2004), pp. 145–156.

Theoharis, T.

K. Sfikas, T. Theoharis, and I. Pratikakis, “ROSy+: 3D object pose normalization based on PCA and reflective object symmetry with application in 3D object retrieval,” Int. J. Comput. Vis. 91, 262–279 (2011).
[CrossRef]

Thibaux, R.

R. B. Rusu, G. Bradski, R. Thibaux, J. Hsu, and W. Garage, “Fast 3D recognition and pose using the viewpoint feature histogram,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2010), pp. 2155–2162.

Vaiday, V. G.

R. M. Haralick, H. Joo, C. N. Lee, X. Zhuang, V. G. Vaiday, and M. Baekim, “Pose estimation from corresponding point data,” IEEE Trans. Syst. Man Cybern. 19, 1426–1446 (1989).
[CrossRef]

Veltkamp, R. C.

J. W. H. Tangelder and R. C. Veltkamp, “A survey of content based 3D shape retrieval methods,” in Proceedings of Shape Modeling Application (IEEE, 2004), pp. 145–156.

Vincze, M.

A. Aldoma and M. Vincze, “CAD-model recognition and 6DOF pose estimation using 3D cues,” in Proceedings of IEEE International Conference on Computer Vision Workshops (IEEE, 2011), pp. 585–592.

Wang, L.

Wang, Q.

Wu, T.

T. Wu, N. G. Lv, X. P. Lou, and P. Sun, “Automatic 3D point clouds registration method,” Proc. SPIE 7855, 785526 (2010).
[CrossRef]

Xavier, B.

L. F. Ramon, F. Sira, D. C. Jose, and B. Xavier, “Target detection in Ladar using robust statistics,” Proc. SPIE 5988, 59880J (2005).

Xia, Z. W.

Zhuang, X.

R. M. Haralick, H. Joo, C. N. Lee, X. Zhuang, V. G. Vaiday, and M. Baekim, “Pose estimation from corresponding point data,” IEEE Trans. Syst. Man Cybern. 19, 1426–1446 (1989).
[CrossRef]

Ziou, D.

M. A. A. Mhamdi, D. Ziou, A. Lachkar, and S. E. A. Ouatik, “An efficient method for 3D objects retrieval based on fixed number of 2D views approach,” in Proceedings of 5th International Symposium on Communications and Mobile Network (ISVC) (IEEE, 2010), pp. 1–4.

Appl. Opt.

Comput. Vis. Image Understanding

B. Taati and M. Greenspan, “Local shape descriptor selection for object recognition in range data,” Comput. Vis. Image Understanding 115, 681–694 (2011).
[CrossRef]

IEEE Trans. Image Process.

C. Grönwall, F. Gustafsson, and M. Millnert, “Ground target recognition using rectangle estimation,” IEEE Trans. Image Process. 15, 3400–3408 (2006).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell.

P. J. Besl and R. C. Jain, “Segmentation through variable-order surface fitting,” IEEE Trans. Pattern Anal. Mach. Intell. 10, 167–192 (1988).
[CrossRef]

C. Dorai and A. K. Jain, “COSMOS—a representation scheme for 3D free-form objects,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 1115–1130 (1997).
[CrossRef]

IEEE Trans. Syst. Man Cybern.

R. M. Haralick, H. Joo, C. N. Lee, X. Zhuang, V. G. Vaiday, and M. Baekim, “Pose estimation from corresponding point data,” IEEE Trans. Syst. Man Cybern. 19, 1426–1446 (1989).
[CrossRef]

IET Comput. Vis.

H. T. Ho and D. Gibbins, “Curvature-based approach for multi-scale feature extraction from 3D meshes and unstructured point clouds,” IET Comput. Vis. 3, 201–212 (2009).

Int. J. Comput. Vis.

K. Sfikas, T. Theoharis, and I. Pratikakis, “ROSy+: 3D object pose normalization based on PCA and reflective object symmetry with application in 3D object retrieval,” Int. J. Comput. Vis. 91, 262–279 (2011).
[CrossRef]

J. Info. Technol. Appl.

J. L. Shih, C. H. Lee, and C. H. Chuang, “A 3D model retrieval approach based on the combination of PCA plane projections,” J. Info. Technol. Appl. 5, 46–56 (2011).

Opt. Express

Pattern Recogn. Lett.

A. Adán, P. Merchán, and S. Salamanca, “3D scene retrieval and recognition with depth gradient images,” Pattern Recogn. Lett. 32, 1337–1353 (2011).
[CrossRef]

Proc. SPIE

J. Neulist and W. Armbruster, “Segmentation, classification, and pose estimation of military vehicles in low resolution laser radar images,” Proc. SPIE 5791, 218–225 (2005).
[CrossRef]

L. F. Ramon, F. Sira, D. C. Jose, and B. Xavier, “Target detection in Ladar using robust statistics,” Proc. SPIE 5988, 59880J (2005).

T. Wu, N. G. Lv, X. P. Lou, and P. Sun, “Automatic 3D point clouds registration method,” Proc. SPIE 7855, 785526 (2010).
[CrossRef]

C. S. L. Chun and F. A. Sadjadi, “Target recognition study using polarimetric laser radar,” Proc. SPIE 5426, 274–284 (2004).
[CrossRef]

R. D. Nieves and W. D. Reynolds, “Three-dimensional transformation for automatic target recognition using LIDAR data,” Proc. SPIE 7684, 76840Y (2010).
[CrossRef]

Other

R. B. Rusu, G. Bradski, R. Thibaux, J. Hsu, and W. Garage, “Fast 3D recognition and pose using the viewpoint feature histogram,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2010), pp. 2155–2162.

A. Aldoma and M. Vincze, “CAD-model recognition and 6DOF pose estimation using 3D cues,” in Proceedings of IEEE International Conference on Computer Vision Workshops (IEEE, 2011), pp. 585–592.

W. E. L. Grimson, Object Recognition by Computer (MIT, 1990).

R. P. Paul, Robot Manipulators Mathematics Programming and Control (MIT, 1981).

H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, and W. Stuetzle, “Surface reconstruction from unorganized points,” in Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques (ACM, 1992), pp. 71–78.

J. W. H. Tangelder and R. C. Veltkamp, “A survey of content based 3D shape retrieval methods,” in Proceedings of Shape Modeling Application (IEEE, 2004), pp. 145–156.

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Figures (13)

Fig. 1.
Fig. 1.

Expression of the target pose.

Fig. 2.
Fig. 2.

Relationship between normals on target surface and MCS.

Fig. 3.
Fig. 3.

CAD models of (a) heavy mobile tactical truck M977, (b) missile launch vehicle M730A1, (c) Hummer military vehicle, (d) tank M1A1, (e) tank LECRERC, and (f) tank FLACKP.

Fig. 4.
Fig. 4.

3D point cloud of (a) heavy mobile tactical truck M977, (b) missile launch vehicle M730A1, (c) Hummer military vehicle, (d) tank M1, (e) tank LECRERC, and (f) tank FLACKP.

Fig. 5.
Fig. 5.

Workflow of the pose estimation algorithm.

Fig. 6.
Fig. 6.

Neighborhood relationship among data points, the measured target, corresponding range image, and point cloud.

Fig. 7.
Fig. 7.

Filtration of nonplanar points from missile launcher point cloud by different curvedness. (a) 3D model of M730A1, (b) 3D point cloud of M730A1, (c) 3D point cloud obtained after the filtration of nonplanar points according to the curvedness (C<0.1), (d) 3D point cloud obtained after the filtration of nonplanar points according to the curvedness (C<0.025).

Fig. 8.
Fig. 8.

Histogram distribution of the angles between each normal and centroid G¯.

Fig. 9.
Fig. 9.

Calculation of the vectors in the positive direction of the axes in MCS. (a) The vectors n and o are obtained through filtering the point normals and denoted as n=(nx,ny,nz) and o=(ox,oy,oz); (b) determination of the vector a=(ax,ay,az).

Fig. 10.
Fig. 10.

Schematic diagram of data collection by airborne ladar.

Fig. 11.
Fig. 11.

Pose estimation performance of tank LECRERC with partial occlusion: (a) The effective pose estimation rate obtained with different occlusion levels; (b) ME of the effective ψ, θ, and ϕ; (c) SE of the effective ψ, θ, and ϕ.

Fig. 12.
Fig. 12.

Pose estimation performance of tank LECRERC under noisy conditions. (a) ME of ψ, θ, and ϕ; (b) SE of ψ, θ, and ϕ.

Fig. 13.
Fig. 13.

Point cloud of tank LECRERC affected by noise. (a) The noise-free point cloud and (b) corresponding point cloud with Gaussian noise σ=25cm.

Tables (2)

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Table 1. Relationship between Curvatures and Surface Types

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Table 2. Pose Estimation Errors of the Six Military Vehicle Models

Equations (17)

Equations on this page are rendered with MathJax. Learn more.

Ax+By+Cz+D=0,
S(u,v)=Au2+Buv+Cv2+Du+Ev+F.
n=Su×Sv|Su×Sv|.
E=SuSu,F=SuSv,G=SvSv,
L=nSuu,M=nSuv,N=nSvv.
K(p)=LNM2EGF2,
H(p)=EN2FM+GL2(EGF2),
kmax(p)=H(p)+H2(p)K(p),
kmin(p)=H(p)H2(p)K(p).
Cp=kmax2(p)+kmin2(p)2.
RPY(ϕ,θ,ψ)=Rot(Zm,ϕ)Rot(Ym,θ)Rot(Xm,ψ),
Rot(Zm,ϕ)=[cosϕsinϕ0sinϕcosϕ0001],Rot(Ym,θ)=[cosθ0sinθ010sinθ0cosθ],Rot(Xm,ψ)=[1000cosψsinψ0sinψcosψ],
T=[nxoxaxnyoyaynzozaz]=RPY(ϕ,θ,ψ).
Rot(Zm,ϕ)1RPY(ϕ,θ,ψ)=Rot(Ym,θ)Rot(Xm,ψ).
ϕ=atan2(ny,nx),
θ=atan2(nz,cosϕnx+sinϕny),
ψ=atan2(sinϕaxcosϕay,sinϕox+cosϕoy).

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