Extended 3D Line Segments from RGB-D Data for Pose Estimation

  • Anders Glent Buch
  • Jeppe Barsøe Jessen
  • Dirk Kraft
  • Thiusius Rajeeth Savarimuthu
  • Norbert Krüger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)

Abstract

We propose a method for the extraction of complete and rich symbolic line segments in 3D based on RGB-D data. Edges are detected by combining cues from the RGB image and the aligned depth map. 3D line segments are then reconstructed by back-projecting 2D line segments and intersecting this with local surface patches computed from the 3D point cloud. Different edge types are classified using the new enriched representation and the potential of this representation for the task of pose estimation is demonstrated.

Keywords

Edge detection pose estimation 

References

  1. 1.
    Basu, M.: Gaussian-based edge-detection methods-a survey. IEEE Transactions on Systems, Man and Cybernetics 32(3), 252–260 (2002)CrossRefGoogle Scholar
  2. 2.
    Oskoei, M.A., Hu, H.: A survey on edge detection methods. Technical Report CES-506, University of Essex (2010)Google Scholar
  3. 3.
    Lejeune, A., Piérard, S., Van Droogenbroeck, M., Verly, J.: A new jump edge detection method for 3D cameras. In: IC3D, Liège, Belgium (2011)Google Scholar
  4. 4.
    Jiang, X., Bunke, H.: Edge detection in range images based on scan line approximation. CVIU 73(2), 183–199 (1999)Google Scholar
  5. 5.
    Parvin, B., Medioni, G.: Adaptive multiscale feature extraction from range data. Computer Vision, Graphics and Image Processing, 346–356 (1989)Google Scholar
  6. 6.
    Fang, F., Boyaci, H., Kersten, D.: Border ownership selectivity in human early visual cortex and its modulation by attention. The Journal of Neuroscience 29(2), 460–465 (2009)CrossRefGoogle Scholar
  7. 7.
    Pugeault, N., Wörgötter, F., Krüger, N.: Visual primitives: Local, condensed, and semantically rich visual descriptors and their applications in robotics. International Journal of Humanoid Robotics (Special Issue on Cognitive Humanoid Vision) 7(3), 379–405 (2010)CrossRefGoogle Scholar
  8. 8.
    Canny, J.: A computational approach to edge detection. TPAMI 8(6), 679–698 (1986)CrossRefGoogle Scholar
  9. 9.
    Al-hujazi, E., Sood, A.: Range image segmentation with applications to robot bin-picking using vacuum gripper. In: IEEE International Conference on Systems, Man, and Cybernetics (1990)Google Scholar
  10. 10.
    Steder, B., Rusu, R.B., Konolige, K., Burgard, W.: Point feature extraction on 3d range scans taking into account object boundaries. In: ICRA, pp. 2601–2608 (2011)Google Scholar
  11. 11.
    Economopoulos, A., Martakos, D.: Depth-assisted edge detection via layered scale-based smoothing. In: ISPA, pp. 149–154 (2001)Google Scholar
  12. 12.
    Kalkan, S., Wörgötter, F., Krüger, N.: Statistical analysis of local 3d structure in 2d images. In: CVPR, pp. 1114–1121 (2006)Google Scholar
  13. 13.
    Felsberg, M., Kalkan, S., Krüger, N.: Continuous dimensionality characterization of image structures. Image and Vision Computing 27, 628–636 (2009)CrossRefGoogle Scholar
  14. 14.
    Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view rgb-d object dataset. In: ICRA, pp. 1817–1824 (2011)Google Scholar
  16. 16.
    Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: Efficient and robust 3D object recognition. In: CVPR, pp. 998–1005 (2010)Google Scholar
  17. 17.
    Buch, A.G., Kraft, D., Kämäräinen, J.K., Petersen, H.G., Krüger, N.: Pose estimation using local structure-specific shape and appearance context. In: ICRA (accepted, 2013)Google Scholar
  18. 18.
    Detry, R., Piater, J.: Continuous surface-point distributions for 3D object pose estimation and recognition. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part III. LNCS, vol. 6494, pp. 572–585. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. TPAMI 14(2), 239–256 (1992)CrossRefGoogle Scholar
  20. 20.
    Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. IJCV 13(2), 119–152 (1994)CrossRefGoogle Scholar
  21. 21.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. TPAMI 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  22. 22.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. IJCV 65(1-2), 43–72 (2005)CrossRefGoogle Scholar
  23. 23.
    Lowe, D.: Object recognition from local scale-invariant features. In: ICCV, vol. 2, pp. 1150–1157 (1999)Google Scholar
  24. 24.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  25. 25.
    Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. TPAMI 21(5), 433–449 (1999)CrossRefGoogle Scholar
  26. 26.
    Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: ICRA, pp. 3212–3217 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Anders Glent Buch
    • 1
  • Jeppe Barsøe Jessen
    • 1
  • Dirk Kraft
    • 1
  • Thiusius Rajeeth Savarimuthu
    • 1
  • Norbert Krüger
    • 1
  1. 1.The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkOdenseDenmark

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