Real-Time Scale Invariant 3D Range Point Cloud Registration

  • Anuj Sehgal
  • Daniel Cernea
  • Milena Makaveeva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6111)

Abstract

Stereo cameras, laser rangers and other time-of-flight ranging devices are utilized with increasing frequency as they can provide information in the 3D plane. The ability to perform real-time registration of the 3D point clouds obtained from these sensors is important in many applications. However, the tasks of locating accurate and dependable correspondences between point clouds and registration can be quite slow. Furthermore, any algorithm must be robust against artifacts in 3D range data as sensor motion, reflection and refraction are commonplace. The SIFT feature detector is a robust algorithm used to locate features, but cannot be extended directly to the 3D range point clouds since it requires dense pixel information, whereas the range voxels are sparsely distributed. This paper proposes an approach which enables SIFT application to locate scale and rotation invariant features in 3D point clouds. The algorithm then utilizes the known point correspondence registration algorithm in order to achieve real-time registration of 3D point clouds.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bendels, G., Degener, P., Wahl, R., Koertgen, M., Klein, R.: Image-based registration of 3d-range data using feature surface elements. In: Proceedings of The 5th International Symposium of Virtual Reality, Archaeology and Cultural Heritage, VAST 2004 (2004)Google Scholar
  2. 2.
    Callieri, M., Cignoni, P., Ganovelli, F., Montani, C., Pingi, P., Scopigno, R.: Vclab’s tools for 3d range data processing. In: Proceedings of the 1st EURO-GRAPHICS Workshop on Graphics and Cultural Heritage, Brighton, UK (November 2003)Google Scholar
  3. 3.
    Cernea, D.: Graphical methods for online surface fitting on 3d range sensor point clouds. Master’s thesis, Jacobs University Bremen, Germany (August 2009)Google Scholar
  4. 4.
    Schall, O., Belyaev, A., Seidel, H.P.: Robust filtering of noisy scattered point data. In: Proceedings of Point-Based Graphics on Eurographics/IEEE VGTC Symposium, June 2005, pp. 71–144 (2005)Google Scholar
  5. 5.
    Lowe, D.: Distinctive image features from scale- invariant keypoints. International Journal of Computer Vision 60(2) (November 2004)Google Scholar
  6. 6.
    Lowe, D.: Object recognition from local scale-invariant features. In: Proceedings of International Conference on Computer Vision, Corfu, Greece (September 1999)Google Scholar
  7. 7.
    Pisarevsky, V., et al.: Opencv, the open computer vision library (2008), http://mloss.org/software/view/68/
  8. 8.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. ACM Commun. 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Int. J. Comput. Vision 13(2), 119–152 (1994)CrossRefGoogle Scholar
  10. 10.
    Gough, B. (ed.): GNU Scientific Library Reference Manual, 2nd edn. Network Theory Ltd. (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Anuj Sehgal
    • 1
  • Daniel Cernea
    • 2
  • Milena Makaveeva
    • 3
  1. 1.Indian Underwater Robotics SocietyNoidaIndia
  2. 2.Department of Computer ScienceUniversity of KaiserslauternKaiserslauternGermany
  3. 3.Computer ScienceJacobs University BremenBremenGermany

Personalised recommendations