137
Views
1
CrossRef citations to date
0
Altmetric
Full Paper

Multi-contour initial pose estimation for 3D registration

, &
Pages 1173-1185 | Received 31 Oct 2015, Accepted 29 May 2016, Published online: 07 Jul 2016

References

  • Pratt GManzo J. The darpa robotics challenge [competitions]. IEEE Rob. Autom. Mag. 2013;20:10–12.
  • Mason MT. Manipulator grasping and pushing operations. In: Mason MTSalisbury Jeditors. Robot hands and the mechanics of manipulation. Cambridge (MA): Massachusetts Institute of Technology; 1985.p. 171–294.
  • Bicchi AKumar V. Robotic grasping and contact: a review. In: IEEE International Conference on Robotics and Automation. San Francisco, CA; Vol. 12000. p. 348–353.
  • Besl PMcKay ND. A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 1992;14:239–256.
  • Silva LBellon ORPBoyer KL. Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 2005;27:762–776.
  • Cordón ODamas SSantamaría J. A fast and accurate approach for 3d image registration using the scatter search evolutionary algorithm. Pattern Recognit. Lett. 2006;27:1191–1200.
  • Otake YArmand MArmiger RSet al. Intraoperative image-based multiview 2D/3D registration for image-guided orthopaedic surgery: incorporation of fiducial-based c-arm tracking and GPU-acceleration. IEEE Trans. Med. Imaging. 2012;31:948–962.
  • Gruen AAkca D. Least squares 3d surface and curve matching. ISPRS J. Photogrammetry Remote Sens. 2005;59:151–174.
  • Fitzgibbon AW. Robust registration of 2d and 3d point sets. In: British Machine Vision Conference. Manchester; 2001.
  • Gelfand NMitra NJGuibas LJ. Robust global registration. In: Eurographics symposium on geometry processing. Vienna; 2005.
  • Makadia APatterson AIDaniilidis K. Fully automatic registration of 3d point clouds. In: International Conference on Computer Vision and Pattern Recognition. New York, NY; 2006. p. 1297–1304.
  • Johnson AHebert M. Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Trans. Pattern Anal. Mach. Intell. 1999;21:433–449.
  • Sharp GLee SWehe D. Icp registration using invariant features. IEEE Trans. Pattern Anal. Mach. Intell. 2002;24:90–102.
  • Rusu RBlodow NBeetz M. Fast point feature histograms (FPFH) for 3d registration. In: IEEE International Conference on Robotics and Automation. Kobe; 2009. p. 3212–3217.
  • Rusinkiewicz SLevoy M. Efficient variants of the icp algorithm. In: International Conference on 3d Digital Imaging and Modeling. Quebec City; 2001. p. 145–152.
  • Nuchter ALingemann KHertzberg J. Cached K-d tree search for ICP algorithms. In: International Conference on 3-d Digital Imaging and Modeling. Montreal; 2007. p. 419–426.
  • Rusu RBradski GThibaux Ret al. Fast 3d recognition and pose using the viewpoint feature histogram. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. St. Louis; 2010. p. 2155–2162.
  • Ballard DH. Readings in computer vision: issues, problems, principles, and paradigms. chap. Generalizing the hough transform to detect arbitrary shapes; 1987. p. 714–725.
  • Glover JRusu RBradski G. Monte carlo pose estimation with quaternion kernels and the bingham distribution. In: Proceedings of robotics: science and systems. Los Angeles, CA, USA; 2011.
  • Kriegman D. Computing stable poses of piecewise smooth objects. CVGIP: Image Understanding. 1992;55:109–118.
  • Mason RRimon EBurdick J. Stable poses of 3-dimensional objects. In: IEEE International Conference on Robotics and Automation. Albuquerque; 1997. p. 391–398.
  • Wan WCheung EPan Jet al. Optimizing the parameters of tilting surfaces in robotic workcells. IEEE International Conference on Automation Science and Enginnering. Gothenburg; 2015.
  • Yee STWan WVentura JA. 3-d localization and feature recovering through cad-based stable pose calculation. Pattern Recog. Lett. 2001;22:105–121.
  • Tan TNSullivan GDBaker KD. Model-based localisation and recognition of road vehicles. Int. J. Comput. Vision. 1998;27:5–25.
  • Kang SBIkeuchi K. The complex egi: a new representation for 3-d pose determination. IEEE Trans. Pattern Anal. Mach. Intell. 1993;15:707–721.
  • Park SSubbarao M. Automatic 3d reconstruction based on novel pose estimation and integration techniques. Image Vision Comput. 2004;22:623–635.
  • Singh ASha JNarayan Ket al. Bigbird: a large-scale 3d database of object instances. In: IEEE International Conference on Robotics and Automation. Hong Kong; 2014. p. 509–516.
  • Lai KBo LRen X. A large-scale hierarchical multi-view rgb-d object dataset. In: IEEE International Conference on Robotics and Automation. Shanghai; 2011. p. 1817–1824.
  • Cui YStricker D. 3d shape scanning with a kinect. In: Acm siggraph 2011 posters. SIGGRAPH ’11; Vancouver; 2011. p. 57:1.
  • Ester MKriegel HP. A density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining. Portland; 1996. p. 226–231.
  • Cheung EChao CNewman W. Initial pose estimation using cross-section contours. In: IEEE International Conference on Robotics and Biominmetics. Bali; 2014. p. 878–883.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.