Table of Contents Author Guidelines Submit a Manuscript
Journal of Sensors
Volume 2016, Article ID 2546819, 28 pages
http://dx.doi.org/10.1155/2016/2546819
Research Article

Monte Carlo Registration and Its Application with Autonomous Robots

Institute of Robotics and Mechatronics, German Aerospace Center, 82234 Oberpfaffenhofen, Germany

Received 25 March 2016; Revised 28 June 2016; Accepted 10 July 2016

Academic Editor: Pablo Gil

Copyright © 2016 Christian Rink et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. Rink, Z.-C. Marton, D. Seth, T. Bodenmüller, and M. Suppa, “Feature based particle filter registration of 3D surface models and its application in robotics,” in Proceedings of the 26th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '13), pp. 3187–3194, Tokyo, Japan, November 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. C. Rink, S. Kriegel, J. Hasse, and Z. Marton, “Onthey particle filter registration for laser data,” in Proceedings of the IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR '16), Cluj-Napoca, Romania, 2016.
  3. C. Rink and S. Kriegel, “Streaming Monte Carlo pose estimation for autonomous object modeling,” in Proceedings of the 13th Conference on Computer and Robot Vision (CRV '16), Victoria, Canada, 2016.
  4. S. Kriegel, C. Rink, T. Bodenmüller, and M. Suppa, “Efficient next-best-scan planning for autonomous 3D surface reconstruction of unknown objects,” Journal of Real-Time Image Processing, vol. 10, no. 4, pp. 611–631, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” The IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239–256, 1992. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Sturm, W. Burgard, and D. Cremers, “Evaluating egomotion and structure-from-motion approaches using the TUM RGB-D benchmark,” in Proceedings of the Workshop on Color-Depth Camera Fusion in Robotics (IROS '12), October 2012.
  7. T. Stoyanov, M. Magnusson, H. Andreasson, and A. J. Lilienthal, “Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations,” International Journal of Robotics Research, vol. 31, no. 12, pp. 1377–1393, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Kerl, J. Sturm, and D. Cremers, “Robust odometry estimation for RGB-D cameras,” in Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA '13), pp. 3748–3754, Karlsruhe, Germany, May 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. T. Whelan, H. Johannsson, M. Kaess, J. J. Leonard, and J. McDonald, “Robust real-time visual odometry for dense RGB-D mapping,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '13), pp. 5724–5731, Karlsruhe, Germany, May 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. C. Choi and H. I. Christensen, “RGB-D object tracking: a particle filter approach on GPU,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1084–1091, Tokyo, Japan, November 20143. View at Publisher · View at Google Scholar
  11. K. H. Strobl, A flexible approach to close-range 3-d modeling [M.S. dissertation], Technische Universität München, München, Germany, 2014.
  12. E. Mair, K. H. Strobl, T. Bodenmüller, M. Suppa, and D. Burschka, “Real-time image-based localization for hand-held 3D-modeling,” Künstliche Intelligenz, vol. 24, no. 3, pp. 207–214, 2010. View at Publisher · View at Google Scholar
  13. M. Isard and A. Blake, “Contour tracking by stochastic propagation of conditional density,” in Computer Vision—ECCV '96: 4th European Conference on Computer Vision Cambridge, UK, April 15–18, 1996 Proceedings, Volume I, vol. 1064 of Lecture Notes in Computer Science, pp. 343–356, Springer, Berlin, Germany, 1996. View at Publisher · View at Google Scholar
  14. W. Sepp, S. Fuchs, and G. Hirzinger, “Hierarchical featureless tracking for position-based 6-DoF visual servoing,” in Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '06), pp. 4310–4315, IEEE, Beijing, China, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, MIT Press, Cambridge, Mass, USA, 2005.
  16. V. Ferrari, F. Jurie, and C. Schmid, “From images to shape models for object detection,” International Journal of Computer Vision, vol. 87, no. 3, pp. 284–303, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM, vol. 24, no. 6, pp. 381–395, 1981. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  18. C.-S. Chen and Y.-P. Hung, “RANSAC-based DARCES: a new approach to fast automatic registration of partially overlapping range images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 11, pp. 1229–1234, 1999. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Winkelbach, “Efficient methods for solving 3D-Puzzle-Problems,” it-Information Technology, vol. 50, no. 3/2009, pp. 199–201, 2008. View at Publisher · View at Google Scholar
  20. B. Drost, M. Ulrich, N. Navab, and S. Ilic, “Model globally, match locally: efficient and robust 3D object recognition,” in Proceedings of the 23rd IEEE Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 998–1005, San Francisco, Calif, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. U. Hillenbrand, “Consistent parameter clustering: definition and analysis,” Pattern Recognition Letters, vol. 28, no. 9, pp. 1112–1122, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. R. B. Rusu, N. Blodow, and M. Beetz, “Fast Point Feature Histograms (FPFH) for 3D registration,” in Proceedings of the 2009 IEEE International Conference on Robotics and Automation (ICRA '09), pp. 3212–3217, Kobe, Japan, May 2009. View at Publisher · View at Google Scholar
  23. A. Aldoma, Z.-C. Marton, F. Tombari et al., “Tutorial: point cloud library: three-dimensional object recognition and 6 dof pose estimation,” IEEE Robotics & Automation Magazine, vol. 19, no. 3, pp. 80–91, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. R. B. Rusu, N. Blodow, Z. Marton, and M. Beetz, “Aligning point cloud views using persistent feature histograms,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '08), Acropolis Convention Center, Nice, France, September 2008.
  25. N. Gelfand, N. J. Mitra, L. J. Guibas, and H. Pottmann, “Robust global registration,” in Proceedings of the 3rd Eurographics Symposium on Geometry Processing (SGP '05), M. Desbrun and H. Pottmann, Eds., pp. 197–206, Eurographics Association, 2005.
  26. P. Li, P. Cheng, M. A. Sutton, and S. R. McNeill, “Three-dimensional point cloud registration by matching surface features with relaxation labeling method,” Experimental Mechanics, vol. 45, no. 1, pp. 71–82, 2005. View at Publisher · View at Google Scholar · View at Scopus
  27. G. Barequet and M. Sharir, “Partial surface and volume matching in three dimensions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 9, pp. 929–948, 1997. View at Publisher · View at Google Scholar · View at Scopus
  28. G. Barequet and M. Sharir, “Partial surface matching by using directed footprints,” Computational Geometry: Theory and Applications, vol. 12, no. 1-2, pp. 45–62, 1999. View at Publisher · View at Google Scholar · View at MathSciNet
  29. F. Tombari and L. Di Stefano, “Hough voting for 3D object recognition under occlusion and clutter,” IPSJ Transactions on Computer Vision and Applications, vol. 4, pp. 20–29, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. J. Glover, R. Rusu, and G. Bradski, “Monte Carlo pose estimation with quaternion kernels and the Bingham distribution,” in Proceedings of the Robotics: Science and Systems Conference, Los Angeles, Calif, USA, June 2011.
  31. J. Glover and S. Popovic, “Bingham procrustean alignment for object detection in clutter,” in Proceedings of the 26th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '13), pp. 2158–2165, Tokyo, Japan, November 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. T. Bodenmüller, Streaming surface reconstruction from real time 3D measurements [M.S. thesis], Technische Universität München, Munich, Germany, 2009.
  33. W. R. Scott, G. Roth, and J.-F. Rivest, “View planning for automated three-dimensional object reconstruction and inspection,” ACM Computing Surveys, vol. 35, no. 1, pp. 64–96, 2003. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Chen, Y. Li, and N. M. Kwok, “Active vision in robotic systems: a survey of recent developments,” International Journal of Robotics Research, vol. 30, no. 11, pp. 1343–1377, 2011. View at Publisher · View at Google Scholar · View at Scopus
  35. M. Karaszewski, R. Sitnik, and E. Bunsch, “On-line, collision-free positioning of a scanner during fully automated three-dimensional measurement of cultural heritage objects,” Robotics and Autonomous Systems, vol. 60, no. 9, pp. 1205–1219, 2012. View at Publisher · View at Google Scholar · View at Scopus
  36. S. Khalfaoui, R. Seulin, Y. Fougerolle, and D. Fofi, “An efficient method for fully automatic 3D digitization of unknown objects,” Computers in Industry, vol. 64, no. 9, pp. 1152–1160, 2013. View at Publisher · View at Google Scholar · View at Scopus
  37. L. Torabi and K. Gupta, “An autonomous six-DOF eye-in-hand system for in situ 3D object modeling,” International Journal of Robotics Research, vol. 31, no. 1, pp. 82–100, 2012. View at Publisher · View at Google Scholar · View at Scopus
  38. J. I. Vasquez-Gomez, L. E. Sucar, and R. Murrieta-Cid, “View/state planning for three-dimensional object reconstruction under uncertainty,” Autonomous Robots, 2015. View at Publisher · View at Google Scholar · View at Scopus
  39. U. Thomas, S. Kriegel, and M. Suppa, “Fusing color and geometry information for understanding cluttered scenes,” in Proceedings of the International Conference on Intelligent Robots and Systems IROS: Robots in Clutter Workshop (IROS '14), Chicago, Ill, USA, September 2014.
  40. K. Bae and D. D. Lichti, “Automated registration of unorganized point clouds from terrestrial laser scanners,” in International Archives of Photogrammetry and Remote Sensing, vol. 35 of Proceedings of ISPRS Working Group V/2, pp. 222–227, 2004. View at Google Scholar
  41. M. Pauly, M. Gross, and L. P. Kobbelt, “Efficient simplification of point-sampled surfaces,” in Proceedings of the IEEE Visualisation (VIS '02), pp. 163–170, Boston, Mass, USA, November 2002. View at Scopus
  42. M. Pauly, R. Keiser, and M. H. Gross, “Multi-scale feature extraction on point-sampled surfaces,” Computer Graphics Forum, vol. 22, no. 3, pp. 281–289, 2003. View at Publisher · View at Google Scholar · View at Scopus
  43. S. Gumhold, X. Wang, and R. Macleod, “Feature extraction from point clouds,” in Proceedings of the 10th International Meshing Roundtable, pp. 293–305, October 2001.
  44. E. W. Weisstein, CRC Concise Encyclopedia of Mathematics, Chapman & Hall/CRC, 2nd edition, 2002.
  45. F. Zacharias, C. Borst, and G. Hirzinger, “Capturing robot workspace structure: representing robot capabilities,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '07), pp. 3229–3236, San Diego, Calif, USA, November 2007. View at Publisher · View at Google Scholar · View at Scopus
  46. J. C. Mitchell, “Sampling rotation groups by successive orthogonal images,” SIAM Journal on Scientific Computing, vol. 30, no. 1, pp. 525–547, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  47. A. Yershova, S. Jain, S. M. Lavalle, and J. C. Mitchell, “Generating uniform incremental grids on SO3 using the hopf fibration,” International Journal of Robotics Research, vol. 29, no. 7, pp. 801–812, 2010. View at Publisher · View at Google Scholar · View at Scopus
  48. J. Arvo, “Fast random rotation matrices,” in Graphics Gems III, D. Kirk, Ed., pp. 117–120, Academic Press Professional, San Diego, Calif, USA, 1992. View at Google Scholar
  49. K. Shoemake, “Uniform random rotations,” in Graphics Gems III, D. Kirk, Ed., pp. 124–132, Academic Press Professional, San Diego, Calif, USA, 1992. View at Google Scholar
  50. W. Feiten, P. Atwal, R. Eidenberger, and T. Grundmann, “6D pose uncertainty in robotic perception,” in Advances in Robotics Research, pp. 89–98, Springer, Berlin, Germany, 2009. View at Google Scholar
  51. G. H. Givens and J. A. Hoeting, Computational Statistics, Wiley, Hoboken, NJ, USA, 2005. View at MathSciNet
  52. J. L. Hintze and R. D. Nelson, “Violin plots: a box plot-density trace synergism,” The American Statistician, vol. 52, no. 2, pp. 181–184, 1998. View at Google Scholar · View at Scopus
  53. M. Suppa, S. Kielhöfer, J. Langwald, F. Hacker, K. H. Strobl, and G. Hirzinger, “The 3D-modeller: a multi-purpose vision platform,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '07), pp. 781–787, Rome, Italy, April 2007. View at Publisher · View at Google Scholar · View at Scopus
  54. P. Kremer, T. Wimb, J. Artigas et al., “Multimodal telepresent control of DLR's rollin' justin,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '09), pp. 1601–1602, Kobe, Japan, May 2009. View at Publisher · View at Google Scholar
  55. T. Bodenmüller, W. Sepp, M. Suppa, and G. Hirzinger, “Tackling multi-sensory 3D data acquisition and fusion,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '07), pp. 2180–2185, San Diego, Calif, USA, November 2007. View at Publisher · View at Google Scholar · View at Scopus
  56. S. Kriegel, Autonomous 3D modeling of unknown objects for active scene exploration [Ph.D. thesis], Technische Universität München (TUM), 2015.