Advertisement

Sparse Point Registration

  • Rangaprasad Arun SrivatsanEmail author
  • Prasad Vagdargi
  • Howie Choset
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 10)

Abstract

This work introduces a Sparse Point Registration (SPR) method for performing robust registration given the geometric model of the object and few sparse point-measurements (<20) of the object’s surface. Such a method is of critical importance in applications such as probing-based surgical registration, manipulation, etc. Our approach for SPR is iterative and in each iteration, the current best pose estimate is perturbed to generate several poses. Among the generated poses, the best pose as evaluated by an inexpensive cost function is used to estimate the locally optimum registration. This process is repeated, until the pose converges within a tolerance bound. Two variants of the SPR are developed: deterministic (dSPR) and probabilistic (pSPR). Compared to the pSPR, the dSPR is faster in converging to the local optimum, and requires fewer parameters to be tuned. On the other hand, the pSPR provides uncertainty information in addition to the registration estimate. Both the approaches were evaluated using various standard data sets and the compared to results obtained using state-of-the-art methods. Upon comparison with other methods, both dSPR and pSPR were found to be robust to initial pose errors as well as noise in measurements. The effectiveness of the approach is also demonstrated with an application of robot-probing based registration.

Notes

Acknowledgements

This work was supported by NRI large grant IIS-1426655. The authors would also like to thank Mr. Nicolas Zevallos, Mr. Ky Woodard and the Center for Machine Learning and Health, Carnegie Mellon University.

References

  1. 1.
    Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. In: Robotics-DL Tentative, pp. 586–606. International Society for Optics and Photonics (1992)Google Scholar
  2. 2.
    Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Proceedings of 3rd International Conference on 3-D Digital Imaging and Modeling, pp. 145–152. IEEE (2001)Google Scholar
  3. 3.
    Segal, A., Haehnel, D., Thrun, S.: Generalized-ICP. In: Robotics: Science and Systems, vol. 2, no. 4 (2009)Google Scholar
  4. 4.
    Moghari, M.H., Abolmaesumi, P.: Point-based rigid-body registration using an unscented Kalman filter. IEEE Trans. Med. Imaging 26(12), 1708–1728 (2007)CrossRefGoogle Scholar
  5. 5.
    Billings, S.D., Boctor, E.M., Taylor, R.H.: Iterative most-likely point registration (IMLP): a robust algorithm for computing optimal shape alignment. PLoS ONE 10(3) (2015)Google Scholar
  6. 6.
    Audette, M.A., Ferrie, F.P., Peters, T.M.: An algorithmic overview of surface registration techniques for medical imaging. Med. Image Anal. 4(3), 201–217 (2000)CrossRefGoogle Scholar
  7. 7.
    Simon, D.A., Hebert, M., Kanade, T.: Techniques for fast and accurate intrasurgical registration. J. Image Guided Surg. 1(1), 17–29 (1995)CrossRefGoogle Scholar
  8. 8.
    Ma, B., Ellis, R.E.: Robust registration for computer-integrated orthopedic surgery: laboratory validation and clinical experience. Medical Image Anal. 7, 237–250 (2003)CrossRefGoogle Scholar
  9. 9.
    Ma, B., Ellis, R.E.: Surface-based registration with a particle filter. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 566–573. Springer (2004)Google Scholar
  10. 10.
    Srivatsan, R.A., Rosen, G.T., Naina, F.D., Choset, H.: Estimating SE(3) elements using a dual quaternion based linear Kalman filter. In: Robotics: Science and Systems (2016)Google Scholar
  11. 11.
    Penney, G.P., Edwards, P.J., King, A.P., Blackall, J.M., Batchelor, P.G., Hawkes, D.J.: A stochastic iterative closest point algorithm (stochastICP). In: MICCAI, pp. 762–769 (2001)Google Scholar
  12. 12.
    Horn, B.K.: Closed-form solution of absolute orientation using unit quaternions. JOSA A 4(4), 629–642 (1987)CrossRefGoogle Scholar
  13. 13.
    Tam, G.K., Cheng, Z.-Q., Lai, Y.-K., Langbein, F.C., Liu, Y., Marshall, D., Martin, R.R., Sun, X.-F., Rosin, P.L.: Registration of 3d point clouds and meshes: a survey from rigid to nonrigid. IEEE Trans. Vis. Comput. Graph. 19(7), 1199–1217 (2013)CrossRefGoogle Scholar
  14. 14.
    Tsin, Y., Kanade, T.: A correlation-based approach to robust point set registration. In: European Conference on Computer Vision, pp. 558–569. Springer (2004)Google Scholar
  15. 15.
    Phillips, J.M., Liu, R., Tomasi, C.: Outlier robust ICP for minimizing fractional RMSD. In: Sixth International Conference on 3-D Digital Imaging and Modeling, 2007: 3DIM’07, pp. 427–434. IEEE (2007)Google Scholar
  16. 16.
    Gelfand, N., Mitra, N.J., Guibas, L.J., Pottmann, H.: Robust global registration. In: Symposium on Geometry Processing, vol. 2, no. 3, p. 5 (2005)Google Scholar
  17. 17.
    Makadia, A., Patterson, A., Daniilidis, K.: Fully automatic registration of 3D point clouds. In: CVPR, vol. 1, pp. 1297–1304. IEEE (2006)Google Scholar
  18. 18.
    Yang, J., Li, H., Jia, Y.: Go-ICP: solving 3D registration efficiently and globally optimally. In: IEEE International Conference on Computer Vision, pp. 1457–1464 (2013)Google Scholar
  19. 19.
    Pennec, X., Thirion, J.-P.: A framework for uncertainty and validation of 3-D registration methods based on points and frames. Int. J. Comput. Vis. 25(3), 203–229 (1997)CrossRefGoogle Scholar
  20. 20.
    Hauberg, S., Lauze, F., Pedersen, K.S.: Unscented Kalman filtering on Riemannian manifolds. J. Math. Imaging Vis. 46(1), 103–120 (2013)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Srivatsan, R.A., Xu, M., Zevallos, N., Choset, H.: Bingham distribution-based linear filter for online pose estimation. In: Robotics: Science and Systems (2017)Google Scholar
  22. 22.
    Srivatsan, R.A., Choset, H.: Multiple start branch and prune filtering algorithm for nonconvex optimization. In: WAFR (2016)Google Scholar
  23. 23.
    Srivatsan, R.A., Ayvali, E., Wang, L., Roy, R., Simaan, N., Choset, H.: Complementary model update: a method for simultaneous registration and stiffness mapping in flexible environments. In: International Conference on Robotics and Automation, pp. 924–930 (2016)Google Scholar
  24. 24.
    Ma, B., Ellis, R.E.: Spatial-stiffness analysis of surface-based registration. In: MICCAI, pp. 623–630 (2004)Google Scholar
  25. 25.
    Glozman, D., Shoham, M., Fischer, A.: A surface-matching technique for robot-assisted registration. Comput. Aided Surg. 6(5), 259–269 (2001)Google Scholar
  26. 26.
    Gadeyne, K., Bruyninckx, H.: Markov techniques for object localization with force-controlled robots. In: International Conference on Advanced Robotics (2001)Google Scholar
  27. 27.
    Chhatpar, S.R., Branicky, M.S.: Particle filtering for localization in robotic assemblies with position uncertainty. In: IROS, pp. 3610–3617 (2005)Google Scholar
  28. 28.
    Hsiao, K., Kaelbling, L.P.: Task-driven tactile exploration. In: Robotics: Science and Systems (2010)Google Scholar
  29. 29.
    Petrovskaya, A., Khatib, O.: Global localization of objects via touch. IEEE Trans. Robot. 27(3), 569–585 (2011)CrossRefGoogle Scholar
  30. 30.
    Saund, B., Chen, S., Simmons, R.: Touch based localization of parts for high precision manufacturing. In: International Conference on Robotics and Automation. IEEE (2017)Google Scholar
  31. 31.
    Javdani, S., Klingensmith, M., Bagnell, J.A., Pollard, N.S., Srinivasa, S.S.: Efficient touch based localization through submodularity. In: International Conference on Robotics and Automation, pp. 1828–1835. IEEE (2013)Google Scholar
  32. 32.
    Hebert, P., Howard, T., Hudson, N., Ma, J., Burdick, J.W.: The next best touch for model-based localization. In: ICRA, pp. 99–106 (2013)Google Scholar
  33. 33.
    Pauly, M., Gross, M., Kobbelt, L.P.: Efficient simplification of point-sampled surfaces. In: Conference on Visualization’02, pp. 163–170 (2002)Google Scholar
  34. 34.
    Cignoni, P., Montani, C., Scopigno, R.: A comparison of mesh simplification algorithms. Comput. Graph. 22(1), 37–54 (1998)CrossRefGoogle Scholar
  35. 35.
    Garland, M., Heckbert, P.S.: Surface simplification using quadric error metrics. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 209–216. ACM Press/Addison-Wesley Publishing Co. (1997)Google Scholar
  36. 36.
    Turk, G., Levoy, M.: The Stanford 3D Scanning Repository. Stanford University Computer Graphics Laboratory. http://graphics.stanford.edu/data/3Dscanrep
  37. 37.
    AIM@SHAPE Model Repository. Fertility Point Cloud Scan. http://visionair.ge.imati.cnr.it/ontologies/shapes/releases.jsp
  38. 38.
    Srivatsan, R.A., Vagdargi, P., Zevallos, N., Choset, H.: Multimodal registration using stereo imaging and contact sensing. In: Robotics: Science and Systems, Workshop on ‘Revisiting Contact—Turning a Problem into a Solution’ (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Rangaprasad Arun Srivatsan
    • 1
    Email author
  • Prasad Vagdargi
    • 1
  • Howie Choset
    • 1
  1. 1.Robotics Institute at Carnegie Mellon UniversityPittsburghUSA

Personalised recommendations