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New Approach for Measured Surface Localization Based on Umbilical Points

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Abstract

Measured surface localization (MSL) is one of the key essentials for the assessment of form error in precision manufacturing. Currently, the researches on MSL have focused on the corresponding relation search between two surfaces, the performance improvement of localization algorithms and the uncertainty analysis of localization. However, low efficiency, limitation of localization algorithms and mismatch of multiple similarities of feature points with no prior are the common disadvantages for MSL. In order to match feature points quickly and fulfill MSL efficiently, this paper presents a new localization approach for measured surfaces by extracting the generic umbilics and estimating their single complex variables, describing the match methods of ambiguous relation at umbilics, presenting the initial localization process of one pair matched points, refining MSL on the basis of obtained closet points for some measured points by the improvement directed projection method. In addition, the proposed algorithm is simulated in two different types of surfaces, two different localization types and multiple similar surfaces, also tested with the part of B-spline surface machined and bottle mould with no knowledge, finally the initial and accurate rigid body transformation matrix, localization errors between two surfaces and execution time are got. The experimental results show that the proposed method is feasible, more accurate in localization and high in efficiency. The proposed research can not only improve the accuracy and performance of form error assessment, but also provide an effective guideline for the integration of different types of measured surfaces.

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References

  1. H Robert, V Daniel, A Bilal. Engineering the smart factory.Chinese Journal of Mechanical Engineering, 2016, 29(6): 1046–1051.

  2. H Ding, L M Zhu. Geometric theories and methods for digital manufacturing of complex surface. Beijing: Science Press, 2011. (in Chinese)

  3. P J Besl, N D Mckay. A method for registration of 3-D shapes. Robotics - DL tentative. International Society for Optics and Photonics, 1992, 14(2): 239–256.

  4. S F Li, P Wang, Z K Shen. A survey of iterative closet point algorithm. Signal Processing, 2009, 25(10): 1582–1588. (in Chinese)

  5. J Wang, L S Zhou, L Y Zhang, et al. Surface matching based on genetic algorithm. Journal of Image and Graphics, 2007, 12(4): 695–699.

  6. G Papaioannou, A Katabasis, T Theoharis. Reconstruction of three dimensional objects through matching of their parts. IEEE Computer Society, 2002, 24(1): 114–124.

  7. H G Cui, Y Chen, J X Liu, et al. Simple matching method for surface reconstruction error inspection. Journal of Naval University of Engineering, 2012, 24(2): 62–66. (in Chinese)

  8. G S Tan, L Y Zhang. Pose registration technology of complex surface based on the maximum-entropy principle. Transaction of the Chinese Society for Agricultural Machinery, 2014, 45(7): 300–305. (in Chinese)

  9. J T Xu, W J Liu, Y W Sun. Algorithm for free-form surface matching based on curvatures. Journal of Computer-Aided Design and Computer Graphics,2007, 19(2):193–197. (in Chinese)

  10. L He, C P Yu, G Y Lu. Error evaluation of free-from surface based on curvature matching. Infrared and Laser Engineering, 2009, 38: 335–338. (in Chinese)

  11. K H Ko, T Maekawa, N M Patrikalakis. Algorithm for optimal free-form object matching. Computer Aided Design, 2003, 35(10): 913– 923.

  12. C S Chua, R Jarvis. Point signatures:a new representation for 3D object recognition. International Journal of Computers Vision, 1997, 25(1): 63–85.

  13. G C Sharp, S W Lee, D K Wehe. ICP registration using invariant feature. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(1): 90–102.

  14. S M Yamany, A A Farag. Surface signature: an orientation independent free-form surface representation scheme for the purpose of objects registration and matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(8): 1105–1120.

  15. A E Johnson, M Hebert. Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(5): 433–499.

  16. G Barequent, M Sharir. Partial surface matching by using directed footprints. Computational Geometry: Theory and Applications, 1999, 12(1–2): 45–62.

  17. H Pottmann, J Wallner, Q X Huang, et al. Integral invariants for robust geometry processing. Computer Aided Geometric Design, 2009, 26(1): 37–60.

  18. Q X Huang, S Flory, N Gelfand, et al. Reassembling fractured objects by geometric matching. ACM Transactions on Graphics, 2006, 25(3): 569–578.

  19. F Tombari, S Salti, L D Stefano, et al. Unique signatures of histograms for local surface description. 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5–11, 2010: 356–369.

  20. J Jung, G Sohg, K Bang, et al. Matching aerial images to 3D building models using context-based geometric hashing. Isprs Annals of Photogrammetry Remote Sensing and Spatial Information, 2016, 16(6): 17–23.

  21. X L Pan, L Y Zhang, Y W Jie, et al. Algorithm for three-dimensional partial surface matching. Journal of Nanjing University of Aeronautics and Astronautics, 2004, 36(5): 544–549. (in Chinese)

  22. G Xiao, S H Ong, K W C Foong. Efficient partial-surface registration for 3D objects. Computer Vision and Image Understanding, 2005, 98(2): 271–293.

  23. J Wang, L S Zhou. Surface rough matching algorithm based on maximum weight clique. Journal of Computer-Aided Design and Computer Graphics, 2008, 20(2): 167–173. (in Chinese)

  24. L Shi, G Z Sun, Z Q Wang, et al. Maximum independent set algorithm for surface matching. Mechanical Science and Technology for Aerospace Engineering, 2010, 29(12): 1617–1622.

  25. J J Du, D Gao, L B Kong, et al. Study of matching methods for error evaluation of optical free-form surface. Optics and Precision Engineering, 2006, 14(1): 133–138.

  26. Y Yu, J Lu, X C Wang. Modeling and analysis of the best match in free-form surface measuring. Mechanical Science and Technology for Aerospace Engineering, 2001, 20(3): 465–468, 471.

  27. T Maekawa, F E Wolter, N M Patrikalakis. Umbilics and lines of curvature for shape interrogation. Computer Aided Geometric Design, 1996, 13(12): 133–161.

  28. C Q Qi, Y Z Chen, Y Gan, et al. The key technology research of complex surface reconstruction in reverse engineering. Chinese Journal of Mechanical Engineering, 2003, 39(4): 131–135.

  29. Y Ma, J P Kruth. Parametrization of randomly measured points for least squares fitting of B-spline curves and surfaces. Computer Aided Design, 1995, 27(9): 663–675.

  30. K H Ko, T Maekawa, N M Patrikalakis. Shape intrinsic properties for free-form object matching. Journal of Computing and Information Science in Engineering, 2003, 3(4): 325–333.

  31. Z Xu, N Dai, C D ZHang, et al. Multi-source data fusion based on iterative deformation. Journal of Mechanical Engineering, 2014, 50(7): 191–198.

  32. K H Ko, T Maekawa, N M Patrikalakis. Algorithm for optimal partial matching of free-form objects with scaling effects. Graphical Model, 2005(67): 120–148.

  33. L A Piefl, W Tiller. The NURBS Book. Springer, New York, 1995.

  34. Y S Liu, J C Paul, J H Yong, et al. Automatic least-squares projection of points onto point clouds with applications in reverse engineering. Computer Adied Design, 2006(38): 1251–1263.

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Correspondence to Guo-Fu Yin.

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Supported by Science and Technology Supporting Projects of China (Grant No. 2015BAF27B01), Sichuan Provincial Science and Technology Supporting Program of China (Grant Nos. 2014GZ0119, 2017GZ0350), and Open Research Fund of Key Laboratory of Manufacturing and Automation, Xihua University (Grant No. S2jj2013-042).

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Xiao, XP., Yin, M., Heng, L. et al. New Approach for Measured Surface Localization Based on Umbilical Points. Chin. J. Mech. Eng. 30, 1203–1215 (2017). https://doi.org/10.1007/s10033-017-0171-8

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  • DOI: https://doi.org/10.1007/s10033-017-0171-8

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