Digital image-stitching techniques applied to dynamic measurement of large structures

  • Danilo Damasceno Sabino
  • Joao Antonio Pereira
  • Peyman Poozesh
Technical Paper
  • 11 Downloads

Abstract

In this work has been discussed a propose of using three-dimensional point-tracking measuring technique for measurement of large structures in which the entire field of interest could not be captured by a unique stereo-vision system. Two pairs of a stereo-system are used to capture the whole field of measurement of interest of the model and point cloud registration techniques are exploited aiming at extending the capability of digital image measurement system for dynamic measuring (displacement) for large-scale structures. Three different image registration algorithms, principle component analysis, singular value decomposition, and iterative closest point are used in the stitching process to join the point clouds obtained with the multi-camera system. The proposal is applied to vibration measurement of a wind turbine blade of 2.3 m in length, whose field of view of the whole set of points of interest is greater than the field of view of a unique stereo-vision system. The reconstruction of the set of measured points was obtained from the junction of the points clouds of each stereo-system in a reference system and a probabilistic and statistical analysis of the error generated by the transformation of the point clouds was performed. And finally, the frequencies of the structure obtained from the digital image measured data were compared with the values obtained from a set of accelerometers.

Keywords

Registration 3DPT Data stitching Large structures Wind turbine blades 

References

  1. 1.
    Sutton M, Wolters W, Peters W, Ranson W, McNeill S (1983) Determination of displacements using an improved digital correlation method. Image Vis Comput 1:133–139CrossRefGoogle Scholar
  2. 2.
    Sutton M, Mingqi C, Peters W, Chao Y, McNeill S (1986) Application of an optimized digital correlation method to planar deformation analysis. Image Vis Comput 4:143–150CrossRefGoogle Scholar
  3. 3.
    Chu TC, Ranson WF, Sutton MA (1985) Applications of digital-image-correlation techniques to experimental mechanics. Exp Mech 25:232–244CrossRefGoogle Scholar
  4. 4.
    Baqersad J, Poozesh P, Niezrecki C, Avitabile P (2016) Photogrammetry and optical methods in structural dynamics—a review. Mech Syst Signal Process.  https://doi.org/10.1016/j.ymssp.2016.02.011 Google Scholar
  5. 5.
    Chen F, Chen X, Xie X, Feng X, Yang L (2013) Full-field 3D measurement using multi-camera digital image correlation system. Opt Lasers Eng 51:1044–1052CrossRefGoogle Scholar
  6. 6.
    Wang Y, Lava P, Coppieters S, Houtte PV, Debruyne D (2013) Application of a multi-camera stereo DIC set-up to assess strain fields in an Erichsen test: methodology and validation. Strain 49:190–198CrossRefGoogle Scholar
  7. 7.
    Malesa M, Malowany K, Kujawińska M (2014) Multi-camera DIC system with a spatial data stitching procedure for measurements of engineering objects. Photonics Lett Pol 6:157–159Google Scholar
  8. 8.
    Nguyen TN, Huntley JM, Burguete RL, Coggrave CR (2012) Multiple-view shape and deformation measurement by combining fringe projection and digital image correlation. Strain 48:256–266CrossRefGoogle Scholar
  9. 9.
    LeBlanc B, Niezrecki C, Avitabile P, Chen J, Sherwood J (2013) Damage detection and full surface characterization of a wind turbine blade using three-dimensional digital image correlation. Struct Health Monit 12:430–439CrossRefGoogle Scholar
  10. 10.
    Barone S, Paoli A, Razionale AV (2012) Three-dimensional point cloud alignment detecting fiducial markers by structured light stereo imaging. Mach Vis Appl 23:217–229CrossRefGoogle Scholar
  11. 11.
    Rusu RB, Blodow N, Beetz M (2009) Fast Point Feature Histograms (FPFH) for 3D registration. IEEE Int Conf Robot Autom 2009:3212–3217Google Scholar
  12. 12.
    Li N, Cheng P, Sutton MA, McNeill SR (2005) Three-dimensional point cloud registration by matching surface features with relaxation labeling method. Exp Mech 45:71–82CrossRefGoogle Scholar
  13. 13.
    Wang F, Ye Y, Hu X, Shan J (2016) Point cloud registration by combining shape and intensity contexts. In: 2016 9th IAPR workshop on pattern recognition in remote sensing (PRRS), pp 1–6Google Scholar
  14. 14.
    Poozesh P, Baqersad J, Niezrecki C, Avitabile P, Harvey E, Yarala R (2016) Large-area photogrammetry based testing of wind turbine blades. Mech Syst Signal Process.  https://doi.org/10.1016/j.ymssp.2016.07.021 Google Scholar
  15. 15.
    Urbanic RJ, ElMaraghy HA, ElMaraghy WH (2008) A reverse engineering methodology for rotary components from point cloud data. Int J Adv Manuf Technol 37:1146–1167CrossRefGoogle Scholar
  16. 16.
    Keaveney S, Keogh C, Gutierrez-Heredia L, Reynaud EG (2016) Applications for advanced 3D imaging, modelling, and printing techniques for the biological sciences. In: 2016 22nd international conference on virtual system & multimedia (VSMM), pp 1–8Google Scholar
  17. 17.
    Smith LI (2002) A tutorial on principal components analysis. Cornell University, Ithaca, pp 51–65Google Scholar
  18. 18.
    Bellekens B, Spruyt V, Berkvens R, Weyn M (2014) A survey of rigid 3d pointcloud registration algorithms. In: Fourth international conference on ambient computing, applications, services and technologies. Citeseer, pp 8–13Google Scholar
  19. 19.
    Kabsch W (1978) A discussion of the solution for the best rotation to relate two sets of vectors. Acta Crystallogr 34:827–828CrossRefGoogle Scholar
  20. 20.
    Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. Pattern Anal Mach Intell IEEE Trans 14:239–256CrossRefGoogle Scholar
  21. 21.
    Rusu RB (2010) Semantic 3D object maps for everyday manipulation in human living environments. KI Künstliche Intelligenz 24:345–348CrossRefGoogle Scholar
  22. 22.
    PONTOS v6.3. GOM mbH, Braunschweig, 2011Google Scholar
  23. 23.
    ARAMIS v6.3. GOM mbH, Braunschweig, 2011Google Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  • Danilo Damasceno Sabino
    • 1
  • Joao Antonio Pereira
    • 1
  • Peyman Poozesh
    • 2
  1. 1.Department of Mechanical EngineeringUNESP-Univ Estadual PaulistaIlha SolteiraBrazil
  2. 2.University of Massachusetts LowellLowellUSA

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