Performance verification of a photogrammetric scanning system for micro-parts using a three-dimensional artifact: adjustment and calibration

  • F. Lavecchia
  • M. G. Guerra
  • L. M. Galantucci


Performance verification is a fundamental issue to assure the traceability of a measurement instrument. This issue is very important for non-contact 3D scanning systems, also for the limited number of existing standards. There are many factors affecting the process in a photogrammetric scanning system, and they have to be considered during a performance verification. In this context, it is crucial to completely define the camera model and its spatial locations and orientations during the scan, estimating the intrinsic and extrinsic parameters. There are two main proposals in this paper. Firstly, authors investigated on how the reliability of the adjustment procedure could be improved, adopting a more complex geometry of the reference object, using a three-dimensional one instead of a bi-dimensional pattern of targets. Secondly, an approach to a calibration procedure for photogrammetric scanning system has been drawn up and applied, using the same artifact used for the adjustment. The ISO 15530-3 2011 standard was adopted for the uncertainty assessment of the results.


Micro-scanning Calibration Adjustment Photogrammetry 3D image processing Uncertainty 


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The authors would like to thank Ph.D. Marta Pesce for her valuable contribution to the first concept of this paper.


  1. 1.
    Dornfeld D, Min S, Takeuchi Y (2006) Recent advances in mechanical micromachining. CIRP Ann - Manuf Technol 55:745–768. CrossRefGoogle Scholar
  2. 2.
    Byrne G, Dornfeld D, Denkena B (2003) Advancing cutting technology. CIRP Ann - Manuf Technol 52(2):483–507CrossRefGoogle Scholar
  3. 3.
    Qin Y, Brockett A, Ma Y et al (2009) Micro-manufacturing: research, technology outcomes and development issues. Int J Adv Manuf Technol 47(9–12):821–837Google Scholar
  4. 4.
    Hansen HN, Carneiro K, Haitjema H, De Chiffre L (2006) Dimensional micro and nano metrology. CIRP Ann - Manuf Technol 55(2):721–743CrossRefGoogle Scholar
  5. 5.
    Hoffmann J, Weckenmann A, Sun Z (2008) Electrical probing for dimensional micro metrology. CIRP J Manuf Sci Technol 1(1):59–62CrossRefGoogle Scholar
  6. 6.
    Savio E, De Chiffre L, Schmitt R (2007) Metrology of freeform shaped parts. CIRP Ann - Manuf Technol 56:810–835. CrossRefGoogle Scholar
  7. 7.
    Yang P, Takamura T, Takahashi S, Takamasu K, Sato O, Osawa S, Takatsuji T (2011) Development of high-precision micro-coordinate measuring machine: multi-probe measurement system for measuring yaw and straightness motion error of XY linear stage. Precis Eng 35(3):424–430CrossRefGoogle Scholar
  8. 8.
    Danzl R, Helmli F, Scherer S (2011) Focus variation—a robust technology for high resolution optical 3D surface metrology. Strojniški Vestn – J Mech Eng 2011:245–256. CrossRefGoogle Scholar
  9. 9.
    Thian SCH, Feng W, Wong YS, Fuh JYH, Loh HT, Tee KH, Tang Y, Lu L (2007) Dimensional measurement of 3D microstructure based on white light interferometer. J Phys Conf Ser 48:1435–1446. CrossRefGoogle Scholar
  10. 10.
    Conroy M, Armstrong J (2005) A comparison of surface metrology techniques. J Phys Conf Ser 13:458–465. CrossRefGoogle Scholar
  11. 11.
    Nouira H, Salgado J, El-Hayek N et al (2014) Setup of a high-precision profilometer and comparison of tactile and optical measurements of standards. Meas Sci Technol 25:44016. CrossRefGoogle Scholar
  12. 12.
    Azevedo CRF, Marques ER (2010) Three-dimensional analysis of fracture, corrosion and wear surfaces. Eng Fail Anal 17:286–300. CrossRefGoogle Scholar
  13. 13.
    Fisher RF, Hintenlang DE (2008) Micro-CT imaging of MEMS components. J Nondestruct Eval 27(4):115–125CrossRefGoogle Scholar
  14. 14.
    Jiménez R, Torralba M, Yagüe-Fabra J, Ontiveros S, Tosello G (2017) Experimental approach for the uncertainty assessment of 3D complex geometry dimensional measurements using computed tomography at the mm and sub-mm scales. Sensors 17:1137. CrossRefGoogle Scholar
  15. 15.
    Angelo Beraldin J, Mackinnon D, Cournoyer L (2015) Metrological characterization of 3D imaging systems: progress report on standards developments. Int Congr Metrol 3:1–21. Google Scholar
  16. 16.
    Percoco G, Modica F, Fanelli S (2016) Image analysis for 3D micro-features: a new hybrid measurement method. Precis Eng 48:123–132. CrossRefGoogle Scholar
  17. 17.
    Percoco G, Guerra MG, Sanchez Salmeron AJ, Galantucci LM (2017) Experimental investigation on camera calibration for 3D photogrammetric scanning of micro-features for micrometric resolution. Int J Adv Manuf Technol 91:2935–2947. CrossRefGoogle Scholar
  18. 18.
    Gallo A, Muzzupappa M, Bruno F (2014) 3D reconstruction of small sized objects from a sequence of multi-focused images. J Cult Herit 15:173–182. CrossRefGoogle Scholar
  19. 19.
    Galantucci LM, Pesce M, Lavecchia F (2015) A stereo photogrammetry scanning methodology, for precise and accurate 3D digitization of small parts with sub-millimeter sized features. CIRP Ann - Manuf Technol 64:507–510. CrossRefGoogle Scholar
  20. 20.
    Galantucci LM, Lavecchia F, Percoco G (2013) Multistack close range photogrammetry for low cost submillimeter metrology. J Comput Inf Sci Eng 13:44501. CrossRefGoogle Scholar
  21. 21.
    Mendricky R (2016) Determination of measurement accuracy of optical 3D scanners. 1565–1572.
  22. 22.
    Bernal C, De Agustina B, Marín MM, Camacho AM (2014) Accuracy analysis of fringe projection systems based on blue light technology. Key Eng Mater 615:9–14. CrossRefGoogle Scholar
  23. 23.
    Vagovský J, Buranský I, Görög A (2015) Evaluation of measuring capability of the optical 3D scanner. Energy Procedia 100:1198–1206. CrossRefGoogle Scholar
  24. 24.
    McCarthy MB, Brown SB, Evenden A, Robinson AD (2011) NPL freeform artefact for verification of non-contact measuring systems. Soc PhotoOptical 7864:78640K–78640K–13. Google Scholar
  25. 25.
    Barnfather JD, Goodfellow MJ, Abram T (2016) Photogrammetric measurement process capability for metrology assisted robotic machining. Meas J Int Meas Confed 78:29–41. CrossRefGoogle Scholar
  26. 26.
    Brown DC (1971) Close-range camera calibration. Photogramm Eng 37:855–866Google Scholar
  27. 27.
    Percoco G, Salmerón AJS (2017) 3D image based modelling for inspection of objects with micro-features, using inaccurate calibration patterns: an experimental contribution. Int J Interact Des Manuf 11:415–425. CrossRefGoogle Scholar
  28. 28.
    Sims-Waterhouse D, Piano S, Leach R (2017) Verification of micro-scale photogrammetry for smooth three-dimensional object measurement. Meas Sci Technol 28:55010. CrossRefGoogle Scholar
  29. 29.
    Sims-Waterhouse D, Bointon P, Piano S, Leach RK (2017) Experimental comparison of photogrammetry for additive manufactured parts with and without laser speckle projection. 103290W.
  30. 30.
    Lavecchia F, Guerra MG, Galantucci LM (2017) The influence of software algorithms on photogrammetric micro-feature measurement’s uncertainty. Int J Adv Manuf Technol 1–15., 93
  31. 31.
    JCGM (Joint Committee for Guides in Metrology)200:2008- International Vocabulary of Metrology – Basic and General Concepts and Associated Terms (VIM), 2008Google Scholar
  32. 32.
    Neuschaefer-rube U, Neugebauer M, Dziomba T, et al (2013) New developments of measurement standards and procedures for micro and nanometrology at the PtbGoogle Scholar
  33. 33.
    Ritter M, Dziomba T, Kranzmann A, Koenders L (2007) A landmark-based 3D calibration strategy for SPM. Meas Sci Technol 18:404–414. CrossRefGoogle Scholar
  34. 34.
    De Chiffre L, Carli L, Eriksen RS (2011) Multiple height calibration artefact for 3D microscopy. CIRP Ann - Manuf Technol 60:535–538. CrossRefGoogle Scholar
  35. 35.
    Remondino F (2006) Detectors and descriptors for photogrammetric applications. Int Arch Photogramm Remote Sens Spat Inf Sci 2–7Google Scholar
  36. 36.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110. CrossRefGoogle Scholar
  37. 37.
    (2016) Agisoft Photoscan User Manual.
  38. 38.
  39. 39.
    ISO 5436-1:2000 Geometrical Product Specifications (GPS) – Surface texture: Profile method; Measurement standards – Part 1: Material measuresGoogle Scholar
  40. 40.
    JCFGIM. JIOSGI 2008;50:134. Evaluation of measurement data — Guide to the expression of uncertainty in measurementGoogle Scholar
  41. 41.
    ISO 15530-3:2011 Geometrical product specifications (GPS) -- Coordinate measuring machines (CMM): Technique for determining the uncertainty of measurement -- Part 3: Use of calibrated workpieces or measurement standardsGoogle Scholar
  42. 42.
    UNI EN ISO 4288:2000 Geometrical Product Specifications (GPS), Surface texture: Profile method, Rules and procedures for the assessment of surface textureGoogle Scholar
  43. 43.
    Accreditation AA for L. G104 - Guide for Estimation of Measurement Uncertainty In Testing December 2014. Am Assoc Lab Accredit 2014:1–31Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • F. Lavecchia
    • 1
  • M. G. Guerra
    • 1
  • L. M. Galantucci
    • 1
  1. 1.Dipartimento di Meccanica, Matematica e ManagementPolitecnico di BariBariItaly

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