A practical coordinate unification method for integrated tactile–optical measuring system

Abstract To meet the requirement of both high speed and high accuracy 3D measurements for dimensional metrology, multi-sensor measuring systems have been developed to measure, analyse and reverse engineer the geometry of objects. This paper presents a new development in coordinate unification called the “centroid of spherical centres” method, which can be used instead of the traditional method which uses three datum-points to perform the geometric transformation and unification of tactile and optical sensors. The benefits of the proposed method are improved accuracy in coordinate unification and the method is used to integrate a coordinate measuring machine (CMM) and optical sensors (structured light scanning system and FaroArm laser line probe). A sphere-plate artefact is developed for data fusion of the multi-sensor system and experimental results validate the accuracy and effectiveness of this method.

[1]  Carlos Estrela,et al.  Accuracy of cone beam computed tomography and panoramic and periapical radiography for detection of apical periodontitis. , 2008, Journal of endodontics.

[2]  Yasushi Yagi,et al.  Accurate calibration of intrinsic camera parameters by observing parallel light pairs , 2008, 2008 IEEE International Conference on Robotics and Automation.

[3]  Joaquim Salvi,et al.  A state of the art in structured light patterns for surface profilometry , 2010, Pattern Recognit..

[4]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[5]  Yunbao Huang,et al.  Multi-sensor calibration through iterative registration and fusion , 2009, Comput. Aided Des..

[6]  Wang Jianguo,et al.  Study on a full field of view laser scanning system , 2007 .

[7]  Dimitris Gorpas,et al.  A binocular machine vision system for three-dimensional surface measurement of small objects , 2007, Comput. Medical Imaging Graph..

[8]  Ray A. Jarvis,et al.  A Perspective on Range Finding Techniques for Computer Vision , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Hsi-Yung Feng,et al.  Analysis of digitizing errors of a laser scanning system , 2001 .

[10]  Xiangqian Jiang,et al.  Multisensor data fusion in dimensional metrology , 2009 .

[11]  Greg Welch,et al.  An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.

[12]  V. H. Chan,et al.  A multi-sensor approach to automating co-ordinate measuring machine-based reverse engineering , 2001 .

[13]  Feipeng Da,et al.  Fringe image processing based on structured light series , 2009, International Conference on Optical Instruments and Technology.

[14]  Wolfgang Osten,et al.  Model-based, active inspection of three-dimensional objects using a multi-sensor measurement system , 2013, Optical Metrology.

[15]  Robert Sitnik,et al.  The hybrid contact–optical coordinate measuring system , 2011 .

[16]  Bernd Jähne,et al.  A Physical Model of Time-of-Flight 3D Imaging Systems, Including Suppression of Ambient Light , 2009, Dyn3D.

[17]  Craig M. Shakarji,et al.  Least-Squares Fitting Algorithms of the NIST Algorithm Testing System , 1998, Journal of research of the National Institute of Standards and Technology.

[18]  Alain Delchambre,et al.  Robust Structured Light Pattern for Use with a Spatial Light Modulator in 3-D Endoscopy , 2013 .

[19]  K-C Fan A non-contact automatic measurement for free-form surface profiles , 1997 .

[20]  Chi Zhang,et al.  Real-time 3D shape inspection system of automotive parts based on structured light pattern , 2011 .

[21]  O. Faugeras Three-dimensional computer vision: a geometric viewpoint , 1993 .

[22]  G. Sansoni,et al.  Combination of a Vision System and a Coordinate Measuring Machine for the Reverse Engineering of Freeform Surfaces , 2001 .

[23]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Wei-Song Lin,et al.  Accuracy analysis on the estimation of camera parameters for active vision systems , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[25]  Jin Tao,et al.  A 3-D point sets registration method in reverse engineering , 2007, Comput. Ind. Eng..

[26]  Paul R. Cohen,et al.  Camera Calibration with Distortion Models and Accuracy Evaluation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[28]  Colin Bradley,et al.  A Complementary Sensor Approach to Reverse Engineering , 2001 .

[29]  K. B. Atkinson Introduction to Modern Photogrammetry. , 2003 .

[30]  Andreas Koschan,et al.  Multi-sensor Registration and Integration for Inspection and Navigation , 2004 .

[31]  R. W. Wedderburn Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method , 1974 .

[32]  A. Weckenmann,et al.  Probing Systems in Dimensional Metrology , 2004 .

[33]  Wang Jianguo,et al.  Complete 3D measurement in reverse engineering using a multi-probe system , 2005 .