Automated and Frequent Calibration of a Robot Manipulator-mounted IR Range Camera for Steel Bridge Maintenance

  • Andrew Wing Keung ToEmail author
  • Gavin Paul
  • David Rushton-Smith
  • Dikai Liu
  • Gamini Dissanayake
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 92)


This paper presents an approach to perform frequent hand-eye calibration of an Infrared (IR) range camera mounted to the end-effector of a robot manipulator in a field environment. A set of three reflector discs arranged in a structured pattern is attached to the robot platform to provide high contrast image features with corresponding range readings for accurate calculation of the camera-to-robot base transform. Using this approach the hand-eye transform between the IR range camera and robot end-effector can be determined by considering the robot manipulator model. Experimental results show that a structured lighting-based IR range camera can be reliably hand-eye calibrated to a six DOF robot manipulator using the presented automated approach. Once calibrated, the IR range camera can be positioned with the manipulator so as to generate a high resolution geometric map of the surrounding environment suitable for performing the grit-blasting task.


Point Cloud Feature Point Robot Manipulator Calibration Plate Feature Point Extraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported by the Centre of Excellence for Autonomous Systems (CAS), the Roads and Maritime Services (RMS) and the University of Technology, Sydney.


  1. 1.
    K.S. Arun, T.S. Huang, S.D. Blostein, Least-squares fitting of two 3-d point sets, in IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 698–700 (1987)Google Scholar
  2. 2.
    P. Biber, S. Fleck, T. Duckett, 3d modeling of indoor environments for a robotic security guard, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 124–124 (2005)Google Scholar
  3. 3.
    Y.C. Chang, Y. Yamamoto, Dynamic decision making of mobile robot under obstructed environment, in International Conference on Intelligent Robots and Systems, pp. 4091–4096 (2006)Google Scholar
  4. 4.
    M. Dinham, G. Fang, A low cost hand-eye calibration method for arc welding robots, in International Conference on Robotics and Biomimetics (ROBIO), pp. 1889–1893 (2009)Google Scholar
  5. 5.
    M. Dinham, G. Fang, Low cost simultaneous calibration of a stereo vision system and a welding robot, in IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1452–1456 (2010)Google Scholar
  6. 6.
    C.S. Gatla, R. Lumia, J. Wood, G. Starr, Calibrating pan-tilt cameras in robot hand-eye systems using a single point, in IEEE International Conference on Robotics and Automation, 2007, pp. 3186–3191 (2007)Google Scholar
  7. 7.
    W. Gander, G.H. Golub, R. Strebel, Strebel least squares fitting of circles and ellipses. BIT Numer. Math. 34(4), 558–578Google Scholar
  8. 8.
    J. Ji, L. Sun, L. Yu, A new pose measuring and kinematics calibrating method for manipulators, in IEEE International Conference on Robotics and Automation, 2007, pp. 4925–4930 (2007)Google Scholar
  9. 9.
    H. Malm, A. Heyden, Simplified intrinsic camera calibration and hand-eye calibration for robot vision, in International Conference on Intelligent Robots and Systems (IROS), vol. 1, pp. 1037–1043 (2003)Google Scholar
  10. 10.
    S. May, D. Droeschel, S. Fuchs, D. Holz, A. Nuchter, Robust 3d-mapping with time-of-flight cameras, in International Conference on Intelligent Robots and Systems (IROS), pp. 1673–1678 (2009)Google Scholar
  11. 11.
    A. Omodei, G. Legnani, R. Adamini, Three methodologies for the calibration of industrial manipulators: experimental results on a scara robot. J. Robot. Syst. 17(6), 291–307 (2000)CrossRefzbMATHGoogle Scholar
  12. 12.
    G. Paul, N. Kirchner, D. Gamini, D.K. Liu, An effective exploration approach to simultaneous mapping and surface material-type identification of complex three-dimensional environments. J. Field Robot. 26(11–12), 915–933 (2009)CrossRefzbMATHGoogle Scholar
  13. 13.
    G. Paul, S. Webb, D.K. Liu, G. Dissanayake, A robotic system for steel bridge maintenance: field testing, in Australasian Conference on Robotics and Automation, pp. 1–8 (2010)Google Scholar
  14. 14.
    Q. Shi, N. Xi, Y. Chen, W. Sheng, Registration of point clouds for 3d shape inspection, in International Conference on Intelligent Robots and Systems, pp. 235–240 (2006)Google Scholar
  15. 15.
    K.H. Strobl, G. Hirzinger, More accurate camera and hand-eye calibrations with unknown grid pattern dimensions, in IEEE International Conference on Robotics and Automation (ICRA), pp. 1398–1405 (2008)Google Scholar
  16. 16.
    M.Y. Yang, W. Forstner, Plane detection in point cloud data. Technical Report, Department of Photogrammetry, pp. 1–16 (2010)Google Scholar
  17. 17.
    Q. Zhang, R. Pless, Extrinsic calibration of a camera and laser range finder (improves camera calibration), in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 3, pp. 2301–2306 (2004)Google Scholar
  18. 18.
    Z. Zhang, H. Ma, S. Meng, S. Zhang, T. Guo, J. Chen, X. Hu, New calibration method of fringe projection imaging system, in International Conference on Electronics and Optoelectronics (ICEOE), 2011, vol. 1, pp. 88–91 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Andrew Wing Keung To
    • 1
    Email author
  • Gavin Paul
    • 1
  • David Rushton-Smith
    • 1
  • Dikai Liu
    • 1
  • Gamini Dissanayake
    • 1
  1. 1.University of TechnologySydneyAustralia

Personalised recommendations