Advertisement

The Scan View Planning Algorithm Based on the CAD Model

  • Yali Wang
  • Yun Hao
  • Ying Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)

Abstract

The surface of large-scale structure is large and complicated, and a lot of different positions under different pose are needed for the measuring system to get integrated three-dimensional surface data. The measurement accuracy could be improved by increasing sampling point number, but there are unavoidable problems like having larger data information and less efficient. When high measurement accuracy and high measurement efficiency are required, it is needed to plan the view point. In this paper, rectangle self-adaption sampling algorithm based on the CAD model is proposed. In this method, firstly, the least sampling points are determined by dividing up the large-scale structure surface area based on the camera measurement area. Secondly, the surface curvature of one view point is calculated and compared with camera incident angle. If all of the surface curvatures of the view point are less than the camera incident angle, the view point is saved. Otherwise, one view point is increased. Finally, the same operations are applied to all of the view points, and the final view point number is determined. Simulation and experimental results show that, the algorithm proposed can measure the surface of large-scale structure quickly and efficiently.

Keywords

Scan view planning Rectangle self-adaption sampling algorithm Surface curvatures Large-scale structure 

References

  1. 1.
    Tarabanis, K.A., Allen, P.K., Tsai, R.Y.: A survey of sensor planning in computer vision. IEEE Trans. Robot. Autom. 11(1), 86–104 (1995)Google Scholar
  2. 2.
    Smith, K.B., Zheng, Y.F.: Multi-laser displacement sensor used in accurate digitizing technique. J. Manuf. Sci. Eng. 116(4), 482–490 (1994)Google Scholar
  3. 3.
    Zhang, J., Wang, W.: Active Sensor Planning for Multiview Vision Tasks. Springer, Heidelberg (2008)Google Scholar
  4. 4.
    Gong, C.: Robotic measurement system: self-calibration, real-time error compensation and path planning. The University of Michigan (2000)Google Scholar
  5. 5.
    Poniatowska, M.: Free-form surface machining error compensation applying 3D CAD machining pattern model. Comput. Aided Des. 62, 227–235 (2015)Google Scholar
  6. 6.
    Poniatowska, M.: Deviation model based method of planning accuracy inspection of free-form surfaces using CMMs. Measurement 45(5), 927–937 (2012)Google Scholar
  7. 7.
    Soutootero, M., Murphy, I., Duchemin, C., et al.: European Inventory on validation of non-formal and informal learning 2014. Final synthesis report. Meas. Sci. Technol. 19(8), 817–822 (2014)Google Scholar
  8. 8.
    Lin, J., Shi, Z., Pan, C., et al.: Profile error evaluation of large gears based on NURBS surface fitting. Chin. J. Sci. Instrum. 37(3), 533–539 (2016)Google Scholar
  9. 9.
    Gao, G.: Determine sample points and probe path for sculptured surface in CMM environment. J. Xian Jiaotong Univ. (7), 57–63 (1996)Google Scholar
  10. 10.
    Lai, X.M., Huang, T., Chen, G.L.: Adaptive sampling of digitizing for the free form surface. J. Shanghai Jiaotong Univ. 33(7), 837–841 (1999)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Zhonghuan Information College Tianjin University of TechnologyTianjinChina

Personalised recommendations