DOI QR코드

DOI QR Code

Real-time Polygon Generation and Texture Mapping for Tele-operation using 3D Point Cloud Data

원격 작업을 위한 3 차원 점군 데이터기반의 실시간 폴리곤 생성 및 텍스처 맵핑 기법

  • Received : 2013.04.12
  • Accepted : 2013.09.02
  • Published : 2013.10.01

Abstract

In this paper, real-time polygon generation algorithm of 3D point cloud data and texture mapping for tele-operation is proposed. In a tele-operation, it is essential to provide more highly realistic visual information to a tele-operator. By using 3D point cloud data, the tele-operator can observe the working environment from various view point with a reconstructed 3D environment. However, there are huge empty space in 3D point cloud data, since there is no environmental information among the points. This empty space is not suitable for an environmental information. Therefore, real-time polygon generation algorithm of 3D point cloud data and texture mapping is presented to provide more highly realistic visual information to the tele-operator. The 3D environment reconstructed from the 3D point cloud data with texture mapped polygons is the crucial part of the tele-operation.

References

  1. ROS, "PR2," http://www.ros.org/.
  2. PCL, "Point Cloud Library," http://pointclouds.org/.
  3. G. R. Jang, Y. D. Shin, J. S. Yoon, J. H. Bae, J. H. Park, and M. H. Baeg, "3D-based visual assistance system for teleoperation," ICCAS 2012, pp. 1998-2000, Oct. 2012.
  4. G. R. Jang, Y. D. Shin, J. S. Yoon, J. H. Park, and M. H. Baeg, "Grid-based real-time polygon generation algorithm of a 3D point cloud for a tele-operation," Proc. of 2013 28th ICROS Annual Conference (in Korean), pp. 387-388, May 2013.
  5. H. Edelsbrunner, "Three-dimensional alpha shapes," ACM Trans. on Graphics, vol. 13, no. 1, pp. 43-72, Jan. 1994. https://doi.org/10.1145/174462.156635
  6. F. Bernardini, "The ball-pivoting algorithm for surface reconstruction," IEEE Trans. on Visualization and Computer Graphics, vol. 5, no.4, pp. 349-359, Dec 1999. https://doi.org/10.1109/2945.817351
  7. Microsoft, "KINECT for windows," http://www.microsoft.com/ en-us/kinectforwindows/.
  8. Microsoft, "Direct3D11," http://msdn.microsoft.com/enus/ library/windows/desktop/ff476342.aspx/.
  9. Y. D. Shin, G. R. Jang, J. H. Park, and M. H. Baeg, "Threedimensional point clouds data acquisition from a depth sensing camera," Proc. of KSPE 2011 Spring Conference (in Korean), pp. 99-100, Jun. 2011.
  10. F. Menna, F. Remondino, R. Battisti, and E. Nocerino, "Geometric investigation of a gaming active device," Proc. of SPIE, Prague, Czech Republic, pp. 19-22, Sep. 2011.
  11. Z. Zhang, "Flexible camera calibration by viewing a plane from unknown orientations," Proc. of the IEEE Int. Conf. on Computer Vision, vol. 1, pp. 666-673, Sep. 1999.
  12. Camera Calibration Toolbox for Matlab. Available online: http://www.vision.caltech.edu/bouguetj/calib_doc/.
  13. J. H. Park, Y. D. Shin, J. H. Bae, and M. H. Baeg, "Spatial uncertainty model for visual features using a $Kinect^{TM}$ sensor," Sensors, vol. 12, no. 7, pp. 8640-8662, Jun. 2012. https://doi.org/10.3390/s120708640
  14. G. R. Jang, Y. D. Shin, J. H. Park, and M. H. Baeg, "Efficient 3D point cloud rendering for tele-operator : real-time and large scale data," Proc. of 2012 27th ICROS Annual Conference (in Korean), pp. 382-383, Apr. 2012.

Cited by

  1. Optimal Depth Calibration for KinectTM Sensors via an Experimental Design Method vol.21, pp.11, 2015, https://doi.org/10.5302/J.ICROS.2015.15.0130