IJAT Vol.10 No.2 pp. 163-171
doi: 10.20965/ijat.2016.p0163


Registration of Point-Clouds from Terrestrial and Portable Laser Scanners

Takuma Watanabe, Takeru Niwa, and Hiroshi Masuda

The University of Electro-Communications
1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan

Corresponding author,

October 12, 2015
February 3, 2016
Online released:
March 4, 2016
March 5, 2016
registration, 3D scanning, point-cloud
We proposed a registration method for aligning short-range point-clouds captured using a portable laser scanner (PLS) to a large-scale point-cloud captured using a terrestrial laser scanner (TLS). As a PLS covers a very limited region, it often fails to provide sufficient features for registration. In our method, the system analyzes large-scale point-clouds captured using a TLS and indicates candidate regions to be measured using a PLS. When the user measures a suggested region, the system aligns the captured short-range point-cloud to the large-scale point-cloud. Our experiments show that the registration method can adequately align point-clouds captured using a TLS and a PLS.
Cite this article as:
T. Watanabe, T. Niwa, and H. Masuda, “Registration of Point-Clouds from Terrestrial and Portable Laser Scanners,” Int. J. Automation Technol., Vol.10 No.2, pp. 163-171, 2016.
Data files:
  1. [1] P. J. Besl and N. D. Mckay, “A method for registration of 3-D shapes,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.14, pp. 239-256, Feb. 1992.
  2. [2] S. Rusinkiewicz and M. Levoy, “Efficent cariants of the ICP algorithm,” 3-D Digital Imaging and Modeling, Proc. Third Int. Coference on IEEE, pp. 145-152, 2001.
  3. [3] R. B. Rusu, N. Blodow, and M. Beetz, “Fast point feature histograms (FPFH) for 3D registration,” Robotics and Automation, ICRA’09, 2009 IEEE Int. Conf., pp. 3212–3217, May 2009.
  4. [4] H. Men, B. Gebre, and K. Pochiraju, “Color point cloud registration with 4D ICP algorithm,” Robotics and Autoation (ICRA), 2011 IEEE Int. Conf. on. IEEE, pp. 1511–1516, 2011.
  5. [5] J. Chen, X. Wu, M.Y. Wang, and X. Li, “3D shape modeling using a self-developed hand-held 3D laser scanner and an efficient HT-ICP point cloud registration algorithm,” Optics & Laser Technology, Vol.45, pp. 414–423, 2013.
  6. [6] N. J. Mitra, N. Gelfand, H. Pottmann, and L. Guibas, “Registration of point cloud data from a geometric optimization perspective,” Proc. 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing, pp. 22–31, July 2004.
  7. [7] I. Stamos and M. Leordean, “Automated feature-based range registration of urban scenes of large scale,” Computer Vision and Pattern Recognition, Proc. 2003 IEEE Computer Society Coference, Vol.2, pp. 555–561, June 2003.
  8. [8] J. L. Martinez, A. J. Reina, J. Morales, A. Mandow, and A. J. Garcia-Cerezo, “Using multicore processors to parallelize 3D point cloud registration with the Coarse Binary Cubes method,” Mechatronics (ICM), 2013 IEEE Int. Conf., pp. 335–340, Feb. 2013.
  9. [9] J. Liu, G. Geng, and M. Zhou, “Sequence Iterative 3D Registration of Multi-view Point Cloud for Cultural Site Scenes,” Computer Science & Service System (CSSS), 2012 Int. Conf. on. IEEE, pp. 1923–1927, Aug. 2012.
  10. [10] L. Jun, L. Wei, D. Donglai, and S. Qiang, “Point cloud registration algorithm based on NDT with variable size voxel,” Control Conf., 2015 34th Chinese, IEEE, pp. 3707–3712, July 2015.
  11. [11] J. Jiang, J. Cheng, and X. Chen, “Registration for 3-D point cloud using angular-invariant feature,” Neurocomputing, Vol.72, pp. 3839–3844, Oct. 2009.
  12. [12] T. T. Tran, V. T. Cao, and D. Laurendeau, “3D point cloud registration based on the vector field representation,” Pattern Recognition (ACPR), 2013 2nd IAPR Asian Conf. on. IEEE, pp. 491–495, Nov. 2013.
  13. [13] A. Makadia, A. Patterson, and K. Dainiilidis, “Fully automatic registration of 3D point clouds,” Computer Vision and Pattern Recognition, Vol.1, pp. 1297–1304, June 2006.
  14. [14] J. Y. Han, N. H. Perng, and H. J. Chen, “LiDAR point cloud registration by image detection technique,” Geoscience and Remote Sensing Letters, IEEE, Vol.10, No.4, pp. 746–750, Nov. 2012.
  15. [15] D. Grant, J. Bethel, and M. Crawford, “Point-to-plane registration of terrestrial laser scans,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol.72, pp. 16–26, Aug. 2012.
  16. [16] J. Xiao, B. Adler, J. Zhang, and H. Zhang, “Planar Segment Based Three-dimensional Point Cloud Registration in Outdoor Environments” Journal of Field Robotics, Vol.30, No.4, pp. 552–582, July 2013.
  17. [17] N. Li, P. Cheng, M. A. Sutton, and S. R. McNeill, “Three-dimensional point cloud registration by matching surface features with relaxation labeling method,” Experimental Mechanics, Vol.45, No.1, pp. 71–82, 2005.
  18. [18] B. Yang and Y. Zang, “Automated registration of dense terrestrial laser-scanning point clouds using curves,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol.95, pp. 109–121, Sept. 2014.
  19. [19] P. W. Theiler and K. Schindler, “Automatic registration of terrestrial laser scanner point clouds using natural planar surfaces,” ISPRS Ann. Photogramm. Remote Sensing and Spatial Information Sciences, Vol.1, pp. 173–178, 2012.
  20. [20] H. Masuda, T. Niwa, I. Tanaka, and R. Matsuoka, “Reconstruction of Polygonal Faces from Large-Scale Point-Clouds of Engineering Plants,” Computer-Aided Design and Applications, Vol.12, pp. 555–563, April 2015.
  21. [21] R. Schnabel, R. Wahl, and R. Klein, “Efficient RANSAC for Point-Cloud Shape Detection,” Computer Graphics Forum, Vol.26, No.2, pp. 214–226, 2007.
  22. [22] G. Lukacs, A. D. Marshall, and R. R. Martin, “Faithful Least-Squares Fitting of Spheres, Cylinders, Cones and Tori for Reliable Segmentation,” Proc., 5th European Conf. on Computer Vision, pp. 671–686, 1998.
  23. [23] H. Masuda and I. Tanaka, “Extraction of Surface Primitives from Noisy Large-Scale Point-Clouds,” Computer-Aided Design and Applications, Vol.6, No.3, pp. 387–398, 2009.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 19, 2024