IJAT Vol.12 No.3 pp. 328-338
doi: 10.20965/ijat.2018.p0328


Efficient Registration of Laser-Scanned Point Clouds of Bridges Using Linear Features

Hiroaki Date*1,†, Takahito Yokoyama*1, Satoshi Kanai*1, Yoshiro Hada*2, Manabu Nakao*3, and Toshiya Sugawara*4

*1Graduate School of Information Science and Technology, Hokkaido University
Kita 14, Nishi 9, Kita-ku, Sapporo 060-0814, Japan

Corresponding author

*2Fujitsu Laboratories Ltd., Atsugi, Japan

*3Fujitsu Ltd., Kawasaki, Japan

*4Docon Co., Ltd., Sapporo, Japan

October 19, 2017
January 5, 2018
Online released:
May 1, 2018
May 5, 2018
laser scanning, point cloud, registration, bridges, linear features

Efficient registration of point clouds from terrestrial laser scanners enables us to move from scanning to point cloud applications immediately. In this paper, a new efficient rough registration method of laser-scanned point clouds of bridges is proposed. Our method relies on straight-line edges as linear features, which often appear in many bridges. Efficient edge-line extraction and line-based registration methods are described in this paper. In our method, first, sampled regular point clouds based on the azimuth and elevation angles are created, and planar regions are extracted using the region growing on the regular point clouds. Then, straight lines of the edges of the planar regions are extracted as linear features. Next, vertical and horizontal line clusters are created according to the direction of the lines. To align the position and orientation of two point clouds, two corresponding nonparallel line pairs from line clusters are used. In the registration process, the RANSAC approach with a hash table of line pairs is used. In this process, the hash table is used for finding candidates of corresponding line pairs efficiently. Sampled points on the line pairs are used to align the line pairs, and occupied voxels and downsampled point clouds are used for efficient consensus calculation. The method is tested using three data sets of different types of bridges: a small steel bridge, a middle-size concrete bridge, and a high-pier concrete bridge. In our experiments, successful rates of our rough registration were 100%, and the processing time of rough registration for 19 point clouds was about 1 min.

Cite this article as:
H. Date, T. Yokoyama, S. Kanai, Y. Hada, M. Nakao, and T. Sugawara, “Efficient Registration of Laser-Scanned Point Clouds of Bridges Using Linear Features,” Int. J. Automation Technol., Vol.12, No.3, pp. 328-338, 2018.
Data files:
  1. [1] D. Akca, “Full automatic registration of laser scanner point clouds,” Optical 3-D Measurement Techniques VI, pp. 330-337, 2003.
  2. [2] P. J. Besl and N. D. McKay, “A Method for Registration of 3D Shapes,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.14, No.2, pp. 239-256, 1992.
  3. [3] S. Rusinkiewicz and M. Levoy, “Efficient Variants of the ICP Algorithm,” Proc. of 3rd Int. Conf. on 3-D Digital Imaging and Modeling, pp. 145-152, 2001.
  4. [4] Z. Kang, J. Li, L. Zhang, Q. Zhao, and S, Zlatanova, “Automatic Registration of Terrestrial Laser Scanning Point Clouds using Panoramic Reflectance Images,” Sensors, Vol.9, No.4, pp. 2621-2646, 2009.
  5. [5] A. Nüchter, S. Gutev, D. Borrmann, and J. Elseberg, “Skyline-based registration of 3D laser scans,” J. of Geo-spatial Information Science, Vol.14, No.2, pp. 85-90, 2011.
  6. [6] R. Yoshimura, H. Date, S. Kanai, R. Honma, K. Oda, and T. Ikeda, “Automatic Registration of MLS Point Clouds and SfM Meshes of Urban Area,” Geo-spatial Information Science, Vol.19, No.3, pp. 171-181, 2016.
  7. [7] B. Yang, Z. Dong, F. Liang, and Y. Liu, “Automatic registration of large-scale urban scene point clouds based on semantic feature points,” ISPRS J. of Photogrammetry and Remote Sensing, Vol.113, pp. 43-58, 2016.
  8. [8] H. Date, Y. Matsuyama, and S. Kanai, “Registration of Point Clouds of Large Scale Environments using Point Projection Images,” Proc. of the 15th Int. Conf. on Precision Engineering, No.D31, 2014.
  9. [9] R. B. Rusu, N. Blodow, and M. Beetz, “Fast point feature histograms (FPFH) for 3d registration,” Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 3212-3217, 2009.
  10. [10] D. Aiger, N. J. Mitra, and D. Cohen-Or, “4-Points Congruent Sets for Robust Surface Registration,” ACM Trans. on Graphics, Vol.27, No.3, pp. 1-10, 2008.
  11. [11] P. W. Theiler, J. D. Wegner, and K. Schindler, “Markerless point cloud registration with keypoint-based 4-points congruent sets,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.II-5/W2, pp. 283-288, 2013.
  12. [12] K. AL-Durgham, A. Habib, and E. Kwak, “RANSAC Approach for Automated Registration of Terrestrial Laser Scans using Linear Features,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.II-5/W2, pp. 13-18, 2013.
  13. [13] 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.
  14. [14] M. A. Fischler and R. C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM, Vol.24, No.6, pp. 381-395, 1981.
  15. [15] M. Poreba and F. Goulette, “A Robust Linear Feature-Based Procedure for Automated Registration of Point Clouds,” Sensors, Vol.15, pp. 1435-1457, 2015.
  16. [16] B. Kamgar-Parsi and B. Kamgar-Parsi, “Algorithms for Matching 3D Line Sets,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.26, No.5, pp. 582-593, 2004.
  17. [17] K. Pulli, “Multiview Registration for Large Data Set,” Proc. of Second Int. Conf. on 3-D Digital Imaging and Modeling, pp. 160-168, 1999.
  18. [18] G. Vosselman, “Advanced Point Cloud Processing,” Photogrammetric Week, pp. 137-146, 2009.
  19. [19] R. Miyazaki, M. Yamamoto, and K. Harada, “Line-Based Planar Structure Extraction from a Point Cloud with an Anisotropic Distribution,” Int. J. Automation Technol., Vol.11, No.4, pp. 657-665, 2017.
  20. [20] F. Moosmann, O. Pink, and C, Stiller, “Segmentation of 3D Lidar Data in non-flat Urban Environments using a Local Convexity Criterion,” Proc. of IEEE Intelligent Vehicles Symposium, pp. 215-220, 2009.
  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] H. Masuda and I. Tanaka, “As-Built 3D Modeling of Large Facilities Based on Interactive Feature Editing,” Computer-Aided Design and Applications, Vol.7, No.3, pp. 349-360, 2010.
  23. [23] J. Elseberg, D. Borrmann, and A. Nüchter, “One billion points in the cloud – an octree for efficient processing of 3D laser scans,” ISPRS J. of Photogrammetry and Remote Sensing, Vol.76, pp. 76-88, 2013.
  24. [24] P. H. S. Torr and A. Zisserman, “MLESAC: A New Robust Estimator with Application to Estimating Image Geometry,” Computer Vision and Image Understanding, Vol.78, pp. 138-156, 2000.

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Last updated on Aug. 16, 2018