Advertisement

An automatic 3D registration method for rock mass point clouds based on plane detection and polygon matching

  • Liang Hu
  • Jun XiaoEmail author
  • Ying Wang
Original Article
  • 107 Downloads

Abstract

Point cloud registration is an essential step in the process of 3D reconstruction. Considering that the surface of rock mass is complex and mainly composed of planes, in this paper, we introduce a novel and automatic 3D registration method for rock mass point clouds based on plane detection and polygon matching. Firstly, planes are detected from rock mass point clouds by an efficient tripe-region growing method, and then, the corresponding polygons are calculated by concave hull method. Secondly, PCA-based polygon matching procedure is used for coarse registration. Finally, ICP method is applied to fine registration. The performance of this method was tested in different rock mass point clouds. Compared with the existing methods, the proposed method demonstrates a reliable and stable solution for accurately registering in rock mass scenes.

Keywords

Automatic registration Rock mass Plane detection Polygon matching 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61471338), Youth Innovation Promotion Association CAS (2015361), Key Research Program of Frontier Sciences CAS (QYZDY-SSW-SYS004), Beijing Nova Program (Z171100001117048), Beijing Science and Technology Project (Z181100003818019) and President Fund of UCAS.

Compliance with ethical standards

Conflict of interest

The authors certify that there is no conflict of interest with any organization for the present work.

References

  1. 1.
    Yan, W.Y., Shaker, A., El-Ashmawy, N.: Urban land cover classification using airborne lidar data: a review. Remote Sens. Environ. 158, 295–310 (2015)CrossRefGoogle Scholar
  2. 2.
    Abellán, A., Oppikofer, T., Jaboyedoff, M., Rosser, N.J., Lim, M., Lato, M.J.: Terrestrial laser scanning of rock slope instabilities. Earth Surf. Process. Landf. 39(1), 80–97 (2014)CrossRefGoogle Scholar
  3. 3.
    Lin, H., Jizhou Gao, Y., Zhou, G.L., Ye, M., Zhang, C., Liu, L., Yang, R.: Semantic decomposition and reconstruction of residential scenes from lidar data. ACM TOG 32(4), 66 (2013)Google Scholar
  4. 4.
    Mohammed, B., Ferdous, S.: 3d object recognition in cluttered scenes with local surface features: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2270–87 (2014)CrossRefGoogle Scholar
  5. 5.
    Xiao, J., Zhang, J., Adler, B., Zhang, H., Zhang, J.: Three-dimensional point cloud plane segmentation in both structured and unstructured environments. Robot. Auton. Syst. 61(12), 1641–1652 (2013)CrossRefGoogle Scholar
  6. 6.
    Montuori, A., Luzi, G., Stramondo, S., Casula, G., Bignami, C., Bonali, E., Bianchi, M.G., Crosetto, M.: Combined use of ground-based systems for cultural heritage conservation monitoring. In Geoscience and Remote Sensing Symposium, pp. 4086–4089 (2014)Google Scholar
  7. 7.
    Diez, Y., Roure, F., Lladó, X., Salvi, J.: A qualitative review on 3d coarse registration methods. ACM CSUR 47(3), 45 (2015)Google Scholar
  8. 8.
    Vöge, M., Lato, M.J., Diederichs, M.S.: Automated rockmass discontinuity mapping from 3-dimensional surface data. Eng. Geol. 164, 155–162 (2013)CrossRefGoogle Scholar
  9. 9.
    Lato, M.J., Vöge, M.: Automated mapping of rock discontinuities in 3d lidar and photogrammetry models. Int. J. Rock Mech. Min. Sci. 54, 150–158 (2012)CrossRefGoogle Scholar
  10. 10.
    Tam, G.K.L., Cheng, Z.-Q., Lai, Y.-K., Langbein, F.C., Liu, Y., Marshall, D., Martin, R.R., Sun, X.-F., Rosin, P.L.: Registration of 3d point clouds and meshes: a survey from rigid to nonrigid. IEEE Trans. Vis. Comput. Graph. 19(7), 1199–1217 (2013)CrossRefGoogle Scholar
  11. 11.
    Priest, S.D.: Discontinuity Analysis for Rock Engineering. Springer, Berlin (2012)Google Scholar
  12. 12.
    Restrepo, M.I., Ulusoy, A.O., Mundy, J.L.: Evaluation of feature-based 3-d registration of probabilistic volumetric scenes. ISPRS J. Photogramm. Remote Sens. 98, 1–18 (2014)CrossRefGoogle Scholar
  13. 13.
    Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (fpfh) for 3d registration. In: IEEE International Conference on Robotics and Automation, 2009. ICRA’09. Citeseer, pp. 3212–3217 (2009)Google Scholar
  14. 14.
    Yang, M.Y., Cao, Y., McDonald, J.: Fusion of camera images and laser scans for wide baseline 3d scene alignment in urban environments. ISPRS J. Photogramm. Remote Sens. 66(6), S52–S61 (2011)CrossRefGoogle Scholar
  15. 15.
    Guo, Y., Sohel, F.A., Bennamoun, M., Wan, J., Lu, M.: Rops: a local feature descriptor for 3d rigid objects based on rotational projection statistics. In: 2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA). IEEE, pp. 1–6 (2013)Google Scholar
  16. 16.
    Mellado, N., Aiger, D., Mitra, N.J.: Super 4pcs fast global pointcloud registration via smart indexing. In Computer Graphics Forum, vol. 33. Wiley Online Library, pp. 205–215 (2014)Google Scholar
  17. 17.
    Mohamad, M., Ahmed, M.T., Rappaport, D., Greenspan, M.: Super generalized 4pcs for 3d registration. In: 2015 International Conference on 3D Vision (3DV). IEEE, pp. 598–606 (2015)Google Scholar
  18. 18.
    Yang, B., Zang, Y.: Automated registration of dense terrestrial laser-scanning point clouds using curves. ISPRS J. Photogramm. Remote Sens. 95, 109–121 (2014)CrossRefGoogle Scholar
  19. 19.
    Xian, Y., Xiao, J., Wang, Y.: A fast registration algorithm of rock point cloud based on spherical projection and feature extraction. Front. Comput. Sci. 13(1), 170–182 (2019)CrossRefGoogle Scholar
  20. 20.
    Elbaz, G., Avraham, T., Fischer, A.: 3d point cloud registration for localization using a deep neural network auto-encoder. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 2472–2481 (2017)Google Scholar
  21. 21.
    Besl, P.J., McKay, N.D.: Method for registration of 3-d shapes. In Sensor Fusion IV: Control Paradigms and Data Structures, vol. 1611, pp. 586–607. International Society for Optics and Photonics (1992)Google Scholar
  22. 22.
    Yang, J., Li, H., Jia, Y.: Go-icp: solving 3d registration efficiently and globally optimally. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1457–1464 (2013)Google Scholar
  23. 23.
    Pomerleau, F., Magnenat, S., Colas, F., Liu, M., Siegwart, R.: Tracking a depth camera: parameter exploration for fast icp. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3824–3829. IEEE (2011)Google Scholar
  24. 24.
    Borrmann, D., Elseberg, J., Lingemann, K., Nüchter, A.: The 3d hough transform for plane detection in point clouds: a review and a new accumulator design. 3D Res. 2(2), 3 (2011)CrossRefGoogle Scholar
  25. 25.
    Limberger, F.A., Oliveira, M.M.: Real-time detection of planar regions in unorganized point clouds. Pattern Recognit. 48(6), 2043–2053 (2015)CrossRefGoogle Scholar
  26. 26.
    Schnabel, R., Wahl, R., Klein, R.: Efficient ransac for point-cloud shape detection. In Computer graphics forum, vol. 26, pp. 214–226. Wiley Online Library (2007)Google Scholar
  27. 27.
    Oesau, S., Lafarge, F., Alliez, P.: Planar shape detection and regularization in tandem. In Computer Graphics Forum, vol. 35, pp. 203–215. Wiley Online Library (2016)Google Scholar
  28. 28.
    Poppinga, J., Vaskevicius, N., Birk, A., Pathak, K.: Fast plane detection and polygonalization in noisy 3d range images. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3378–3383 (2008)Google Scholar
  29. 29.
    Holz, D., Behnke, S.: Fast range image segmentation and smoothing using approximate surface reconstruction and region growing. In: International Conference on Intelligent Autonomous Systems, pp. 61–73 (2012)Google Scholar
  30. 30.
    Xiao, J., Zhang, J., Zhang, J., Zhang, H.: Fast plane detection for slam from noisy range images in both structured and unstructured environments, pp. 1768–1773 (2011)Google Scholar
  31. 31.
    Wang, J., Garratt, M., Anavatti, S.: Dominant plane detection using a rgb-d camera for autonomous navigation. In: International Conference on Automation, Robotics and Applications, pp. 456–460 (2015)Google Scholar
  32. 32.
    Gomes, R.K., de Oliveira, L.P.L., Gonzaga Jr., L., Tognoli, F.M.W., Veronez, M.R., de Souza, M.K.: An algorithm for automatic detection and orientation estimation of planar structures in lidar-scanned outcrops. Comput. Geosci. 90, 170–178 (2016)CrossRefGoogle Scholar
  33. 33.
    Oesau, S., Lafarge, F., Alliez, P.: Planar shape detection and regularization in tandem. Comput. Graph. Forum 35(17), 2453–4 (2015)Google Scholar
  34. 34.
    Gigli, G., Mugnai, F., Leoni, L., Casagli, N.: Brief communication “analysis of deformations in historic urban areas using terrestrial laser scanning”. Nat. Hazard. Earth Syst. Sci. 9(6), 1759–1761 (2009)CrossRefGoogle Scholar
  35. 35.
    Lato, M., Kemeny, J., Harrap, R.M., Bevan, G.: Rock bench: establishing a common repository and standards for assessing rockmass characteristics using lidar and photogrammetry. Comput. Geosci. 50, 106–114 (2013)CrossRefGoogle Scholar
  36. 36.
    Leng, X., Xiao, J., Wang, Y.: A multi-scale plane-detection method based on the hough transform and region growing. Photogramm. Rec. 31(154), 166–192 (2016)CrossRefGoogle Scholar
  37. 37.
    Holz, D., Ichim, A.E., Tombari, F., Rusu, R.B., Behnke, S.: Registration with the point cloud library: a modular framework for aligning in 3-d. IEEE Robot. Autom. Mag. 22(4), 110–124 (2015)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of Chinese Academy of ScienceBeijingChina

Personalised recommendations