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Vol. 3 No. 01 (2022)

Articles

Targetless Coregistration of Terrestrial Laser Scanning Point Clouds Using a Multi Surrounding Scan Image-Based Technique

https://doi.org/10.38094/jastt301119

Published 2022-03-02

  • Bashar Alsadik

Bashar Alsadik
Department of Earth Observation Science, ITC Faculty, University of Twente, The Netherlands

Abstract

The coregistration of terrestrial laser point clouds is widely investigated where different techniques are presented to solve this problem. The techniques are divided either as target-based or targetless approaches for coarse and fine coregistration. The targetless approach is more challenging since no physical reference targets are placed in the field during the scanning. Mainly, targetless methods are image-based and they are applied through projecting the point clouds back to the scanning stations. The projected 360 point cloud images are normally in the form of panoramic images utilizing either intensity or RGB values, and an image matching is followed to align the scan stations together. However, the point cloud coregistration is still a challenge since ICP like methods are applicable for fine registration. Furthermore, image-based approaches are restricted when there is: a limited overlap between point clouds, no RGB data accompanied to intensity values, and unstructured scanned objects in the point clouds. Therefore, we present in this paper the concept of a multi surrounding scan MSS image-based approach to overcome the difficulty to register point clouds in challenging cases. The multi surrounding scan approach means to create multi-perspective images per laser scan point cloud. These multi-perspective images will offer different viewpoints per scan station to overcome the viewpoint distortion that causes the failure of the image matching in challenging situations. Two experimental tests are applied using point clouds collected in Enschede city and the published 3D toolkit data set in Bremen city. The experiments showed a successful coregistration approach even in challenging settings with different constellations.

Keywords

Terrestrial Laser Scanning, Point Cloud, Coregistration, Equirectangular Image, Perspective Image

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References

  1. Yang Chen and G. Medioni, "Object modelling by registration of multiple range images," Image and vision computing, vol. 10, no. 3, pp. 145-155, 1992.
  2. P. J. Besl and N. D. McKay, "A Method for Registration of 3-D Shapes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, no. 2, pp. 239-256, 1992, doi: 10.1109/34.121791.
  3. Sayyad Shahzad and M. Wiggenhagen, "Co-registration of terrestrial laser scans and close range digital images using scale invariant features," 2010.
  4. A. Swart, J. Broere, R. Veltkamp, and R. Tan, "Refined non-rigid registration of a panoramic image sequence to a LiDAR point cloud," presented at the Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis, Munich, Germany, 2011.
  5. P. W. Theiler, J. D. Wegner, and K. Schindler, "Markerless point cloud registration with keypoint-based 4-points congruent sets," ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., vol. II-5/W2, pp. 283-288, 2013, doi: 10.5194/isprsannals-II-5-W2-283-2013.
  6. K.-L. Low, "Linear least-squares optimization for point-to-plane icp surface registration," Chapel Hill, University of North Carolina, vol. 4, 2004.
  7. A. F. Habib, M. S. Ghanma, and M. Tait, "Integration of lidar and photogrammetry for close range applications.," presented at the Proceedings of the ISPRS Geo-Imagery Bridging Continents, 2004.
  8. M. Alshawa, "ICL: Iterative Closest Line a Novel Point Cloud Registration Algorithm Based on Linear Features," in ISPRS 2nd summer school in Ljubljana, Slovenia, 2007-07 2007, pp. 1-6. Available:https://halshs.archives-ouvertes.fr/halshs-00280659.
  9. M. Alshawa, "ICL: Iterative Closest Line a Novel Point Cloud Registration Algorithm Based on Linear Features," presented at the ISPRS 2nd summer school in, Ljubljana,Slovenia., 2007.
  10. A. Thapa, S. Pu, and M. Gerke, "Semantic Feature Based Registration of Terrestrial Point Clouds," in Laserscanning '09 Commission III, WG 2, Paris, France, M. P.-D. F. Bretar, G. Vosselman, Ed., 2009, vol. XXXVIII-3/W8, 2009: ISPRS Archives, pp. 230-235
  11. W. Changchang, B. Clipp, L. Xiaowei, J. M. Frahm, and M. Pollefeys, "3D model matching with Viewpoint-Invariant Patches (VIP)," in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, 23-28 June 2008 2008, pp. 1-8, doi: 10.1109/CVPR.2008.4587501.
  12. R. Wang, F. P. Ferrie, and J. Macfarlane, "Automatic registration of mobile LiDAR and spherical panoramas," in 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 16-21 June 2012 2012, pp. 33-40, doi: 10.1109/CVPRW.2012.6238912.
  13. J. Böhm and S. Becker, "Automatic marker-free registration of terrestrial laser scans using reflectance," Proceedings of 8th Conference on Optical 3D Measurement Techniques, Zurich, Switzerland, pp. 338-344, 2007.
  14. H. Houshiar, J. Elseberg, D. Borrmann, and A. Nüchter, "A study of projections for key point based registration of panoramic terrestrial 3D laser scan," Geo-spatial Information Science, vol. 18, no. 1, pp. 11-31, 2015/01/02 2015, doi: 10.1080/10095020.2015.1017913.
  15. W.-C. Chang and V.-T. Pham, "3-D Point Cloud Registration Using Convolutional Neural Networks," Applied Sciences, vol. 9, no. 16, p. 3273, 2019.
  16. Y. Wang and J. Solomon, "Deep Closest Point: Learning Representations for Point Cloud Registration," in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 27 Oct.-2 Nov. 2019 2019, pp. 3522-3531, doi: 10.1109/ICCV.2019.00362.
  17. C. Thomson. "Improve point cloud registration with targetless scanning." Vercator. https://info.vercator.com/blog/improve-point-cloud-registration-with-targetless-scanning (accessed April 1st, 2020).
  18. J. P. Snyder, "Map projections: A working manual," in "Professional Paper," Washington, D.C., Report 1395, 1987. [Online]. Available: http://pubs.er.usgs.gov/publication/pp1395
  19. "Street View." Google Maps. https://www.google.com/streetview/ (accessed March 9th, 2019).
  20. "Equirectangular projection." Wikipedia. https://en.wikipedia.org/wiki/Equirectangular_projection (accessed March 3rd, 2020).
  21. T. Luhmann, Robson, S., Kyle, S. and Boehm, J., Close-range photogrammetry and 3d imaging, 2nd ed. Berlin: De Gruyter (De Gruyter textbook), 2014.
  22. N. S. Mutha. "How to map Equirectangular projection to Rectilinear projection." http://blog.nitishmutha.com/equirectangular/360degree/2017/06/12/How-to-project-Equirectangular-image-to-rectilinear-view.html (accessed March 3rd, 2019).
  23. "Gnomonic projection." Wikiwand. https://www.wikiwand.com/en/Gnomonic_projection (accessed April 11th, 2020).
  24. E. Weisstein. "Gnomonic Projection." Wolfram Mathworld. https://mathworld.wolfram.com/GnomonicProjection.html (accessed March 3rd, 2020).
  25. Agisoft. "AgiSoft Metashape." http://www.agisoft.com/downloads/installer/ (accessed.
  26. D. Borrmann and A. Nüchter. "Robotic 3D Scan Repository." http://kos.informatik.uni-osnabrueck.de/3Dscans/ (accessed December 15th, 2017).

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