Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Applied Geodesy

Editor-in-Chief: Kahmen, Heribert / Rizos, Chris


CiteScore 2018: 1.61

SCImago Journal Rank (SJR) 2018: 0.532
Source Normalized Impact per Paper (SNIP) 2018: 1.064

Online
ISSN
1862-9024
See all formats and pricing
More options …
Volume 13, Issue 3

Issues

Empirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners

Tomislav Medić
  • Corresponding author
  • Institute of Geodesy and Geoinformation, University of Bonn, Nussallee 17, 53115 Bonn, Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Christoph Holst / Jannik Janßen / Heiner Kuhlmann
Published Online: 2019-03-22 | DOI: https://doi.org/10.1515/jag-2018-0032

Abstract

The target-based point cloud registration and calibration of terrestrial laser scanners (TLSs) are mathematically modeled and solved by the least-squares adjustment. However, usual stochastic models are simplified to a large amount: They generally employ a single point measurement uncertainty based on the manufacturers’ specifications. This definition does not hold true for the target-based calibration and registration due to the fact that the target centroid is derived from multiple measurements and its uncertainty depends on the detection procedure as well. In this study, we empirically investigate the precision of the target centroid detection and define an empirical stochastic model in the form of look-up tables. Furthermore, we compare the usual stochastic model with the empirical stochastic model on several point cloud registration and TLS calibration experiments. There, we prove that the values of usual stochastic models are underestimated and incorrect, which can lead to multiple adverse effects such as biased results of the estimation procedures, a false a posteriori variance component analysis, false statistical testing, and false network design conclusions. In the end, we prove that some of the adverse effects can be mitigated by employing the a priori knowledge about the target centroid uncertainty behavior.

Keywords: terrestrial laser scanner; calibration; registration; stochastic model; target centroid detection

References

  • [1]

    W. Mukupa, G. W. Roberts, C. M. Hancock, and K. Al-Manasir, “A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures,” Surv. Rev., vol. 49, no. 353, pp. 99–116, 2017.Web of ScienceGoogle Scholar

  • [2]

    H. Yang, M. Omidalizarandi, X. Xu, and I. Neumann, “Terrestrial laser scanning technology for deformation monitoring and surface modeling of arch structures,” Compos. Struct., vol. 169, pp. 173–179, 2017.Web of ScienceCrossrefGoogle Scholar

  • [3]

    C. Holst, T. Medić, and H. Kuhlmann, “Dealing with systematic laser scanner errors due to misalignment at area-based deformation analyses,” J. Appl. Geod., vol. 12, no. 2, pp. 169–185, 2018.CrossrefWeb of ScienceGoogle Scholar

  • [4]

    M. Tsakiri, D. Lichti, and N. Pfeifer, “Terrestrial laser scanning for deformation,” 3rd IAG/12th FIG Symp., p. 10, 2006.Google Scholar

  • [5]

    J. C. K. Chow, W. F. Teskey, and J. W. Lovse, “In-situ Self-calibration of Terrestrial Laser Scanners and Deformation Analysis Using Both Signalized Targets and Intersection of Planes for Indoor Applications,” in 14th FIG Symposium on Deformation Measurements and Analysis, 2011.Google Scholar

  • [6]

    J. Pandžić, M. Pejić, B. Božić, and V. Erić, “Error model of direct georeferencing procedure of terrestrial laser scanning,” Autom. Constr., vol. 78, pp. 13–23, 2017.Web of ScienceCrossrefGoogle Scholar

  • [7]

    Z. Ji, M. Song, H. Guan, and Y. Yu, “Accurate and robust registration of high-speed railway viaduct point clouds using closing conditions and external geometric constraints,” ISPRS J. Photogramm. Remote Sens., vol. 106, pp. 55–67, 2015.CrossrefWeb of ScienceGoogle Scholar

  • [8]

    B. Becerik-Gerber, F. Jazizadeh, G. Kavulya, and G. Calis, “Assessment of target types and layouts in 3D laser scanning for registration accuracy,” Autom. Constr., vol. 20, no. 5, pp. 649–658, 2011.CrossrefWeb of ScienceGoogle Scholar

  • [9]

    L. Wang, B. Muralikrishnan, P. Rachakonda, and D. Sawyer, “Determining geometric error model parameters of a terrestrial laser scanner through two-face, length-consistency, and network methods,” Meas. Sci. Technol., vol. 28, no. 6, 2017.Web of ScienceGoogle Scholar

  • [10]

    B. Muralikrishnan, L. Wang, P. Rachakonda, and D. Sawyer, “Terrestrial laser scanner geometric error model parameter correlations in the Two-face, Length-consistency, and Network methods of self-calibration,” Precis. Eng., vol. 52, pp. 15–29, 2017.Web of ScienceGoogle Scholar

  • [11]

    M. A. Abbas, D. D. Lichti, A. K. Chong, H. Setan, and Z. Majid, “An on-site approach for the self-calibration of terrestrial laser scanner,” Meas. J. Int. Meas. Confed., vol. 52, no. 1, pp. 111-–123, 2014.Google Scholar

  • [12]

    Y. Reshetyuk, “A unified approach to self-calibration of terrestrial laser scanners,” ISPRS J. Photogramm. Remote Sens., vol. 65, no. 5, pp. 445–456, 2010.Web of ScienceCrossrefGoogle Scholar

  • [13]

    J. C. K. Chow, D. D. Lichti, C. Glennie, and P. Hartzell, “Improvements to and comparison of static terrestrial LiDAR self-calibration methods,” Sensors (Switzerland), vol. 13, no. 6, pp. 7224–7249, 2013.CrossrefGoogle Scholar

  • [14]

    T. Kersten, K. Mechelke, M. Lindstaedt, and H. Sternberg, “Geometric accuracy investigations of the latest terrestrial laser scanning systems,” FIG Work. Week, vol. June, pp. 1–16, 2008.Google Scholar

  • [15]

    M. Omidalizarandi and I. Neumann, “Comparison of Target- and Mutual Information Based Calibration of Terrestrial Laser Scanner and Digital Camera for Deformation Monitoring,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. 40, no. 1/W5, pp. 559–564, 2015.Google Scholar

  • [16]

    D. D. Lichti, “Error modelling, calibration and analysis of an AM-CW terrestrial laser scanner system,” ISPRS J. Photogramm. Remote Sens., vol. 61, no. 5, pp. 307–324, 2007.Web of ScienceCrossrefGoogle Scholar

  • [17]

    D. D. Lichti, “The impact of angle parameterisation on terrestrial laser scanner self-calibration,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, vol. 38, no. 3/W8, pp. 171–176, 2009.Google Scholar

  • [18]

    D. D. Lichti, “Terrestrial laser scanner self-calibration: Correlation sources and their mitigation,” ISPRS J. Photogramm. Remote Sens., vol. 65, no. 1, pp. 93–102, 2010.CrossrefWeb of ScienceGoogle Scholar

  • [19]

    M. A. Abbas et al., “Improvements to the accuracy of prototype ship models measurement method using terrestrial laser scanner,” Meas. J. Int. Meas. Confed., vol. 100, pp. 301–310, 2017.Google Scholar

  • [20]

    J. L. Lerma and D. García-San-Miguel, “Self-calibration of terrestrial laser scanners: selection of the best geometric additional parameters,” ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., vol. II-5, no. June, pp. 219–226, 2014.CrossrefGoogle Scholar

  • [21]

    T. Medić, C. Holst, and H. Kuhlmann, “Towards System Calibration of Panoramic Laser Scanners from a Single Station,” Sensors, vol. 17, no. 5, p. 1145, 2017.Web of ScienceCrossrefGoogle Scholar

  • [22]

    X. Ge, “Terrestrial Laser Scanning Technology from Calibration to Registration with Respect to Deformation Monitoring,” Technical University of Munich, 2016.Google Scholar

  • [23]

    S. Soudarissanane, R. Lindenbergh, M. Menenti, and P. Teunissen, “Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points,” ISPRS J. Photogramm. Remote Sens., vol. 66, no. 4, pp. 389–399, 2011.Web of ScienceCrossrefGoogle Scholar

  • [24]

    D. Akca, “Full Automatic Registration of Laser Scanner Point Clouds,” Opt. 3D Meas. Tech. IV, vol. I, pp. 330–337, 2003.Google Scholar

  • [25]

    C. Yi et al., “Automatic Detection of Cross-Shaped Targets for Laser Scan Registration,” IEEE Access, vol. 6, pp. 8483–8500, 2018.Web of ScienceCrossrefGoogle Scholar

  • [26]

    Y. Liang, Q. Zhan, E. Che, M. Chen, and D. Zhang, “Automatic Registration of Terrestrial Laser Scanning Data Using Precisely Located Artificial Planar Targets,” IEEE Geosci. Remote Sens. Lett., vol. 11, no. 1, pp. 69–73, 2014.CrossrefWeb of ScienceGoogle Scholar

  • [27]

    J. Chow, A. Ebeling, and T. Bill, “Low cost Artificial Planar Target Measurement Techniques for TLS,” FIG Congr. 2010 Facing Challenges – Build. Capacit., no. April 2010, p. 13, 2010.Google Scholar

  • [28]

    M. Omidalizarandi, J. Paffenholz, and I. Neumann, “Automatic and accurate passive target centroid detection for applications in engineering geodesy,” Surv. Rev., pp. 1–16, 2018.Google Scholar

  • [29]

    P. Rachakonda, B. Muralikrishnan, and D. Sawyer, “Metrological Evaluation of Contrast Target Center Algorithm for Terrestrial Laser Scanners,” Measurement, no. 134, pp. 15–24, 2018.Web of ScienceGoogle Scholar

  • [30]

    X. Ge and T. Wunderlich, “Target Identification in Terrestrial Laser Scanning,” Surv. Rev., vol. 47, pp. 129–140, 2014.Web of ScienceGoogle Scholar

  • [31]

    K. Kregar, D. Grigillo, and D. Kogoj, “High precision target determination from a point cloud,” ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., vol. II-5/W2, no. November, pp. 11–13, 2013.Google Scholar

  • [32]

    K. Kowalczyk and J. Rapinski, “Investigating the Error Sources in Reflectorless EDM,” J. Surv. Eng., vol. 140, no. 4, p. 6014002, 2014.CrossrefWeb of ScienceGoogle Scholar

  • [33]

    S. Soudarissanane, “The geometry of terrestrial laser scanning; identification of errors, modeling and mitigation of scanning geometry,” TU Delft, 2016.Google Scholar

  • [34]

    M. Zámecnikova, H. Neuner, S. Pegritz, and R. Sonnleitner, “Investigation on the influence of the incidence angle on the reflectorless distance measurement of a terrestrial laser scanner,” Vermessung Geoinf., no. 2+3, pp. 208–218, 2015.Google Scholar

  • [35]

    D. Wujanz, M. Burger, M. Mettenleiter, and F. Neitzel, “An intensity-based stochastic model for terrestrial laser scanners,” ISPRS J. Photogramm. Remote Sens., vol. 125, pp. 146–155, 2017.CrossrefWeb of ScienceGoogle Scholar

  • [36]

    T. Jurek, H. Kuhlmann, and C. Holst, “Impact of spatial correlations on the surface estimation based on terrestrial laser scanning,” J. Appl. Geod., vol. 11, no. 3, pp. 143–155, 2017.Web of ScienceGoogle Scholar

  • [37]

    E. W. Grafarend and F. Sanso, Optimization and Design of Geodetic Networks, 1st ed. Springer-Verlag Berlin Heidelberg, 1985.Google Scholar

  • [38]

    D. D. Lichti and J. Franke, “Self-Calibration of the iQsun 880 Laser Scanner,” in In Proceedings of the Optical 3-D 927 Measurement Techniques VII, Vienna, Austria, 2005, pp. 122–131.Google Scholar

  • [39]

    Y. Reshetyuk, “Self-calibration and direct georeferencing in terrestrial laser scanning,” KTH Stockholm, 2009.Google Scholar

  • [40]

    B. Muralikrishnan et al., “Volumetric performance evaluation of a laser scanner based on geometric error model,” Precis. Eng., vol. 40, pp. 139–150, 2015.CrossrefGoogle Scholar

  • [41]

    K.-R. Koch, Parameter Estimation and Hypothesis Testing in Linear Models, 2nd ed. New York: Springer, 1999.Google Scholar

  • [42]

    T. Krarup, J. Juhl, and K. Kubik, “Götterdämmerung over least squares adjustment,” in 14th Congress of ISPRS, 1980.Google Scholar

  • [43]

    A. Wieser and F. K. Brunner, “Short Static GPS Sessions: Robust Estimation Results,” GPS Solut., vol. 5, no. 3, pp. 70–79, 2002.CrossrefGoogle Scholar

  • [44]

    R. G. Lomax and D. L. Hans-Vaughn, An Introduction to Statistical Concepts, 3rd ed. New York: Taylor & Francis, 2012.Google Scholar

  • [45]

    E. Heinz, C. Holst, and H. Kuhlmann, “Erhöhung der räumlichen Auflösung oder Steigerung der Einzelpunktgenauigkeit beim Laserscanning – Analyse der Modellierungsgenauigkeit am Beispiel einer Ebene,” in Photogrammetrie Laserscanning Optische 3DMesstechnik – Beiträge der Oldenburger 3D-Tage, 2018, pp. 167–179.Google Scholar

  • [46]

    Leica, “Leica ScanStation P20 Industry ’ s Best Performing Ultra-High Speed Scanner,” Leica Scanstation P20 Datasheet, 2015.

  • [47]

    W. Schofield and M. Breach, Engineering Surveying, 6th ed. Oxford: Elsevier, 2007.Google Scholar

  • [48]

    Leica, “Leica HDS Check & Adjust – User Manual,” vol. 1, 2012.

  • [49]

    D. Garcia-San-Miguel and J. L. Lerma, “Geometric calibration of a terrestrial laser scanner with local additional parameters: An automatic strategy,” ISPRS J. Photogramm. Remote Sens., vol. 79, pp. 122–136, 2013.CrossrefWeb of ScienceGoogle Scholar

  • [50]

    W. F. Caspary, Concepts of network and deformation analysis, 3rd ed. Sidney, Australia: School of Geomatic Engineering, University of New South Wales, 2000.Google Scholar

About the article

Received: 2018-08-02

Accepted: 2019-03-06

Published Online: 2019-03-22

Published in Print: 2019-07-26


Citation Information: Journal of Applied Geodesy, Volume 13, Issue 3, Pages 179–197, ISSN (Online) 1862-9024, ISSN (Print) 1862-9016, DOI: https://doi.org/10.1515/jag-2018-0032.

Export Citation

© 2019 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Tomislav Medić, Heiner Kuhlmann, and Christoph Holst
Remote Sensing, 2019, Volume 11, Number 13, Page 1519

Comments (0)

Please log in or register to comment.
Log in