New Approach for Low-Cost TLS Target Measurement
Publication: Journal of Surveying Engineering
Volume 145, Issue 3
Abstract
The registration and calibration of data captured with terrestrial laser scanner (TLS) instruments can be effectively achieved using signalized targets comprising components of both high and low reflectivity, so-called contrast targets. For projects requiring tens or even hundreds of such targets, the cost of manufacturer-constructed targets can be prohibitive. Moreover, the details of proprietary target center coordinate measurement algorithms are often not available to users. This paper reports on the design of a low-cost contrast target using readily available materials and an accompanying center measurement algorithm. Their compatibility with real terrestrial laser scanner data was extensively tested on six different instruments: two FARO Focus three-dimensional (3D) scanners, a Leica HDS6100, a Leica P40, a RIEGL VZ-400, and a Zoller+Fröhlich Imager 5010. Repeatability was examined as a function of range, incidence angle, sampling resolution, and target contrast. Performance in system self-calibration and from independent accuracy assessment is also reported. The results demonstrate compatibility for all five scanners. However, all data sets except the FARO Focus 3D require exclusion of observations made at high incidence angles in order to prevent range biases. Results also demonstrate that the spectral reflectivity of the target components is critical to ensure high contrast between target components, and therefore high-quality target center coordinate measurements.
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Acknowledgments
The authors acknowledge funding from the Natural Sciences and Engineering Research Council and the Canada Foundation for Innovation. Spatial Technologies, Calgary, Canada, is gratefully acknowledged for the use of the Leica P40. Dr. Peter Dawson from the University of Calgary is thanked for the use of the Z+F Imager 5010.
References
Ahn, S. J., H. J. Warnecke, and R. Kotowski. 1999. “Systematic geometric image measurement errors of circular object targets: Mathematical formulation and correction.” Photogramm. Rec. 16 (93): 485–502. https://doi.org/10.1111/0031-868X.00138.
Chow, J., A. Ebeling, and B. Teskey. 2010. “Low cost artificial planar target measurement techniques for terrestrial laser scanning.” In Proc., FIG Congress 2010. Copenhagen, Denmark: International Federation of Surveyors.
Förstner, W., and B. Wrobel. 2004. “Mathematical concepts in photogrammetry.” In Manual of photogrammetry, edited by J. C. McGlome, E. M. Mikhail, and J. Bethel, 15–180. 5th ed. Bethesda, MD: American Society for Photogrammetry and Remote Sensing (ASPRS).
Ge, X., and T. Wunderlich. 2015. “Target identification in terrestrial laser scanning.” Surv. Rev. 47 (341): 129–140. https://doi.org/10.1179/1752270614Y.0000000097.
Horn, B. K. P. 1987. “Closed-form solution of absolute orientation using unit quaternions.” J. Opt. Soc. Am. A. 4 (4): 629–642. https://doi.org/10.1364/JOSAA.4.000629.
Hullo, J.-F., G. Thibault, C. Boucheny, F. Dory, and A. Mas. 2015. “Multi-sensor as-built models of complex industrial architectures.” Remote Sens. 7 (12): 16339–16362. https://doi.org/10.3390/rs71215827.
Liang, Y.-B., Q.-M. Zhan, E.-Z. Che, M.-W. Chen, and D.-L. Zhang. 2014. “Automatic registration of terrestrial laser scanning data using precisely located artificial planar targets.” IEEE Geosci. Remote Sens. Lett. 11 (1): 69–73. https://doi.org/10.1109/LGRS.2013.2246134.
Lichti, D., S. Brustle, and J. Franke. 2007. “Self calibration and analysis of the Surphaser 25HS 3D scanner.” In Proc., FIG Working Week 2007. Copenhagen, Denmark: International Federation of Surveyors.
Lichti, D. D. 2007. “Error modelling, calibration and analysis of an AM–CW terrestrial laser scanner system.” ISPRS J. Photogramm. Remote Sens. 61 (5): 307–324. https://doi.org/10.1016/j.isprsjprs.2006.10.004.
Luhmann, T. 2014. “Eccentricity in images of circular and spherical targets and its impact on spatial intersection.” Photogramm. Rec. 29 (148): 417–433. https://doi.org/10.1111/phor.12084.
Muralikrishnan, B., et al. 2015. “Volumetric performance evaluation of a laser scanner based on geometric error model.” Precis. Eng. 40 (Apr): 139–150. https://doi.org/10.1016/j.precisioneng.2014.11.002.
Rachakonda, P., B. Muralikrishnan, D. Sawyer, and L. Wang. 2017. “Method to determine the center of contrast targets from terrestrial laser scanner data.” In Proc., 32nd ASPE Annual Meeting, 223–229. Raleigh, NC: American Society for Precision Engineering.
Reshetyuk, Y. 2010. “A unified approach to self-calibration of terrestrial laser scanners.” ISPRS J. Photogramm. Remote Sens. 65 (5): 445–456. https://doi.org/10.1016/j.isprsjprs.2010.05.005.
Shakarji, C. M. 1998. “Least-squares fitting algorithms of the NIST algorithm testing system.” J. Res. Nat. Inst. Stand. Technol. 103 (6): 633–641. https://doi.org/10.6028/jres.103.043.
Soudarissanane, S., R. Lindenbergh, M. Menenti, and P. Teunissen. 2011. “Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points.” ISPRS J. Photogramm. Remote Sens. 66 (4): 389–399. https://doi.org/10.1016/j.isprsjprs.2011.01.005.
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© 2019 American Society of Civil Engineers.
History
Received: Sep 11, 2018
Accepted: Feb 13, 2019
Published online: Jun 12, 2019
Published in print: Aug 1, 2019
Discussion open until: Nov 12, 2019
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