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Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods
Department of Geomatics Engineering, the University of Calgary, 2500 University Drive N.W., Calgary, AB T2N 1N4, Canada
Department of Civil and Environmental Engineering, the University of Houston, N107 Engineering Building 1, Houston, TX 77204-4003, USA
* Author to whom correspondence should be addressed.
Received: 5 March 2013; in revised form: 7 April 2013 / Accepted: 14 May 2013 / Published: 31 May 2013
Abstract: Terrestrial laser scanners are sophisticated instruments that operate much like high-speed total stations. It has previously been shown that unmodelled systematic errors can exist in modern terrestrial laser scanners that deteriorate their geometric measurement precision and accuracy. Typically, signalised targets are used in point-based self-calibrations to identify and model the systematic errors. Although this method has proven its effectiveness, a large quantity of signalised targets is required and is therefore labour-intensive and limits its practicality. In recent years, feature-based self-calibration of aerial, mobile terrestrial, and static terrestrial laser scanning systems has been demonstrated. In this paper, the commonalities and differences between point-based and plane-based self-calibration (in terms of model identification and parameter correlation) are explored. The results of this research indicate that much of the knowledge from point-based self-calibration can be directly transferred to plane-based calibration and that the two calibration approaches are nearly equivalent. New network configurations, such as the inclusion of tilted scans, were also studied and prove to be an effective means for strengthening the self-calibration solution, and improved recoverability of the horizontal collimation axis error for hybrid scanners, which has always posed a challenge in the past.
Keywords: LiDAR; terrestrial laser scanners; calibration; accuracy; error analysis; quality assurance
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Cite This Article
MDPI and ACS Style
Chow, J.C.K.; Lichti, D.D.; Glennie, C.; Hartzell, P. Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods. Sensors 2013, 13, 7224-7249.
Chow JCK, Lichti DD, Glennie C, Hartzell P. Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods. Sensors. 2013; 13(6):7224-7249.
Chow, Jacky C.K.; Lichti, Derek D.; Glennie, Craig; Hartzell, Preston. 2013. "Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods." Sensors 13, no. 6: 7224-7249.