Sensors 2013, 13(6), 7224-7249; doi:10.3390/s130607224

Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods

1,* email, 1email, 2email and 2email
Received: 5 March 2013; in revised form: 7 April 2013 / Accepted: 14 May 2013 / Published: 31 May 2013
(This article belongs to the Section Physical Sensors)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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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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.

AMA Style

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.

Chicago/Turabian Style

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.

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