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Calibrating inter-target distances in a network with high accuracy using a terrestrial laser scanner

Published

Author(s)

Balasubramanian Muralikrishnan, Katharine Shilling, Vincent Lee, Mary Gregg, Dennis Everett

Abstract

Self-calibration, as applied to terrestrial laser scanners (TLS), is the process of estimating error model parameters from measurements made on a network of stationary targets from multiple positions of the TLS through a least-squares bundle adjustment. While the typical objective of self-calibration is to improve the accuracy of the TLS, we employ the technique to calibrate inter-target distances in the network with low uncertainty. We demonstrate the technique using two different TLSs, a short-range high accuracy and a long-range low accuracy TLS, in a 11 m × 6 m × 3 m space with 20 targets and four TLS positions. We obtain inter-target distance uncertainties of under 0.2 mm (k = 2) for the short-range TLS even when it was fully uncompensated (i.e., internal error model turned off) and under 0.35 mm (k = 2) for the long-range TLS. These uncertainties are substantially smaller, sometimes even an order of magnitude smaller, than the errors typically expected from a TLS measurement of the same targets from a single position. We demonstrate the validity of our uncertainty estimates using a laser tracker as a reference. The calibrated network of targets can subsequently be used to the evaluate the performance of the same TLS or other measurement instruments whose accuracy specifications are larger (typically, by at least a factor of four) than the uncertainties obtained by this method.
Citation
Measurement Science & Technology

Keywords

bundle-adjustment, checkerboard/contrast targets, network method, performance evaluation, self-calibration, terrestrial laser scanner, uncertainty

Citation

Muralikrishnan, B. , Shilling, K. , Lee, V. , Gregg, M. and Everett, D. (2026), Calibrating inter-target distances in a network with high accuracy using a terrestrial laser scanner, Measurement Science & Technology, [online], https://doi.org/10.1016/j.precisioneng.2026.04.029, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=961104 (Accessed September 19, 2026)
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Created May 4, 2026, Updated September 18, 2026
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