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Remote Sens. 2019, 11(7), 845; https://doi.org/10.3390/rs11070845

Decreasing the Uncertainty of the Target Center Estimation at Terrestrial Laser Scanning by Choosing the Best Algorithm and by Improving the Target Design

Institute of Geodesy and Geoinformation, University of Bonn, 53115 Bonn, Germany
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Received: 7 March 2019 / Revised: 4 April 2019 / Accepted: 6 April 2019 / Published: 8 April 2019
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Abstract

During the registration and georeferencing of terrestrial laser scans, it is common to use targets to mark discrete points. To improve the accuracy of the registration, the uncertainties of the target center estimation (TCE) have to be minimized. The present study examines different factors influencing the precision of the TCE. Here, the focus is on the algorithm and the target design. It is determined that, in general, the uncertainties of the TCE are much smaller than those indicated by the manufacturers. By comparing different algorithms for the first time, it was possible to clearly determine that an algorithm using image correlations yields the smallest standard deviations for the TCE. A comparison of different target designs could not identify an ideal commercially available target. For this reason, a new target, the BOTA8 (BOnn TArget with 8-fold pattern) was developed, which leads to smaller standard deviations than the previous targets. By choosing the best algorithm and improving the target design, standard deviations of 0.5 mm in distance direction and 1.2 arcsec in angular direction for a scan distance up to 100 m were achieved with the laser scanner Leica ScanStation P20. The uncertainties could be reduced by several millimetres and angular seconds compared to the manufacturer’s targets and software. View Full-Text
Keywords: terrestrial laser scanning; target center estimation; target design terrestrial laser scanning; target center estimation; target design
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Janßen, J.; Medic, T.; Kuhlmann, H.; Holst, C. Decreasing the Uncertainty of the Target Center Estimation at Terrestrial Laser Scanning by Choosing the Best Algorithm and by Improving the Target Design. Remote Sens. 2019, 11, 845.

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