2019
DOI: 10.3390/s19040938
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An Improvement in the Identification of the Centres of Checkerboard Targets in Point Clouds Using Terrestrial Laser Scanning

Abstract: Measurement using terrestrial laser scanning is performed at several stations to measure an entire object. In order to obtain a complete and uniform point cloud, it is necessary to register each and every scan in one local or global coordinate system. One registration method is based on reference points—in this case, checkerboard targets. The aim of this research was to analyse the accuracy of checkerboard target identification and propose an algorithm to improve the accuracy of target centre identification, p… Show more

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Cited by 5 publications
(1 citation statement)
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“…As the scanning resolution increases, the point cloud data near the center of the target become more intensive, and then, the extraction accuracy of the conventional method is improved. As can be seen from Figure 14, the proposed algorithm has the largest error at a scanning resolution of 3.1 mm@10 m. Tis can be explained by the variability of beam refections, i.e., the distortion of intensity values in regions characterized by color transitions [55]. As shown in Figure 11, the intensity values of the point cloud are abnormal at the transition between the black and white colors of the target.…”
Section: Algorithm Evaluationmentioning
confidence: 92%
“…As the scanning resolution increases, the point cloud data near the center of the target become more intensive, and then, the extraction accuracy of the conventional method is improved. As can be seen from Figure 14, the proposed algorithm has the largest error at a scanning resolution of 3.1 mm@10 m. Tis can be explained by the variability of beam refections, i.e., the distortion of intensity values in regions characterized by color transitions [55]. As shown in Figure 11, the intensity values of the point cloud are abnormal at the transition between the black and white colors of the target.…”
Section: Algorithm Evaluationmentioning
confidence: 92%