Development of a mixed pixel filter for improved dimension estimation using AMCW laser scanner
Accurate dimension estimation is desired in many fields, but the traditional dimension estimation methods are time-consuming and labor-intensive. In the recent decades, 3D laser scanners have become popular for dimension estimation due to their high measurement speed and accuracy. Nonetheless, scan...
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Published in | ISPRS journal of photogrammetry and remote sensing Vol. 119; pp. 246 - 258 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.09.2016
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Subjects | |
Online Access | Get full text |
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Summary: | Accurate dimension estimation is desired in many fields, but the traditional dimension estimation methods are time-consuming and labor-intensive. In the recent decades, 3D laser scanners have become popular for dimension estimation due to their high measurement speed and accuracy. Nonetheless, scan data obtained by amplitude-modulated continuous-wave (AMCW) laser scanners suffer from erroneous data called mixed pixels, which can influence the accuracy of dimension estimation. This study develops a mixed pixel filter for improved dimension estimation using AMCW laser scanners. The distance measurement of mixed pixels is firstly formulated based on the working principle of laser scanners. Then, a mixed pixel filter that can minimize the classification errors between valid points and mixed pixels is developed. Validation experiments were conducted to verify the formulation of the distance measurement of mixed pixels and to examine the performance of the proposed mixed pixel filter. Experimental results show that, for a specimen with dimensions of 840mm×300mm, the overall errors of the dimensions estimated after applying the proposed filter are 1.9mm and 1.0mm for two different scanning resolutions, respectively. These errors are much smaller than the errors (4.8mm and 3.5mm) obtained by the scanner’s built-in filter. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0924-2716 1872-8235 |
DOI: | 10.1016/j.isprsjprs.2016.06.004 |