The impact of object properties and scan geometry on the quality of TLS data

Object surface Properties, range, and measuring trip time are main variables affecting the positional accuracy of the computed point clouds by the terrestrial Laser Scanner (TLS). In this research, Practical experiments were carried out by Faro focus premium TLS in order to investigate how does the variation of surface roughness and its reflectivity affect the positional accuracy of the measured scanner data at different scan angles and ranges. For this purpose, different materials that have distinct surface properties were conducted (glass, steel, wood, ekoplast, and total station (TS) sheet targets). Also, to examine the impact of the surface color, three of those selected materials have been painted with RGB color and black and white colors as well. About 54 scans were recorded during the experiment as all materials were scanned at three different scanning angles of (0˚, 30˚, and 60˚) and at ranges of 5 and 20 meters. The experiment's findings reveals that, at various incident angles, smooth surfaces have a greater impact on the accuracy of the scanned objects to create 3D point clouds than do rough surfaces. Furthermore, the total RMSEs in the point clouds position that measured from surface painted with red and black colors is greeter and higher than those measured from blue, green, and white colors painted surfaces. Interestingly, the total station target had never reflects the laser beam at all incident angles and ranges for class-1 laser beams. Additionally, the intensity of various materials varies. For example, the smooth materials steel and glass have varying degrees of accuracy because of their respective characteristics of the surface.

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Publication Details

Journal
ZANCO Journal of Pure and Applied Sciences
Published
2024-08-31
DOI
https://doi.org/10.21271/zjpas.36.4.8
Primary Topic
Gene expression and cancer classification
Type
article
Field-Weighted Citation Impact
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article

The impact of object properties and scan geometry on the quality of TLS data

Bakhtyar Ahmed Mala, Dleen Muhammed Salih Al-shrafany
ZANCO Journal of Pure and Applied Sciences
Gene expression and cancer classification
article

The impact of object properties and scan geometry on the quality of TLS data

Bakhtyar Ahmed Mala, Dleen Muhammed Salih Al-shrafany
article en

Abstract

Object surface Properties, range, and measuring trip time are main variables affecting the positional accuracy of the computed point clouds by the terrestrial Laser Scanner (TLS). In this research, Practical experiments were carried out by Faro focus premium TLS in order to investigate how does the variation of surface roughness and its reflectivity affect the positional accuracy of the measured scanner data at different scan angles and ranges. For this purpose, different materials that have distinct surface properties were conducted (glass, steel, wood, ekoplast, and total station (TS) sheet targets). Also, to examine the impact of the surface color, three of those selected materials have been painted with RGB color and black and white colors as well. About 54 scans were recorded during the experiment as all materials were scanned at three different scanning angles of (0˚, 30˚, and 60˚) and at ranges of 5 and 20 meters. The experiment's findings reveals that, at various incident angles, smooth surfaces have a greater impact on the accuracy of the scanned objects to create 3D point clouds than do rough surfaces. Furthermore, the total RMSEs in the point clouds position that measured from surface painted with red and black colors is greeter and higher than those measured from blue, green, and white colors painted surfaces. Interestingly, the total station target had never reflects the laser beam at all incident angles and ranges for class-1 laser beams. Additionally, the intensity of various materials varies. For example, the smooth materials steel and glass have varying degrees of accuracy because of their respective characteristics of the surface.

ZANCO Journal of Pure and Applied SciencesVol. 36(4)
Salahaddin University-Erbil (IQ)
Openalex Percentile: Top 87%
Gene expression and cancer classification
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