Details
| Original language | English |
|---|---|
| Pages (from-to) | 333-353 |
| Number of pages | 21 |
| Journal | Journal of Applied Geodesy |
| Volume | 17 |
| Issue number | 4 |
| Early online date | 7 Apr 2023 |
| Publication status | Published - 26 Oct 2023 |
Abstract
This work addresses the topic of a quality modelling of terrestrial laser scans, including different quality measures such as precision, systematic deviations in distance measurement and completeness. For this purpose, the term "quality"is first defined in more detail in the field of TLS. A distinction is made between a total of seven categories that affect the quality of the TLS point cloud. The focus in this work lies on the uncertainty modeling of the TLS point clouds especially the distance measurement. It is demonstrated that influences such as the intensity and the incidence angle can lead to systematic deviations in the distance measurement of more than 1 mm. Based on these findings, it is presented that systematic deviations in distance measurement can be divided into four classes using machine learning classification approaches. The predicted classes can be useful for deformation analysis or for processing steps like registration. At the end of this work the entire quality assessment process is demonstrated using a real TLS point cloud (40 million points).
Keywords
- classification, machine learning, quality assessment, systematic deviations, uncertainty modelling
ASJC Scopus subject areas
- Mathematics(all)
- Modelling and Simulation
- Engineering(all)
- Engineering (miscellaneous)
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
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In: Journal of Applied Geodesy, Vol. 17, No. 4, 26.10.2023, p. 333-353.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Automatic quality assessment of terrestrial laser scans
AU - Hartmann, Jan Moritz
AU - Heiken, Max Leonard
AU - Alkhatib, Hamza
AU - Neumann, Ingo
N1 - Funding Information: Research funding: The research presented was carried out within the scope of the collaborative project “Qualitátsgerechte Virtualisierung von zeitvariablen Objekträumen (QViZO)”, which was supported by the German Federal Ministry for Economic Affairs and Energy (BMWi) and the Central Innovation Programme for SMEs (ZIM FuE- Kooperationsprojekt, 16KN086442).
PY - 2023/10/26
Y1 - 2023/10/26
N2 - This work addresses the topic of a quality modelling of terrestrial laser scans, including different quality measures such as precision, systematic deviations in distance measurement and completeness. For this purpose, the term "quality"is first defined in more detail in the field of TLS. A distinction is made between a total of seven categories that affect the quality of the TLS point cloud. The focus in this work lies on the uncertainty modeling of the TLS point clouds especially the distance measurement. It is demonstrated that influences such as the intensity and the incidence angle can lead to systematic deviations in the distance measurement of more than 1 mm. Based on these findings, it is presented that systematic deviations in distance measurement can be divided into four classes using machine learning classification approaches. The predicted classes can be useful for deformation analysis or for processing steps like registration. At the end of this work the entire quality assessment process is demonstrated using a real TLS point cloud (40 million points).
AB - This work addresses the topic of a quality modelling of terrestrial laser scans, including different quality measures such as precision, systematic deviations in distance measurement and completeness. For this purpose, the term "quality"is first defined in more detail in the field of TLS. A distinction is made between a total of seven categories that affect the quality of the TLS point cloud. The focus in this work lies on the uncertainty modeling of the TLS point clouds especially the distance measurement. It is demonstrated that influences such as the intensity and the incidence angle can lead to systematic deviations in the distance measurement of more than 1 mm. Based on these findings, it is presented that systematic deviations in distance measurement can be divided into four classes using machine learning classification approaches. The predicted classes can be useful for deformation analysis or for processing steps like registration. At the end of this work the entire quality assessment process is demonstrated using a real TLS point cloud (40 million points).
KW - classification
KW - machine learning
KW - quality assessment
KW - systematic deviations
KW - uncertainty modelling
UR - http://www.scopus.com/inward/record.url?scp=85153751684&partnerID=8YFLogxK
U2 - 10.1515/jag-2022-0030
DO - 10.1515/jag-2022-0030
M3 - Article
VL - 17
SP - 333
EP - 353
JO - Journal of Applied Geodesy
JF - Journal of Applied Geodesy
SN - 1862-9016
IS - 4
ER -