Details
| Original language | English |
|---|---|
| Article number | 2349 |
| Journal | Remote Sensing |
| Volume | 15 |
| Issue number | 9 |
| Publication status | Published - 29 Apr 2023 |
Abstract
Terrestrial laser scanners (TLSs) are a standard method for 3D point cloud acquisition due to their high data rates and resolutions. In certain applications, such as deformation analysis, modelling uncertainties in the 3D point cloud is crucial. This study models the systematic deviations in laser scan distance measurements as a function of various influencing factors using machine-learning methods. A reference point cloud is recorded using a laser tracker (Leica AT 960) and a handheld scanner (Leica LAS-XL) to investigate the uncertainties of the Z+F Imager 5016 in laboratory conditions. From 49 TLS scans, a wide range of data are obtained, covering various influencing factors. The processes of data preparation, feature engineering, validation, regression, prediction, and result analysis are presented. The results of traditional machine-learning methods (multiple linear and nonlinear regression) are compared with eXtreme gradient boosted trees (XGBoost). Thereby, it is demonstrated that it is possible to model the systemic deviations of the distance measurement with a coefficient of determination of 0.73, making it possible to calibrate the distance measurement to improve the laser scan measurement. An independent TLS scan is used to demonstrate the calibration results.
Keywords
- distance calibration, laser scanning, machine learning, uncertainty modelling
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- General Earth and Planetary Sciences
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Remote Sensing, Vol. 15, No. 9, 2349, 29.04.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Uncertainty Modelling of Laser Scanning Point Clouds Using Machine-Learning Methods
AU - Hartmann, Jan Moritz
AU - Alkhatib, Hamza
N1 - The research presented was carried out within the scope of the collaborative project “Qualitätsgerechte 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 - 2023/4/29
Y1 - 2023/4/29
N2 - Terrestrial laser scanners (TLSs) are a standard method for 3D point cloud acquisition due to their high data rates and resolutions. In certain applications, such as deformation analysis, modelling uncertainties in the 3D point cloud is crucial. This study models the systematic deviations in laser scan distance measurements as a function of various influencing factors using machine-learning methods. A reference point cloud is recorded using a laser tracker (Leica AT 960) and a handheld scanner (Leica LAS-XL) to investigate the uncertainties of the Z+F Imager 5016 in laboratory conditions. From 49 TLS scans, a wide range of data are obtained, covering various influencing factors. The processes of data preparation, feature engineering, validation, regression, prediction, and result analysis are presented. The results of traditional machine-learning methods (multiple linear and nonlinear regression) are compared with eXtreme gradient boosted trees (XGBoost). Thereby, it is demonstrated that it is possible to model the systemic deviations of the distance measurement with a coefficient of determination of 0.73, making it possible to calibrate the distance measurement to improve the laser scan measurement. An independent TLS scan is used to demonstrate the calibration results.
AB - Terrestrial laser scanners (TLSs) are a standard method for 3D point cloud acquisition due to their high data rates and resolutions. In certain applications, such as deformation analysis, modelling uncertainties in the 3D point cloud is crucial. This study models the systematic deviations in laser scan distance measurements as a function of various influencing factors using machine-learning methods. A reference point cloud is recorded using a laser tracker (Leica AT 960) and a handheld scanner (Leica LAS-XL) to investigate the uncertainties of the Z+F Imager 5016 in laboratory conditions. From 49 TLS scans, a wide range of data are obtained, covering various influencing factors. The processes of data preparation, feature engineering, validation, regression, prediction, and result analysis are presented. The results of traditional machine-learning methods (multiple linear and nonlinear regression) are compared with eXtreme gradient boosted trees (XGBoost). Thereby, it is demonstrated that it is possible to model the systemic deviations of the distance measurement with a coefficient of determination of 0.73, making it possible to calibrate the distance measurement to improve the laser scan measurement. An independent TLS scan is used to demonstrate the calibration results.
KW - distance calibration
KW - laser scanning
KW - machine learning
KW - uncertainty modelling
UR - http://www.scopus.com/inward/record.url?scp=85159323629&partnerID=8YFLogxK
U2 - 10.3390/rs15092349
DO - 10.3390/rs15092349
M3 - Article
VL - 15
JO - Remote Sensing
JF - Remote Sensing
SN - 2072-4292
IS - 9
M1 - 2349
ER -