Uncertainty Modelling of Laser Scanning Point Clouds Using Machine-Learning Methods

authored by

Jan Moritz Hartmann, Hamza Alkhatib

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.

Organisation(s)

Geodetic Institute

Type

Article

Journal

Remote Sensing

Volume

15

ISSN

2072-4292

Publication date

29.04.2023

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Earth and Planetary Sciences(all)

Electronic version(s)

https://doi.org/10.3390/rs15092349 (Access: Open)

 

Details in the research portal "Research@Leibniz University"