TY - JOUR

T1 - Modeling the distribution of TLS distance related uncertainties

AU - Hartmann, Jan

AU - Neumann, Ingo

AU - Alkhatib, Hamza

N1 - Publisher Copyright: © 2026 The Authors

PY - 2026/2/20

Y1 - 2026/2/20

N2 - Accurate calibration of terrestrial laser scanners (TLSs) is essential for high-precision applications such as deformation monitoring and structural health assessment. While prior work has addressed either systematic distance deviations or measurement precision in isolation, there remains a lack of methods that jointly model both components within a probabilistic framework. This study introduces a novel approach for the simultaneous estimation and calibration of TLS distance deviation and precision by leveraging probabilistic regression. Assuming Gaussian distributed deviations, the method predicts both the mean (systematic deviation) and standard deviation (precision) using two models: NGBoost (Natural Gradient Boosting) and a deep neural network. The approach is evaluated on a dedicated benchmark data set comprising three measurement campaigns with the Z+F Imager 5016, designed to assess temporal stability and cross-device generalizability. Results reveal that while systematic deviations are temporally stable, they are unit-specific and cannot be directly transferred between devices. In contrast, the predicted precision estimates generalize successfully across different scanner units. NGBoost demonstrates superior robustness compared to the neural network, particularly in data-sparse regions. This is confirmed by an analysis of epistemic model uncertainty, where NGBoost maintains high confidence across the entire domain, whereas the neural network exhibits significant instability at intensity boundaries. The proposed method not only enables effective, instrument-specific calibration but also provides reliable, data-driven uncertainty estimates for decision-making.

AB - Accurate calibration of terrestrial laser scanners (TLSs) is essential for high-precision applications such as deformation monitoring and structural health assessment. While prior work has addressed either systematic distance deviations or measurement precision in isolation, there remains a lack of methods that jointly model both components within a probabilistic framework. This study introduces a novel approach for the simultaneous estimation and calibration of TLS distance deviation and precision by leveraging probabilistic regression. Assuming Gaussian distributed deviations, the method predicts both the mean (systematic deviation) and standard deviation (precision) using two models: NGBoost (Natural Gradient Boosting) and a deep neural network. The approach is evaluated on a dedicated benchmark data set comprising three measurement campaigns with the Z+F Imager 5016, designed to assess temporal stability and cross-device generalizability. Results reveal that while systematic deviations are temporally stable, they are unit-specific and cannot be directly transferred between devices. In contrast, the predicted precision estimates generalize successfully across different scanner units. NGBoost demonstrates superior robustness compared to the neural network, particularly in data-sparse regions. This is confirmed by an analysis of epistemic model uncertainty, where NGBoost maintains high confidence across the entire domain, whereas the neural network exhibits significant instability at intensity boundaries. The proposed method not only enables effective, instrument-specific calibration but also provides reliable, data-driven uncertainty estimates for decision-making.

KW - Distance deviation

KW - Neural network

KW - NGBoost

KW - Probabilistic regression

KW - TLS

KW - Uncertainty modeling

UR - http://www.scopus.com/inward/record.url?scp=105030565085&partnerID=8YFLogxK

U2 - 10.1016/j.measurement.2026.120908

DO - 10.1016/j.measurement.2026.120908

M3 - Article

AN - SCOPUS:105030565085

VL - 270

JO - Measurement: Journal of the International Measurement Confederation

JF - Measurement: Journal of the International Measurement Confederation

SN - 0263-2241

M1 - 120908

ER -