TY - JOUR

T1 - PointNet-based modeling of systematic distance deviations for improved TLS accuracy

AU - Hartmann, Jan

AU - Ernst, Dominik

AU - Neumann, Ingo

AU - Alkhatib, Hamza

N1 - Publisher Copyright: © 2024 Walter de Gruyter GmbH, Berlin/Boston 2024.

PY - 2024/6/19

Y1 - 2024/6/19

N2 - Terrestrial laser scanners (TLSs) have become indispensable for acquiring highly detailed and accurate 3D representations of the physical world. However, the acquired data is subject to systematic deviations in distance measurements due to external influences, such as distance and incidence angle. This research introduces a calibration approach by applying a deep learning model based on PointNet to predict and correct these systematic distance deviations, incorporating not only the XYZ coordinates but also additional features like intensity, incidence angle, and distances within a local neighbourhood radius of 5 cm. By predicting and subsequently correcting systematic distance deviations, the quality of TLS point clouds can be improved. Hence, our model is designed to complement and build upon the foundation of prior internal TLS calibration. A data set collected under controlled environmental conditions, containing various objects of different materials, served as the basis for training and validation the PointNet based model. In addition our analysis showcase the model's capability to accurately model systematic distance deviations, outperforming existing methods like gradient boosting trees by capturing the spatial relationships and dependencies within the data more effectively. By defining test data sets, excluded from the training process, we underscore the ongoing effectiveness of our model's distance measurement calibration, showcasing its ability to improve the accuracy of the TLS point cloud.

AB - Terrestrial laser scanners (TLSs) have become indispensable for acquiring highly detailed and accurate 3D representations of the physical world. However, the acquired data is subject to systematic deviations in distance measurements due to external influences, such as distance and incidence angle. This research introduces a calibration approach by applying a deep learning model based on PointNet to predict and correct these systematic distance deviations, incorporating not only the XYZ coordinates but also additional features like intensity, incidence angle, and distances within a local neighbourhood radius of 5 cm. By predicting and subsequently correcting systematic distance deviations, the quality of TLS point clouds can be improved. Hence, our model is designed to complement and build upon the foundation of prior internal TLS calibration. A data set collected under controlled environmental conditions, containing various objects of different materials, served as the basis for training and validation the PointNet based model. In addition our analysis showcase the model's capability to accurately model systematic distance deviations, outperforming existing methods like gradient boosting trees by capturing the spatial relationships and dependencies within the data more effectively. By defining test data sets, excluded from the training process, we underscore the ongoing effectiveness of our model's distance measurement calibration, showcasing its ability to improve the accuracy of the TLS point cloud.

KW - calibration

KW - deep learning

KW - PointNet

KW - systematic distance deviation

KW - terrestrial laser scanning

UR - http://www.scopus.com/inward/record.url?scp=85196501922&partnerID=8YFLogxK

U2 - 10.1515/jag-2023-0097

DO - 10.1515/jag-2023-0097

M3 - Review article

AN - SCOPUS:85196501922

JO - Journal of Applied Geodesy

JF - Journal of Applied Geodesy

SN - 1862-9016

ER -