[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123675-en":3,"doc-seo-123675-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123675,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Uncertainty Modelling of Laser Scanning Point Clouds Using Machine-Learning Methods","Terrestrial laser scanners (TLSs) provide high data rates and resolutions for 3D point cloud acquisition, but deformation and high-accuracy applications require reliable uncertainty handling. The study models systematic deviations in TLS distance measurements as functions of influencing factors using machine-learning methods. A reference point cloud is recorded with a laser tracker and a handheld scanner to investigate laboratory uncertainties for a specified TLS model. From 49 TLS scans, data preparation, feature engineering, validation, regression, prediction, and result analysis are conducted, comparing linear/nonlinear regression with XGBoost. The model achieves a coefficient of determination of 0.73, enabling distance calibration validated with an independent TLS scan.","remote sensing  \nArticle  \nUncertainty Modelling of Laser Scanning Point Clouds Using Machine-Learning Methods  \nJan Hartmann * and Hamza Alkhatib   \nCitation: Hartmann, J.; Alkhatib, H. Uncertainty Modelling of Laser Scanning Point Clouds  \nUsing Machine-Learning Methods. Remote Sens. 2023, 15, 2349. [https://](https://)[ ](https://)[doi.org/10.3390/rs15092349](doi.org/10.3390/rs15092349)  \nAcademic Editors: Wei Yao, Wenbing Tao and Jie Shao  \nReceived: 17 March 2023  \nRevised: 24 April 2023  \nAccepted: 26 April 2023  \nPublished: 29 April 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nGeodetic Institute, Leibniz Universität Hannover, Nienburger Str. 1, 30167 Hannover, Germany; [alkhatib@gih.uni-hannover.de](alkhatib@gih.uni-hannover.de)  \n* Correspondence: jan.hartmannn@gih.uni-hannover.de  \nAbstract: 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.  \nKeywords: uncertainty modelling; laser scanning; machine learning; distance calibration  \n1. Introduction  \nTerrestrial laser scanners (TLSs) are valuable tools for 3D point acquisition due to their high data rates and resolutions. TLSs are increasingly used in applications with high accuracy requirements, such as in engineering geodesy, so it is important to ensure that these requirements can be met. To achieve this, it is important to take uncertainty factors into account in the choice of viewpoint (distances and incidence angle), and to model the uncertainties of the TLS measurement as accurately as possible. The outcomes of uncertainty modelling can be useful for weighting points in a registration, in testing whether deformations are significant in the deformation analysis, or for viewpoint planning. The precision and accuracy of a TLS measurement are influenced by various factors that have been studied in the literature. Expressions of uncertainties can be carried out according to the guidelines provided in the Guide to the Expression of Uncertainty in Measurement (GUM) [1] . This framework has already been applied to TLSs using Monte Carlo methods [2,3] . Imperfections in the mechanical components can cause axis deviations, which can be modelled according to studies such as [4–8] . The error model in [9] lists 18 different influencing factors that serve as the basis for current calibration methods [8] . The TLS distance measurement is also impacted by atmospheric conditions, such as temperature and air pressure, particularly for larger distanc","cbCaiiKAMO7XXLtU","https://ap.wps.com/l/cbCaiiKAMO7XXLtU","pdf",19311158,1,23,"English","en",105,"# Introduction\n## TLS uncertainty factors and error modelling foundations\n## Forward vs. backward uncertainty modelling\n# Methodology Overview\n## Reference data acquisition and influencing factors\n## Data preparation and feature engineering\n## Validation, regression, and prediction\n# Results and Discussion\n## Comparison of regression models vs. XGBoost\n## Systematic deviation modelling and calibration performance\n## Independent scan validation","[{\"question\":\"Why is uncertainty modelling important for terrestrial laser scanning (TLS)?\",\"answer\":\"TLS measurements affect outcomes in high-accuracy tasks such as deformation analysis and registration. Modelling uncertainty supports correct weighting of points and helps determine whether deformations are significant.\"},{\"question\":\"How does the study model uncertainties in TLS distance measurements?\",\"answer\":\"It builds machine-learning models that map systematic deviations in distance measurements to multiple influencing factors, using a reference point cloud collected in controlled laboratory conditions.\"},{\"question\":\"Which machine-learning approaches are compared, and what is the main performance result?\",\"answer\":\"Traditional multiple linear and nonlinear regression models are compared with XGBoost. The approach can model systemic distance deviations with a coefficient of determination of 0.73, supporting distance calibration improvements.\"}]","Uncertainty Modelling of Laser Scanning Point Clouds Using Machine-Learning Methods | PDF",1785817960,58,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"uncertainty-modelling-of-laser-scanning-point-clouds-using-machine-learning-methods","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/uncertainty-modelling-of-laser-scanning-point-clouds-using-machine-learning-methods/123675/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is uncertainty modelling important for terrestrial laser scanning (TLS)?","Question",{"text":76,"@type":77},"TLS measurements affect outcomes in high-accuracy tasks such as deformation analysis and registration. Modelling uncertainty supports correct weighting of points and helps determine whether deformations are significant.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the study model uncertainties in TLS distance measurements?",{"text":81,"@type":77},"It builds machine-learning models that map systematic deviations in distance measurements to multiple influencing factors, using a reference point cloud collected in controlled laboratory conditions.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine-learning approaches are compared, and what is the main performance result?",{"text":85,"@type":77},"Traditional multiple linear and nonlinear regression models are compared with XGBoost. The approach can model systemic distance deviations with a coefficient of determination of 0.73, supporting distance calibration improvements.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]