Stages of processing data from terrestrial laser 3D scanning of road pavement using Trimble RealWorks

Abstract. Problem. Traditional methods of road condition monitoring have limitations in terms of data accuracy and completeness, particularly in the detection of surface defects. There is a need for technologies that provide precise measurements with millimeter accuracy while ensuring rapid data collection to support effective maintenance and repair planning. Terrestrial laser 3D scanning offers potential solutions, but requires systematic data processing methodologies. Goal. This study aims to develop processing stages for terrestrial laser 3D scanning data of road pavement using Trimble RealWorks software and to provide practical recommendations for its effective application in pavement condition assessment. Methodology. The research examines the functionality of Trimble RealWorks software for processing point cloud data obtained from terrestrial laser scanners. The methodology includes analyzing capabilities such as point cloud registration, display, analysis, and processing of data collected from Trimble laser 3D scanner. Results. The research establishes a structured workflow for processing terrestrial laser scanning data. The point cloud registration ("stitching") process combines individual scans into a unified model using automated algorithms that identify common points and surfaces between scans. The automatic quality control reporting feature provides verification of registration accuracy. The automatic classification of objects separates point clouds into categories, allowing focused analysis on road surfaces. Data cleaning through segmentation tools removes unwanted points that could negatively impact analysis accuracy. The creation of triangulated mesh models provides three-dimensional representation of road surface geometry with high precision, enabling detailed analysis of surface defects. Practical value. This study contributes to the field by developing a systematic approach to processing terrestrial laser scanning data specifically for road pavement analysis.

Authors

Publication Details

Journal
Bulletin of Kharkov National Automobile and Highway University
Published
2025-07-04
DOI
https://doi.org/10.30977/bul.2219-5548.2025.109.0.134
Primary Topic
3D Surveying and Cultural Heritage
Type
article
Field-Weighted Citation Impact
0.00
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article

Stages of processing data from terrestrial laser 3D scanning of road pavement using Trimble RealWorks

Yevhen Dorozhko, S. Urdzik, Angelika G. Batrakova, Hor Sarkisian et al.
Bulletin of Kharkov National Automobile and Highway University
3D Surveying and Cultural Heritage
article

Stages of processing data from terrestrial laser 3D scanning of road pavement using Trimble RealWorks

Yevhen Dorozhko, S. Urdzik, Angelika G. Batrakova, Hor Sarkisian, Iryna Shelkova
article en

Abstract

Abstract. Problem. Traditional methods of road condition monitoring have limitations in terms of data accuracy and completeness, particularly in the detection of surface defects. There is a need for technologies that provide precise measurements with millimeter accuracy while ensuring rapid data collection to support effective maintenance and repair planning. Terrestrial laser 3D scanning offers potential solutions, but requires systematic data processing methodologies. Goal. This study aims to develop processing stages for terrestrial laser 3D scanning data of road pavement using Trimble RealWorks software and to provide practical recommendations for its effective application in pavement condition assessment. Methodology. The research examines the functionality of Trimble RealWorks software for processing point cloud data obtained from terrestrial laser scanners. The methodology includes analyzing capabilities such as point cloud registration, display, analysis, and processing of data collected from Trimble laser 3D scanner. Results. The research establishes a structured workflow for processing terrestrial laser scanning data. The point cloud registration ("stitching") process combines individual scans into a unified model using automated algorithms that identify common points and surfaces between scans. The automatic quality control reporting feature provides verification of registration accuracy. The automatic classification of objects separates point clouds into categories, allowing focused analysis on road surfaces. Data cleaning through segmentation tools removes unwanted points that could negatively impact analysis accuracy. The creation of triangulated mesh models provides three-dimensional representation of road surface geometry with high precision, enabling detailed analysis of surface defects. Practical value. This study contributes to the field by developing a systematic approach to processing terrestrial laser scanning data specifically for road pavement analysis.

Bulletin of Kharkov National Automobile and Highway University(109)
Openalex Percentile: Top 73%
3D Surveying and Cultural Heritage
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