Project
3D laser scanning systems are vital for the digital transformation of geometric measurements. However, the current metrological tool kit cannot keep up with state‑of‑the‑art 3D laser scanning systems. In addition, assessing the measurement uncertainty of such 3D laser scanning systems requires unrealistic computation efforts. This project will address these metrological issues by developing new approaches for the entire data capture and point cloud processing chain. New, validated digital metrological twins (D‑MTs) will be developed which will be used to assess the complete physical measurement. A holistic quality metric will also be derived to handle the typical, excessively large point clouds. Further to this, verification strategies will be developed to validate dedicated software packages, as well as case studies in aerospace manufacturing, logistics, and geodesy which will be used to demonstrate the potential of the project’s developed methods.
Objectives
The overall objective of the project is to foster the current digital transformation in 3D measurement technology based on 3D laser scanning systems by improving traceability to the SI definition of the metre. Holistic, systematic, and generalisable methods and procedures shall be developed for the entire processing chain.
The specific objectives of the project are:
1. To develop measurement models for use with D‑MTs of 3D scanning systems including instrumental, environmental and target influences on the measurement result. A modular approach will be followed to allow model refinement and adjustment to different measurement scenarios and different scanner configurations. The generated large point clouds of the models or D‑MTs will be in a format compatible with existing commercial 3D analysis software. The measurement uncertainty of the point clouds will be determined following the GUM (Guide to the Expression of Uncertainty in Measurement) and its supplements.
2. To assess the quality of point clouds in dimensional metrology through comparisons using a variety of measurement standards with typical (i) dimensions, (ii) features and (iii) materials generated with 3D scanning systems, and D‑MTs of those instruments. Based on these results and the measurement uncertainty, a metric will be determined for the metrological quality of point cloud(s) generated by 3D scanning systems.
3. To develop and establish reference datasets for the assessment and verification of software tools deriving geometric or radiometric features from point clouds. The reference datasets will be suitable for deriving the achievable measurement uncertainty, and data fusion of multiple scans, will be part of the verification scenarios (where appropriate). Datasets will comprise (i) classical geometries such as spheres, planes, cylinders and (ii) more complex geometries typical for two or more applications from industrial metrology, aerospace and geodesy.
4. Using the outcomes of Objectives 1‑3, to (i) develop guidelines for the uncertainty assessment and object classification of geometric and radiometric features derived from large coordinate point clouds, (ii) to determine the measurement uncertainty of target measurement features, using case studies from industrial metrology, aerospace, or geodesy, and (iii) to design and establish novel calibration or verification services for scanning systems.
5. To facilitate the take up of the technology and measurement infrastructure developed in the project by the measurement supply chain (e.g. manufacturers of 3D scanners, developers of data processing and analysis software), standards developing organisations (ISO/TC 172/SC 6, ISO/TC 28/SC 2, ISO/TC 213/WG 10) and end users from aerospace industry, logistics, and geodesy.
Publishable Summary
The publishable summary describes the need, progress beyond the state of the art and potential outcomes and impact of the project.