| Abstract [eng] |
This master's final project examines the application of three-dimensional (3D) laser scanning methods for determining the geometry and volume of enclosed spaces, specifically horizontal fuel tanks. The aim of the work is to evaluate the spatial distribution of data quality/uncertainty in the 3D laser scanning result (point cloud) of a horizontal fuel tank by analyzing the influence of key information parameters on measurement quality. The paper reviews traditional and modern fuel tank calibration methods, 3D laser scanning principles, and technological challenges. The research methodology includes collecting 3D point cloud data using a laser scanner and processing the data using developed and adapted algorithms (determining the scanning device position, calculating and segmenting the cross-section perimeter, determining surface orientation and incidence angle, evaluating local surface approximation error (RMS)). Detailed statistical analysis of the obtained parameters was performed (histograms, heat maps, correlation analysis) to determine their interrelationships and impact on measurement quality. Research results showed that the laser incidence angle, distance to the surface, and point density have a statistically significant impact on the accuracy of 3D scanning results. An optimal range of incidence angles and distances was determined, within which the most reliable results are obtained. It was found that higher point density allows for more stable evaluation of surface parameters. The identified trends remain consistent when analyzing tanks of different sizes. This work quantitatively evaluates the influence of information parameters on 3D scanning accuracy in the context of fuel tank calibration and provides a methodological basis for optimizing scanning and data processing procedures. |
