This dissertation critically examines the collection of LIDAR data using Leica Pegasus Manager and the subsequent processing of this data through quality control (QC) tools. The study focuses specifically on the differences in data accuracy and integrity that arise during the transition from raw data acquisition to QC processing stages. A comprehensive analysis was conducted, generating substantial quantitative data related to LIDAR measurements, processing parameters, and associated error rates. The findings revealed significant variability in data reliability, underscoring the challenges inherent in maintaining consistent data quality throughout the workflow. The research suggests that data integrity and overall usability can be substantially enhanced through the implementation of optimized acquisition and processing protocols. Specifically, it was found that refining these procedures could reduce error rates by up to 30%, demonstrating the potential for meaningful improvements in LIDAR data quality. This has critical implications for the use of LIDAR technology in healthcare applications, particularly in fields such as spatial analysis and medical imaging, where precise and dependable data is essential for accurate diagnostics and treatment planning. By establishing robust frameworks for both data acquisition and quality assurance, this study contributes significantly to technological advancements in healthcare, highlighting the vital role of reliable data in improving patient outcomes and supporting clinical decision-making. Beyond healthcare, the research offers a scalable model for standardizing LIDAR data protocols across various industries, promoting greater consistency and accuracy in the use of this powerful technology.

LIDAR, QC Tools, Mobile Mapping System, Pegasus Manager, 3D

geodesy engineering

Presentation: poster