Automatic quality assessment of terrestrial laser scans

This work addresses the topic of a quality modelling of terrestrial laser scans, including different quality measures such as precision, systematic deviations in distance measurement and completeness. For this purpose, the term "quality"is first defined in more detail in the field of TLS. A distinction is made between a total of seven categories that affect the quality of the TLS point cloud. The focus in this work lies on the uncertainty modeling of the TLS point clouds especially the distance measurement. It is demonstrated that influences such as the intensity and the incidence angle can lead to systematic deviations in the distance measurement of more than 1 mm. Based on these findings, it is presented that systematic deviations in distance measurement can be divided into four classes using machine learning classification approaches. The predicted classes can be useful for deformation analysis or for processing steps like registration. At the end of this work the entire quality assessment process is demonstrated using a real TLS point cloud (40 million points).