Down-Sampling of Point Clouds for the Technical Diagnostics of Buildings and Structures

Abstract

Terrestrial laser scanning (TLS) is a non-destructive testing method for the technical assessment of existing structures. TLS has been successfully harnessed for monitoring technical surface conditions and morphological characteristics of historical buildings (e.g., the detection of cracks and cavities). TLS measurements with very high resolution should be taken to detect minor defects on the walls of buildings. High-resolution measurements are mostly needed in certain areas of interest, e.g., cracks and cavities. Therefore, reducing redundant information on flat areas without cracks and cavities is very important. In this case, automatic down-sampling of datasets according to the aforementioned criterion is required. This paper presents the use of the Optimum Dataset (OptD) method to optimize TLS dataset. A Leica ScanStation C10 time-of-flight scanner and a Z+F IMAGER 5016 phase-shift scanner were used during the research. The research was conducted on a specially prepared concrete sample and real object, i.e., a brick citadel located on the Kościuszko Mound in Cracow. The reduction of dataset by the OptD method and random method from TLS measurements were compared and discussed. The results prove that the large datasets from TLS diagnostic measurements of buildings and structures can be successfully optimized using the OptD method.

Keywords

• terrestrial laser scanning
• dataset reduction
• OptD method
• defect in building wall