The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLVIII-2/W2-2022
08 Dec 2022
 | 08 Dec 2022


S. Nietiedt and T. Luhmann

Keywords: Close-Range Photogrammetry, Dynamic, Monte Carlo simulation, Kernel density estimation, Accuracy

Abstract. The verification of a measurement system is an essential part of system development. For this purpose, various guidelines can be used to evaluate and validate photogrammetric systems. However, these guidelines are only designed to validate systems that observe a static scene. Hence, these guidelines cannot validate measurement systems that observe dynamic scenes. In addition, reference data is not available for most systems, making verification significantly more difficult or not a practical solution. In this work, a simulation-based verification approach is presented. The presented approach allows the analysis of complex systems and the investigation of specific processing steps. The approach is based on a Monte Carlo simulation, which only requires the probability density distributions of the input data and synthetic reference data. For this purpose, the probability density distributions of the input data are determined by kernel density estimation to generate realistic input data. The application is a wind tunnel test, where aerodynamic and structural dynamic phenomena are observed at a wind turbine model. The measurement system consists of four high-speed cameras, which acquire the rotor blades' deformations. The objective of the simulation is to evaluate the complete process regarding the accuracy and precision of the measurement system. Experimental data can be used to estimate the quality of the simulation. It was shown that the simulation produces realistic results and that it is suitable for validating dynamic measurement systems. The simulation showed that the precision and accuracy of the system are highly dependent from the estimation of the self-motion. The achieved accuracy is still high and allows the detection of small-scale blade deformations.