Gabriel Kerekes

Zusammenfassung

Deformation and displacement monitoring of arch dams is usually conducted by measurement methods that gained widespread acceptance. Mostly pointwise measurements acquired within different epochs serve as the basis for deformation analysis. The uncertainty information of these observations is generally established. When referring to surface-based deformation monitoring, terrestrial laser scanning (TLS) is intensively hyped up. Nevertheless, current knowledge about stochastic modelling of TLS observations is scarce and very often reduced to a diagonal variance-covariance matrix (VCM). This neglects the exiting correlations within the point clouds. Aiming to fill the gap, this paper shows one possibility of obtaining a fully populated variance-covariance matrix using the elementary error model (EEM). Previous publications on this topic described the application of EEM for the Leica HDS 7000 laser scanner and showed results on simulated data for laboratory objects. Within this paper instrumental errors of the same scanner are modeled differently, according to recent work. A real scan of the arch dam Kops in Austria highlights influences on the variances, covariances and correlations of points within the point cloud for two instrument error models. Findings show that the model choice does not bring a notable change in the error of positions, but influences the correlation coefficients up to a level of $\Delta$$\rho$þinspace=þinspace0.2.

Zusammenfassung

All measurements are affected by systematic and random deviations. A huge challenge is to correctly consider these effects on the results. Terrestrial laser scanners deliver point clouds that usually precede surface modeling. Therefore, stochastic information of the measured points directly influences the modeled surface quality. The elementary error model (EEM) is one method used to determine error sources impact on variances-covariance matrices (VCM). This approach assumes linear models and normal distributed deviations, despite the non-linear nature of the observations. It has been proven that in 90% of the cases, linearity can be assumed. In previous publications on the topic, EEM results were shown on simulated data sets while focusing on panorama laser scanners. Within this paper an application of the EEM is presented on a real object and a functional model is introduced for hybrid laser scanners. The focus is set on instrumental and atmospheric error sources. A different approach is used to classify the atmospheric parameters as stochastic correlating elementary errors, thus expanding the currently available EEM. Former approaches considered atmospheric parameters functional correlating elementary errors. Results highlight existing spatial correlations for varying scanner positions and different atmospheric conditions at the arch dam Kops in Austria.