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In many cases, the uncertainty of output quantities may be computed by assuming that the distribution represented by the result of measurement and its associated standard uncertainty is a Gaussian. This assumption may be unjustified and the uncertainty of the output quantities determined in this way may be incorrect. One tool to deal with different distribution functions of the input parameters and the resulting mixed-distribution of the output quantities is given through the Monte Carlo techniques. The resulting empirical distribution can be used to approximate the theoretical distribution of the output quantities. All required moments of different orders can then be numerically determined. To evaluate the procedure of derivation and evaluation of output parameter uncertainties outlined in this paper, a case study of kinematic terrestrial laserscanning (k-TLS) will be discussed. This study deals with two main topics: the refined simulation of different configurations by taking different input parameters with diverse probability functions for the uncertainty model into account, and the statistical analysis of the real data in order to improve the physical observation models in case of k-TLS. The solution of both problems is essential for the highly sensitive and physically meaningful application of k-TLS techniques for monitoring of, e. g., large structures such as bridges.

Estimation of Measurement Uncertainty of kinematic TLS Observation Process by means of Monte-Carlo Methods