Ötsch, E., Harmening, C., & Neuner, H. (2023). Investigation of space-continuous deformation from point clouds of structured surfaces. Journal of Applied Geodesy, 151–159. https://doi.org/10.1515/jag-2022-0038
One approach to estimate space-continuous deformation from point clouds is the parameter-based epochal comparison of approximating surfaces. This procedure allows a statistical assessment of the estimated deformations. Typically, holistic geometric models approximate the scanned surfaces. Regarding this, the question arises on how discontinuities of the object’s surface resulting from e.g. single bricks or concrete blocks, influence the parameters of the approximating continuous surfaces and in further consequence the derived deformation. This issue is tackled in the following paper. B-spline surfaces are used to approximate the scanned point clouds. The approximation implies solving a Gauss–Markov-Model, thus allowing accounting for the measurements’ stochastic properties as well as propagating them on the surfaces’ control points. A parametric comparison of two B-spline surfaces can be made on the basis of these estimated control points. This approach is advantageous with regard to the transition of the space-continuous deformation analysis to a point-based task, thus ensuring the applicability of the well-established congruency model. The influence of the structure’s geometry on the surfaces’ control points is investigated using terrestrial laser scans of a clinker facade. Points measured in the joints are eliminated using an own developed segmentation approach. A comparison of the results obtained from segmented as well as from unsegmented laser scans for the B-spline approximation and the subsequent deformation analysis provides information about the structure-related influence. An aqueduct arc is used as measuring object in this study. For the intended comparison, data sets, which contain possible influences due to changes of the mechanical loads, are analysed.
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Research Areas:
Energy Active Buildings, Settlements and Spatial Infrastructures: 40% Mathematical and Algorithmic Foundations: 30% Environmental Monitoring and Climate Adaptation: 30%