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Article

Estimating Control Points for B-Spline Surfaces Using Fully Populated Synthetic Variance–Covariance Matrices for TLS Point Clouds

1
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstr. 8/E120, 1040 Vienna, Austria
2
Institute of Engineering Geodesy, University of Stuttgart, Geschwister-Scholl-Str. 24D, 70174 Stuttgart, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Boris Kargoll and Hamza Alkhatib
Remote Sens. 2021, 13(16), 3124; https://doi.org/10.3390/rs13163124
Received: 1 July 2021 / Revised: 2 August 2021 / Accepted: 5 August 2021 / Published: 6 August 2021
(This article belongs to the Special Issue 3D Modelling from Point Cloud: Algorithms and Methods)
A flexible approach for geometric modelling of point clouds obtained from Terrestrial Laser Scanning (TLS) is by means of B-splines. These functions have gained some popularity in the engineering geodesy as they provide a suitable basis for a spatially continuous and parametric deformation analysis. In the predominant studies on geometric modelling of point clouds by B-splines, uncorrelated and equally weighted measurements are assumed. Trying to overcome this, the elementary errors theory is applied for establishing fully populated covariance matrices of TLS observations that consider correlations in the observed point clouds. In this article, a systematic approach for establishing realistic synthetic variance–covariance matrices (SVCMs) is presented and afterward used to model TLS point clouds by B-splines. Additionally, three criteria are selected to analyze the impact of different SVCMs on the functional and stochastic components of the estimation results. Plausible levels for variances and covariances are obtained using a test specimen of several dm—dimension. It is used to identify the most dominant elementary errors under laboratory conditions. Starting values for the variance level are obtained from a TLS calibration. The impact of SVCMs with different structures and different numeric values are comparatively investigated. Main findings of the paper are that for the analyzed object size and distances, the structure of the covariance matrix does not significantly affect the location of the estimated surface control points, but their precision in terms of the corresponding standard deviations. Regarding the latter, properly setting the main diagonal terms of the SVCM is of superordinate importance compared to setting the off-diagonal ones. The investigation of some individual errors revealed that the influence of their standard deviation on the precision of the estimated parameters is primarily dependent on the scanning distance. When the distance stays the same, one-sided influences on the precision of the estimated control points can be observed with an increase in the standard deviations. View Full-Text
Keywords: B-spline; control points; TLS point cloud; stochastic model; variance–covariance matrix B-spline; control points; TLS point cloud; stochastic model; variance–covariance matrix
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MDPI and ACS Style

Raschhofer, J.; Kerekes, G.; Harmening, C.; Neuner, H.; Schwieger, V. Estimating Control Points for B-Spline Surfaces Using Fully Populated Synthetic Variance–Covariance Matrices for TLS Point Clouds. Remote Sens. 2021, 13, 3124. https://doi.org/10.3390/rs13163124

AMA Style

Raschhofer J, Kerekes G, Harmening C, Neuner H, Schwieger V. Estimating Control Points for B-Spline Surfaces Using Fully Populated Synthetic Variance–Covariance Matrices for TLS Point Clouds. Remote Sensing. 2021; 13(16):3124. https://doi.org/10.3390/rs13163124

Chicago/Turabian Style

Raschhofer, Jakob, Gabriel Kerekes, Corinna Harmening, Hans Neuner, and Volker Schwieger. 2021. "Estimating Control Points for B-Spline Surfaces Using Fully Populated Synthetic Variance–Covariance Matrices for TLS Point Clouds" Remote Sensing 13, no. 16: 3124. https://doi.org/10.3390/rs13163124

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