The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Download
Citation
Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 395–400, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-395-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 395–400, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-395-2022
 
30 May 2022
30 May 2022

UNCERTAINTY MODELING FOR POINT CLOUD-BASED AUTOMATIC INDOOR SCENE RECONSTRUCTION BY STRICT ERROR PROPAGATION ANALYSIS

M. Jarzabek-Rychard1,2 and H.-G. Maas2 M. Jarzabek-Rychard and H.-G. Maas
  • 1Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, Poland
  • 2Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, Germany

Keywords: Error propagation, Uncertainty modeling, Indoor 3D models, Building reconstruction, Taylor series

Abstract. Accurate digital representation of indoor facilities is a key component for the generation of building twins. 3D indoor scenes are often reconstructed from 3D point clouds obtained by various measurement techniques, which usually show different accuracy characteristics. During the reconstruction process, the uncertainties of data and intermediate products propagate into the accuracy of the vectorized model. Although point clouds-based 3D building modeling has been a hot topic of research for at least two decades, a thorough analysis of error propagation for this problem from a geodetic point of view is still underrepresented. In this contribution, we propose an analytical approach to estimate the uncertainty of 3D modeling results using the analytic approach based on first-order Taylor-series expansion. A general model for the input data is established and the uncertainty expressions of all computed products are symbolically derived. We estimate the uncertainty of 3D data fitting, followed by the derivation of vectorized building parameters and their covariance matrices. The results of the theoretical approaches are tested on real data presenting an indoor scene. The practical example is illustrated, thoroughly analysed, and quantified.