Abstract
In many fields such as robotics (Poppinga in International conference on intelligent robots and systems (IROS). IEEE Press 2008), computer vision (Mitra and Nguyen in SoCG’03. pp 322–328, 2003), digital photogrammetry (Yang and Förtsner in Technical report. Nr.1, department of photogrammetry institute of geodesy and geo-information. Uni., Bonn, Germany, 2010), surface reconstruction (Nurunnabi et al. in ISPRS annals of the photogrammetry, remote sensing and spatial information sciences. Melbourne, Australia, pp 269–275, 2012), computational geometry (Lukács et al. in ECCV’98. Springer-Verlag, pp 671–686, 1998) as well as in the increasing applications of laser scanning ((Stathas et al. in Proceedings 11th international fig symposium on deformation measurements. Santorini, Greece, 2003), it is a fundamental task for extracting features from 3D point cloud. Since the physical limitations of the sensors, the occlusions, multiple reflectance and noise can produce off-surface points, robust fitting techniques are required. Robust fitting means an estimation technique which is able to estimate accurate model parameters not only despite small-scale noise in the data set but occasionally large scale measurement errors (outliers). Outliers definition is not easy.
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Awange, J.L., Paláncz, B., Lewis, R.H., Völgyesi, L. (2018). Robust Regression. In: Mathematical Geosciences. Springer, Cham. https://doi.org/10.1007/978-3-319-67371-4_13
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