Robust statistical approaches for feature extraction in laser scanning 3D point cloud data
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Three dimensional point cloud data acquired from mobile laser scanning system commonly contain outliers and/or noise. The presence of outliers and noise means most of the frequently used methods for feature extraction produce inaccurate and non-robust results. We investigate the problems of outliers and how to accommodate them for automatic robust feature extraction. This thesis develops algorithms for outlier detection, point cloud denoising, robust feature extraction, segmentation and ground surface extraction.
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Nurunnabi, A.; West, Geoff; Belton, D. (2015)Three dimensional point cloud data obtained from mobile laser scanning systems commonly contain outliers. In the presence of outliers most of the currently used methods such as principal component analysis for point cloud ...
Nurunnabi, A.; Belton, David; West, Geoff (2012)It is impractical to imagine point cloud data obtained from laser scanner based mobile mapping systems without outliers. The presence of outliers affects the most often used classical statistical techniques used in laser ...
Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud dataNurunnabi, A.; West, Geoff; Belton, David (2015)This paper proposes two robust statistical techniques for outlier detection and robust saliency features, such as surface normal and curvature, estimation in laser scanning 3D point cloud data. One is based on a robust ...