Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data

Abstract

a b s t r a c tThis 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 z-score and the other uses a Mahalanobis type robust distance. The methods couple the ideas of point to plane orthogonal distance and local surface point consistency to get Maximum Consistency with Minimum Distance (MCMD). The methods estimate the best-fit-plane based on most probable outlier free, and most consistent, points set in a local neighbourhood. Then the normal and curvature from the best-fit-plane will be highly robust to noise and outliers. Experiments are performed to show the performance of the algorithms compared to several existing well-known methods (from computer vision, data mining, machine learning and statistics) using synthetic and real laser scanning datasets of complex (planar and non-planar) objects. Results for plane fitting, denoising, sharp feature preserving and segmentation are significantly improved. The algorithms are demonstrated to be significantly faster, more accurate and robust. Quantitatively, for a sample size of 50 with 20% outliers the proposed MCMD_Z is approximately 5, 15 and 98 times faster than the existing methods: uLSIF, RANSAC and RPCA, respectively. The proposed MCMD_MD method can tolerate 75% clustered outliers, whereas, RPCA and RANSAC can only tolerate 47% and 64% outliers, respectively. In terms of outlier detection, for the same dataset, MCMD_Z has an accuracy of 99.72%, 0.4% false positive rate and 0% false negative rate; for RPCA, RANSAC and uLSIF, the accuracies are 97.05%, 47.06% and 94.54%, respectively, and they have misclassification rates higher than the proposed methods. The new methods have potential for local surface reconstruction, fitting, and other point cloud processing tasks.

Robust Cylinder Fitting in Three-Dimensional Point Cloud Data

“…Hence, the first PC ( 2 ) shows the highest ( 2 %) variability of the data. Although PCA has been used successfully in point cloud processing, the results from PCA are highly influenced by outliers and produce misleading results (Nurunnabi et al, 2014(Nurunnabi et al, , 2015(Nurunnabi et al, , 2016.…”

“…As well, the presence of outliers that do not follow the outline of the majority of points of interest is common for many reasons such as systematic biases or sensor errors, multipath reflections, occlusions, unexpected moving objects, environmental disorder like snow, rain, dust, and random noise that may appear as off-* Corresponding author surface points. Pseudo-outliers that are created by multiple and unorganized structures mainly appeared as clustered outliers (cf., Nurunnabi et al, 2015). PCD especially from Mobile Laser Scanning (MLS) and Aerial Laser Scanning (ALS) are mostly incomplete and sometimes locally missing because the corresponding scan systems obtain PCD from a particular point of view.…”

Scan Profiles Based Method for Segmentation and Extraction of Planar Objects in Mobile Laser Scanning Point Clouds

“…These methods segment connected points that have similar characteristics into the same regions. They are more robust to noise and perform better than the edge based approaches (Nurunnabi et al, 2015). Oesau et al (2016) proposed a method for planar object detection for unorganized point clouds.…”

“…Seed points for region growing are selected based on their residuals. As PCA is very sensitive to outliers, Nurunnabi et al (2015) proposed the Robust and Diagnostic PCA (RDPCA) method to detect outliers and then estimate local saliency features from the clean subset. A robust segmentation method based on the estimated local saliency features was proposed to segment dense and homogeneous mobile laser scanning point clouds.…”

“…They can be divided into five groups: edge based (Huang & Menq, 2001), robust model fitting (Fischler & Bolles, 1981;Hough, 1962;Oesau et al, 2016;Vosselman & Maas, 2010), scan line based (Jiang & Bunke, 1994;Sithole & Vosselman, 2003) and region/surface growing based (Nurunnabi et al, 2012;Nurunnabi et al, 2015;Oesau et al, 2016;Rabbani, van den Heuvel, & Vosselmann, 2006;Vosselman & Maas, 2010).…”