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Robust segmentation for multiple planar surface extraction in laser scanning 3D point cloud data

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2012
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Conference Paper

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Nurunnabi, Abdul and Belton, David and West, Geoff. 2012. Robust segmentation for multiple planar surface extraction in laser scanning 3D point cloud data, in 21st International Conference on Pattern Recognition (ICPR), Nov 11-15 2012, pp. 1367-1370. Tsukuba, Japan: IEEE.
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21st Internatioinal Pattern recognitioin (ICPR)
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21st International Conference on Pattern Recognition (ICPR)
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Copyright © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Abstract

This paper investigates the segmentation of multiple planar surfaces from 3D point clouds. A Principle Component Analysis (PCA) based covariance technique is used for segmentation which is one of the most popular approaches in point cloud processing. It is well known that PCA is very sensitive to outliers and does not give reliable estimates for segmentation. We propose a statistically robust segmentation algorithm using a fast-minimum covariance determinant based robust PCA approach to get the local covariance statistics. This results in more reliable, robust and accurate segmentation. The application of the proposed method to simulated and terrestrial laser scanning point cloud datasets gives good results for multiple planar surface extraction and shows significantly better performance than PCA based methods. The algorithm has the potential for non-planar complex surface reconstruction.