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Robust segmentation in laser scanning 3D point cloud data

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Open access
Authors
Nurunnabi, Abdul
Belton, David
West, Geoffrey
Date
2012
Type
Conference Paper

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Nurunnabi, Abdul and Belton, David and West, Geoff. 2012. Robust segmentation in laser scanning 3D point cloud data, in Proceedings of the International Conference on Digital Image Computing Techniques and Applications (DICTA), Dec 3-5 2012. Fremantle, WA: IEEE.
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Digital Image Computing Techniques and Applications (DICTA)
Source Conference
International Conference on Digital Image Computing Techniques and Applications (DICTA)
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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

Segmentation is a most important intermediate step in point cloud data processing and understanding. Covariance statistics based local saliency features from Principal Component Analysis (PCA) are frequently used for point cloud segmentation. However it is well known that PCA is sensitive to outliers. Hence segmentation results can be erroneous and unreliable. The problems of surface segmentation in laser scanning point cloud data are investigated in this paper. We propose a region growing based statistically robust segmentation algorithm that uses a recently introduced fast Minimum Covariance Determinant (MCD) based robust PCA approach. Experiments for several real laser scanning datasets show that PCA gives unreliable and non-robust results whereas the proposed robust PCA based method has intrinsic ability to deal with noisy data and gives more accurate and robust results for planar and non planar smooth surface segmentation.