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    Robust Locally Weighted Regression Techniques for Ground Surface Points Filtering in Mobile Laser Scanning Three Dimensional Point Cloud Data

    235623_235623.pdf (2.834Mb)
    Access Status
    Open access
    Authors
    Nurunnabi, A.
    West, Geoff
    Belton, D.
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Nurunnabi, A. and West, G. and Belton, D. 2015. Robust Locally Weighted Regression Techniques for Ground Surface Points Filtering in Mobile Laser Scanning Three Dimensional Point Cloud Data. IEEE Transactions on Geoscience and Remote Sensing. 54 (4): pp. 2181-2193.
    Source Title
    IEEE Transactions on Geoscience and Remote Sensing
    DOI
    10.1109/TGRS.2015.2496972
    ISSN
    0196-2892
    School
    Department of Spatial Sciences
    Remarks

    Copyright © 2015 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.

    URI
    http://hdl.handle.net/20.500.11937/47686
    Collection
    • Curtin Research Publications
    Abstract

    This paper introduces robust algorithms for extracting the ground points in laser scanning 3-D point cloud data. Global polynomial functions have been used for filtering algorithms for point cloud data; however, it is not suitable as it may lead to bias for the filtering algorithms and can cause misclassification errors when many different objects are present. In this paper, robust statistical approaches are coupled with locally weighted 2-D regression that fits without any predefined global function for the variables of interest. Algorithms are performed iteratively on 2-D profiles: $x-z$ and $y-z$. The $z$ (elevation) values are robustly down weighted based on the residuals for the fitted points. The new set of down-weighted $z$ values, along with the corresponding $x$ (or $y$) values, is used to get a new fit for the lower surface level. The process of fitting and down weighting continues until the difference between two consecutive fits is insignificant. The final fit is the required ground level, and the ground surface points are those that fall within the ground level and the level after adding some threshold value with the ground level for $z$ values. Experimental results are compared with the recently proposed segmentation method through simulated and real mobile laser scanning point clouds from urban areas that include many objects that appear in road scenes such as short walls, large buildings, electric poles, signposts, and cars. Results show that the proposed robust methods efficiently extract ground surface points with better than 97% accuracy.

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