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    A robust cylindrical fitting to point cloud data

    Access Status
    Fulltext not available
    Authors
    Paláncz, B.
    Awange, Joseph
    Somogyi, A.
    Rehány, N.
    Lovas, T.
    Molnár, B.
    Fukuda, Y.
    Date
    2016
    Type
    Journal Article
    
    Metadata
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    Citation
    Paláncz, B. and Awange, J. and Somogyi, A. and Rehány, N. and Lovas, T. and Molnár, B. and Fukuda, Y. 2016. A robust cylindrical fitting to point cloud data. Australian Journal of Earth Sciences. 63 (5): pp. 665-673.
    Source Title
    Australian Journal of Earth Sciences
    DOI
    10.1080/08120099.2016.1230147
    ISSN
    0812-0099
    School
    Department of Spatial Sciences
    URI
    http://hdl.handle.net/20.500.11937/43093
    Collection
    • Curtin Research Publications
    Abstract

    Environmental, engineering and industrial modelling of natural features (e.g. trees) and man-made features (e.g. pipelines) requires some form of fitting of geometrical objects such as cylinders, which is commonly undertaken using a least-squares method that—in order to get optimal estimation—assumes normal Gaussian distribution. In the presence of outliers, however, this assumption is violated leading to a Gaussian mixture distribution. This study proposes a robust parameter estimation method, which is an improved and extended form of vector algebraic modelling. The proposed method employs expectation maximisation and maximum likelihood estimation (MLE) to find cylindrical parameters in case of Gaussian mixture distribution. MLE computes the model parameters assuming that the distribution of model errors is a Gaussian mixture corresponding to inlier and outlier points. The parameters of the Gaussian mixture distribution and the membership functions of the inliers and outliers are computed using an expectation maximisation algorithm from the histogram of the model error distribution, and the initial guess values for the model parameters are obtained using total least squares. The method, illustrated by a practical example from a terrestrial laser scanning point cloud, is novel in that it is algebraic (i.e. provides a non-iterative solution to the global maximisation problem of the likelihood function), is practically useful for any type of error distribution model and is capable of separating points of interest and outliers.

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