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Fitting Cylinder to Point Cloud Data
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| Organization: | Budapest University of Technology and Economics |
| Department: | Photogrammetry and Geoinformatics |
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2014-08-25
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A new robust parameter estimation method taking into account the real model error distribution is presented for large size of noisy data points.Maximum likelihood technique is employed to compute the model parameters assuming that the distribution of the model errors is a Gaussian mixture of the corresponding inlier and outlier measurements. The maximization is carried out by local method, where the initial guess values were computed via numerical Groebner basis. After parameter estimation based on an initially computed distribution, the real errors are determined and the corresponding Gaussian mixture is identified via expectation maximization algorithm. The iteration procedure is converging when the error distribution becomes stationary. The method is illustrated via identifying tree stems using ground-based Lidar data.
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parameter estimation, circular cylinder, point cloud, Gröbner basis, outliers, maximum likelihood, Gaussian mixture, expectation maximization, Lidar
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| Fitting_Cylinder.zip (1.4 MB) - ZIP archive | | Tree_A.dat (395.8 KB) - Unknown MIME type | | Tree_B.dat (2.1 MB) - Unknown MIME type |
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