A robust cylindrical fitting to point cloud data
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—assume...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Published: |
Taylor & Francis Co Ltd
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/43093 |
| _version_ | 1848756595673006080 |
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| author | Paláncz, B. Awange, Joseph Somogyi, A. Rehány, N. Lovas, T. Molnár, B. Fukuda, Y. |
| author_facet | Paláncz, B. Awange, Joseph Somogyi, A. Rehány, N. Lovas, T. Molnár, B. Fukuda, Y. |
| author_sort | Paláncz, B. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:14:42Z |
| format | Journal Article |
| id | curtin-20.500.11937-43093 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:14:42Z |
| publishDate | 2016 |
| publisher | Taylor & Francis Co Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-430932017-09-13T14:30:06Z A robust cylindrical fitting to point cloud data Paláncz, B. Awange, Joseph Somogyi, A. Rehány, N. Lovas, T. Molnár, B. Fukuda, Y. 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. 2016 Journal Article http://hdl.handle.net/20.500.11937/43093 10.1080/08120099.2016.1230147 Taylor & Francis Co Ltd restricted |
| spellingShingle | Paláncz, B. Awange, Joseph Somogyi, A. Rehány, N. Lovas, T. Molnár, B. Fukuda, Y. A robust cylindrical fitting to point cloud data |
| title | A robust cylindrical fitting to point cloud data |
| title_full | A robust cylindrical fitting to point cloud data |
| title_fullStr | A robust cylindrical fitting to point cloud data |
| title_full_unstemmed | A robust cylindrical fitting to point cloud data |
| title_short | A robust cylindrical fitting to point cloud data |
| title_sort | robust cylindrical fitting to point cloud data |
| url | http://hdl.handle.net/20.500.11937/43093 |