Diagnostics based Principal Component Analysis for Robust Plane Fitting in Laser Data
Plane fitting and obtaining characteristics (e.g., normal) from the estimated plane are fundamental tasks in many applications in which laser scanner 3D data is used. Unfortunately, laser data are not free from outliers. Principal Component Analysis (PCA) is a popular method for plane fitting, but i...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Inc.
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/8020 |
| _version_ | 1848745536208764928 |
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| author | Nurunnabi, Abdul Belton, David West, Geoff |
| author2 | N/A |
| author_facet | N/A Nurunnabi, Abdul Belton, David West, Geoff |
| author_sort | Nurunnabi, Abdul |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Plane fitting and obtaining characteristics (e.g., normal) from the estimated plane are fundamental tasks in many applications in which laser scanner 3D data is used. Unfortunately, laser data are not free from outliers. Principal Component Analysis (PCA) is a popular method for plane fitting, but it is known that PCA is very sensitive to outliers and gives misleading non-robust results. We present a robust plane fitting algorithm based on PCA coupled with an outlier detecting diagnostic statistical approach. In this method, the recently introduced robust scatter matrix is used to calculate robust statistical distance for finding outliers. After excluding outliers, PCA is performed on the outlier free data which is used for fitting planar surfaces and to estimate robust normal and other parameters. Demonstration of the new algorithm through several synthetic and vehicle based laser scanning data show that the proposed method is efficient, and gives robust estimates. Results outperform Least Squares (LS), PCA and are significantly better than the well-known RANSAC in terms of time, accuracy and robustness. This method has great potential for robust segmentation, surface reconstruction, and other point cloud processing tasks. |
| first_indexed | 2025-11-14T06:18:55Z |
| format | Conference Paper |
| id | curtin-20.500.11937-8020 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:18:55Z |
| publishDate | 2013 |
| publisher | IEEE Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-80202017-02-28T01:31:39Z Diagnostics based Principal Component Analysis for Robust Plane Fitting in Laser Data Nurunnabi, Abdul Belton, David West, Geoff N/A point cloud surface reconstruction robust normal segmentation Feature extraction outlier Plane fitting and obtaining characteristics (e.g., normal) from the estimated plane are fundamental tasks in many applications in which laser scanner 3D data is used. Unfortunately, laser data are not free from outliers. Principal Component Analysis (PCA) is a popular method for plane fitting, but it is known that PCA is very sensitive to outliers and gives misleading non-robust results. We present a robust plane fitting algorithm based on PCA coupled with an outlier detecting diagnostic statistical approach. In this method, the recently introduced robust scatter matrix is used to calculate robust statistical distance for finding outliers. After excluding outliers, PCA is performed on the outlier free data which is used for fitting planar surfaces and to estimate robust normal and other parameters. Demonstration of the new algorithm through several synthetic and vehicle based laser scanning data show that the proposed method is efficient, and gives robust estimates. Results outperform Least Squares (LS), PCA and are significantly better than the well-known RANSAC in terms of time, accuracy and robustness. This method has great potential for robust segmentation, surface reconstruction, and other point cloud processing tasks. 2013 Conference Paper http://hdl.handle.net/20.500.11937/8020 IEEE Inc. restricted |
| spellingShingle | point cloud surface reconstruction robust normal segmentation Feature extraction outlier Nurunnabi, Abdul Belton, David West, Geoff Diagnostics based Principal Component Analysis for Robust Plane Fitting in Laser Data |
| title | Diagnostics based Principal Component Analysis for Robust Plane Fitting in Laser Data |
| title_full | Diagnostics based Principal Component Analysis for Robust Plane Fitting in Laser Data |
| title_fullStr | Diagnostics based Principal Component Analysis for Robust Plane Fitting in Laser Data |
| title_full_unstemmed | Diagnostics based Principal Component Analysis for Robust Plane Fitting in Laser Data |
| title_short | Diagnostics based Principal Component Analysis for Robust Plane Fitting in Laser Data |
| title_sort | diagnostics based principal component analysis for robust plane fitting in laser data |
| topic | point cloud surface reconstruction robust normal segmentation Feature extraction outlier |
| url | http://hdl.handle.net/20.500.11937/8020 |