Robust and Diagnostic Statistics: A Few Basic Concepts in Mobile Mapping Point Cloud Data Analysis
It is impractical to imagine point cloud data obtained from laser scanner based mobile mapping systems without outliers. The presence of outliers affects the most often used classical statistical techniques used in laser scanning point cloud data analysis and hence the consequent results of point cl...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
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Higher education quality enhancement program (HEQEP)
2012
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/35221 |
| _version_ | 1848754437783289856 |
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| author | Nurunnabi, A. Belton, David West, Geoff |
| author2 | M. Abul Basher Mian |
| author_facet | M. Abul Basher Mian Nurunnabi, A. Belton, David West, Geoff |
| author_sort | Nurunnabi, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | It is impractical to imagine point cloud data obtained from laser scanner based mobile mapping systems without outliers. The presence of outliers affects the most often used classical statistical techniques used in laser scanning point cloud data analysis and hence the consequent results of point cloud processing are inaccurate and non-robust. Therefore, it is necessary to use robust and/or diagnostic statistical methods for reliable estimates, modelling, fitting and feature extraction. In spite of the limitations of classical statistical methods, an extensive literature search shows not much use of robust techniques in point cloud data analysis. This paper presents the basic ideas on mobile mapping technology and point cloud data, investigates outlier problems and presents some applicable robust and diagnostic statistical approaches. Importance and performance of robust and diagnostic techniques are shown for planar surface fitting and surface segmentation by using several mobile mapping real point cloud data examples. |
| first_indexed | 2025-11-14T08:40:24Z |
| format | Conference Paper |
| id | curtin-20.500.11937-35221 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:40:24Z |
| publishDate | 2012 |
| publisher | Higher education quality enhancement program (HEQEP) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-352212017-01-30T13:48:20Z Robust and Diagnostic Statistics: A Few Basic Concepts in Mobile Mapping Point Cloud Data Analysis Nurunnabi, A. Belton, David West, Geoff M. Abul Basher Mian plane fitting outlier detection feature extraction mobile mappingtechnology robust statistics segmentation M-estimator laser scanning 3D modelling PCA covariance technique It is impractical to imagine point cloud data obtained from laser scanner based mobile mapping systems without outliers. The presence of outliers affects the most often used classical statistical techniques used in laser scanning point cloud data analysis and hence the consequent results of point cloud processing are inaccurate and non-robust. Therefore, it is necessary to use robust and/or diagnostic statistical methods for reliable estimates, modelling, fitting and feature extraction. In spite of the limitations of classical statistical methods, an extensive literature search shows not much use of robust techniques in point cloud data analysis. This paper presents the basic ideas on mobile mapping technology and point cloud data, investigates outlier problems and presents some applicable robust and diagnostic statistical approaches. Importance and performance of robust and diagnostic techniques are shown for planar surface fitting and surface segmentation by using several mobile mapping real point cloud data examples. 2012 Conference Paper http://hdl.handle.net/20.500.11937/35221 Higher education quality enhancement program (HEQEP) restricted |
| spellingShingle | plane fitting outlier detection feature extraction mobile mappingtechnology robust statistics segmentation M-estimator laser scanning 3D modelling PCA covariance technique Nurunnabi, A. Belton, David West, Geoff Robust and Diagnostic Statistics: A Few Basic Concepts in Mobile Mapping Point Cloud Data Analysis |
| title | Robust and Diagnostic Statistics: A Few Basic Concepts in Mobile Mapping Point Cloud Data Analysis |
| title_full | Robust and Diagnostic Statistics: A Few Basic Concepts in Mobile Mapping Point Cloud Data Analysis |
| title_fullStr | Robust and Diagnostic Statistics: A Few Basic Concepts in Mobile Mapping Point Cloud Data Analysis |
| title_full_unstemmed | Robust and Diagnostic Statistics: A Few Basic Concepts in Mobile Mapping Point Cloud Data Analysis |
| title_short | Robust and Diagnostic Statistics: A Few Basic Concepts in Mobile Mapping Point Cloud Data Analysis |
| title_sort | robust and diagnostic statistics: a few basic concepts in mobile mapping point cloud data analysis |
| topic | plane fitting outlier detection feature extraction mobile mappingtechnology robust statistics segmentation M-estimator laser scanning 3D modelling PCA covariance technique |
| url | http://hdl.handle.net/20.500.11937/35221 |