Automated registration of unorganised point clouds from terrestrial laser scanners
Laser scanners provide a three-dimensional sampled representation of the surfaces of objects. The spatial resolution of the data is much higher than that of conventional surveying methods. The data collected from different locations of a laser scanner must be transformed into a common coordinate sys...
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| Format: | Thesis |
| Language: | English |
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Curtin University
2006
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| Online Access: | 17721 http://hdl.handle.net/20.500.11937/946 |
| _version_ | 1848743526972522496 |
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| author | Bae, Kwang-Ho |
| author_facet | Bae, Kwang-Ho |
| author_sort | Bae, Kwang-Ho |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Laser scanners provide a three-dimensional sampled representation of the surfaces of objects. The spatial resolution of the data is much higher than that of conventional surveying methods. The data collected from different locations of a laser scanner must be transformed into a common coordinate system. If good a priori alignment is provided and the point clouds share a large overlapping region, existing registration methods, such as the Iterative Closest Point (ICP) or Chen and Medioni’s method, work well. In practical applications of laser scanners, partially overlapping and unorganised point clouds are provided without good initial alignment. In these cases, the existing registration methods are not appropriate since it becomes very difficult to find the correspondence of the point clouds. A registration method, the Geometric Primitive ICP with the RANSAC (GPICPR), using geometric primitives, neighbourhood search, the positional uncertainty of laser scanners, and an outlier removal procedure is proposed in this thesis. The change of geometric curvature and approximate normal vector of the surface formed by a point and its neighbourhood are used for selecting the possible correspondences of point clouds. In addition, an explicit expression of the position uncertainty of measurement by laser scanners is presented in this dissertation and this position uncertainty is utilised to estimate the precision and accuracy of the estimated relative transformation parameters between point clouds. The GP-ICPR was tested with both simulated data and datasets from close range and terrestrial laser scanners in terms of its precision, accuracy, and convergence region. It was shown that the GP-ICPR improved the precision of the estimated relative transformation parameters as much as a factor of 5.In addition, the rotational convergence region of the GP-ICPR on the order of 10°, which is much larger than the ICP or its variants, provides a window of opportunity to utilise this automated registration method in practical applications such as terrestrial surveying and deformation monitoring. |
| first_indexed | 2025-11-14T05:46:59Z |
| format | Thesis |
| id | curtin-20.500.11937-946 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T05:46:59Z |
| publishDate | 2006 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-9462018-04-13T06:52:58Z Automated registration of unorganised point clouds from terrestrial laser scanners Bae, Kwang-Ho terrestrial laser scanner data close-range laser scanner data point clouds from laser scanners Laser scanners provide a three-dimensional sampled representation of the surfaces of objects. The spatial resolution of the data is much higher than that of conventional surveying methods. The data collected from different locations of a laser scanner must be transformed into a common coordinate system. If good a priori alignment is provided and the point clouds share a large overlapping region, existing registration methods, such as the Iterative Closest Point (ICP) or Chen and Medioni’s method, work well. In practical applications of laser scanners, partially overlapping and unorganised point clouds are provided without good initial alignment. In these cases, the existing registration methods are not appropriate since it becomes very difficult to find the correspondence of the point clouds. A registration method, the Geometric Primitive ICP with the RANSAC (GPICPR), using geometric primitives, neighbourhood search, the positional uncertainty of laser scanners, and an outlier removal procedure is proposed in this thesis. The change of geometric curvature and approximate normal vector of the surface formed by a point and its neighbourhood are used for selecting the possible correspondences of point clouds. In addition, an explicit expression of the position uncertainty of measurement by laser scanners is presented in this dissertation and this position uncertainty is utilised to estimate the precision and accuracy of the estimated relative transformation parameters between point clouds. The GP-ICPR was tested with both simulated data and datasets from close range and terrestrial laser scanners in terms of its precision, accuracy, and convergence region. It was shown that the GP-ICPR improved the precision of the estimated relative transformation parameters as much as a factor of 5.In addition, the rotational convergence region of the GP-ICPR on the order of 10°, which is much larger than the ICP or its variants, provides a window of opportunity to utilise this automated registration method in practical applications such as terrestrial surveying and deformation monitoring. 2006 Thesis http://hdl.handle.net/20.500.11937/946 en 17721 Curtin University fulltext |
| spellingShingle | terrestrial laser scanner data close-range laser scanner data point clouds from laser scanners Bae, Kwang-Ho Automated registration of unorganised point clouds from terrestrial laser scanners |
| title | Automated registration of unorganised point clouds from terrestrial laser scanners |
| title_full | Automated registration of unorganised point clouds from terrestrial laser scanners |
| title_fullStr | Automated registration of unorganised point clouds from terrestrial laser scanners |
| title_full_unstemmed | Automated registration of unorganised point clouds from terrestrial laser scanners |
| title_short | Automated registration of unorganised point clouds from terrestrial laser scanners |
| title_sort | automated registration of unorganised point clouds from terrestrial laser scanners |
| topic | terrestrial laser scanner data close-range laser scanner data point clouds from laser scanners |
| url | 17721 http://hdl.handle.net/20.500.11937/946 |