Scan profiles based method for segmentation and extraction of planar objects in mobile laser scanning point clouds
The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: They can b...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/40450 |
| _version_ | 1848755874583019520 |
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| author | Nguyen, H. Belton, D. Helmholz, Petra |
| author_facet | Nguyen, H. Belton, D. Helmholz, Petra |
| author_sort | Nguyen, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: They can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method. |
| first_indexed | 2025-11-14T09:03:14Z |
| format | Conference Paper |
| id | curtin-20.500.11937-40450 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:03:14Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-404502017-09-13T13:39:37Z Scan profiles based method for segmentation and extraction of planar objects in mobile laser scanning point clouds Nguyen, H. Belton, D. Helmholz, Petra The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: They can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method. 2016 Conference Paper http://hdl.handle.net/20.500.11937/40450 10.5194/isprsarchives-XLI-B3-351-2016 unknown |
| spellingShingle | Nguyen, H. Belton, D. Helmholz, Petra Scan profiles based method for segmentation and extraction of planar objects in mobile laser scanning point clouds |
| title | Scan profiles based method for segmentation and extraction of planar objects in mobile laser scanning point clouds |
| title_full | Scan profiles based method for segmentation and extraction of planar objects in mobile laser scanning point clouds |
| title_fullStr | Scan profiles based method for segmentation and extraction of planar objects in mobile laser scanning point clouds |
| title_full_unstemmed | Scan profiles based method for segmentation and extraction of planar objects in mobile laser scanning point clouds |
| title_short | Scan profiles based method for segmentation and extraction of planar objects in mobile laser scanning point clouds |
| title_sort | scan profiles based method for segmentation and extraction of planar objects in mobile laser scanning point clouds |
| url | http://hdl.handle.net/20.500.11937/40450 |