UAV Data Collection Co-Registration: LiDAR and Photogrammetric Surveys for Coastal Monitoring
When georeferencing is a key point of coastal monitoring, it is crucial to understand how the type of data and object characteristics can affect the result of the registration procedure, and, above all, how to assess the reconstruction accuracy. For this reason, the goal of this work is to evaluate...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/49 |
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| author | Carmen Maria Giordano Valentina Alena Girelli Alessandro Lambertini Maria Alessandra Tini Antonio Zanutta |
| author_facet | Carmen Maria Giordano Valentina Alena Girelli Alessandro Lambertini Maria Alessandra Tini Antonio Zanutta |
| author_sort | Carmen Maria Giordano |
| collection | DOAJ |
| description | When georeferencing is a key point of coastal monitoring, it is crucial to understand how the type of data and object characteristics can affect the result of the registration procedure, and, above all, how to assess the reconstruction accuracy. For this reason, the goal of this work is to evaluate the performance of the iterative closest point (ICP) method for registering point clouds in coastal environments, using a single-epoch and multi-sensor survey of a coastal area (near the Bevano river mouth, Ravenna, Italy). The combination of multiple drone datasets (LiDAR and photogrammetric clouds) is performed via indirect georeferencing, using different executions of the ICP procedure. The ICP algorithm is affected by the differences in the vegetation reconstruction by the two sensors, which may lead to a rotation of the slave cloud. While the dissimilarities between the two clouds can be minimized, reducing their impact, the lack of object distinctiveness, typical of environmental objects, remains a problem that cannot be overcome. This work addresses the use of the ICP method for registering point clouds representative of coastal environments, with some limitations related to the required presence of stable areas between the clouds and the potential errors associated with featureless surfaces. |
| format | Article |
| id | doaj-art-149cff8eae74406b9c9a33fdc178ca9b |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-149cff8eae74406b9c9a33fdc178ca9b2025-01-24T13:29:47ZengMDPI AGDrones2504-446X2025-01-01914910.3390/drones9010049UAV Data Collection Co-Registration: LiDAR and Photogrammetric Surveys for Coastal MonitoringCarmen Maria Giordano0Valentina Alena Girelli1Alessandro Lambertini2Maria Alessandra Tini3Antonio Zanutta4Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale Risorgimento 2, 40136 Bologna, ItalyDepartment of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale Risorgimento 2, 40136 Bologna, ItalyDepartment of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale Risorgimento 2, 40136 Bologna, ItalyDepartment of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale Risorgimento 2, 40136 Bologna, ItalyDepartment of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale Risorgimento 2, 40136 Bologna, ItalyWhen georeferencing is a key point of coastal monitoring, it is crucial to understand how the type of data and object characteristics can affect the result of the registration procedure, and, above all, how to assess the reconstruction accuracy. For this reason, the goal of this work is to evaluate the performance of the iterative closest point (ICP) method for registering point clouds in coastal environments, using a single-epoch and multi-sensor survey of a coastal area (near the Bevano river mouth, Ravenna, Italy). The combination of multiple drone datasets (LiDAR and photogrammetric clouds) is performed via indirect georeferencing, using different executions of the ICP procedure. The ICP algorithm is affected by the differences in the vegetation reconstruction by the two sensors, which may lead to a rotation of the slave cloud. While the dissimilarities between the two clouds can be minimized, reducing their impact, the lack of object distinctiveness, typical of environmental objects, remains a problem that cannot be overcome. This work addresses the use of the ICP method for registering point clouds representative of coastal environments, with some limitations related to the required presence of stable areas between the clouds and the potential errors associated with featureless surfaces.https://www.mdpi.com/2504-446X/9/1/49UAVphotogrammetryLiDARco-registrationmonitoringcoastal survey |
| spellingShingle | Carmen Maria Giordano Valentina Alena Girelli Alessandro Lambertini Maria Alessandra Tini Antonio Zanutta UAV Data Collection Co-Registration: LiDAR and Photogrammetric Surveys for Coastal Monitoring Drones UAV photogrammetry LiDAR co-registration monitoring coastal survey |
| title | UAV Data Collection Co-Registration: LiDAR and Photogrammetric Surveys for Coastal Monitoring |
| title_full | UAV Data Collection Co-Registration: LiDAR and Photogrammetric Surveys for Coastal Monitoring |
| title_fullStr | UAV Data Collection Co-Registration: LiDAR and Photogrammetric Surveys for Coastal Monitoring |
| title_full_unstemmed | UAV Data Collection Co-Registration: LiDAR and Photogrammetric Surveys for Coastal Monitoring |
| title_short | UAV Data Collection Co-Registration: LiDAR and Photogrammetric Surveys for Coastal Monitoring |
| title_sort | uav data collection co registration lidar and photogrammetric surveys for coastal monitoring |
| topic | UAV photogrammetry LiDAR co-registration monitoring coastal survey |
| url | https://www.mdpi.com/2504-446X/9/1/49 |
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