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Camera-Aided Orientation of Mobile Lidar Point Clouds Acquired from an Uncrewed Water Vehicle
This article presents a system for recording 3D point clouds of riverbanks with a mobile lidar mounted on an uncrewed water vehicle. The focus is on the orientation of the platform and the lidar sensor. Rivers are areas where the conditions for highly accurate GNSS can be sub-optimal due to multipat...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346613/ https://www.ncbi.nlm.nih.gov/pubmed/37447858 http://dx.doi.org/10.3390/s23136009 |
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author | Sardemann, Hannes Blaskow, Robert Maas, Hans-Gerd |
author_facet | Sardemann, Hannes Blaskow, Robert Maas, Hans-Gerd |
author_sort | Sardemann, Hannes |
collection | PubMed |
description | This article presents a system for recording 3D point clouds of riverbanks with a mobile lidar mounted on an uncrewed water vehicle. The focus is on the orientation of the platform and the lidar sensor. Rivers are areas where the conditions for highly accurate GNSS can be sub-optimal due to multipath effects from the water and shadowing effects by bridges, steep valleys, trees, or other objects at the riverbanks. Furthermore, a small measurement platform may have an effect on the accuracy of orientations measured by an IMU; for instance, caused by electromagnetic fields emitted by the boat rotors, the lidar, and other hardware decreasing IMU accuracy. As an alternative, we use exterior orientation parameters obtained by photogrammetric methods from the images of a camera on the boat capturing the riverbanks in time-lapse mode. Using control points and tie points on the riverbanks enables georeferenced position and orientation determination from the image data, which can then be used to transform the lidar data into a global coordinate system. The main influences on the accuracy of the camera orientations are the distance to the riverbanks, the size of the banks, and the amount of vegetation on them. Moreover, the quality of the camera orientation-based lidar point cloud also depends on the time synchronization of camera and lidar. The paper describes the data processing steps for the geometric lidar–camera integration and delivers a validation of the accuracy potential. For quality assessment of a point cloud acquired with the described method, a comparison with terrestrial laser scanning has been carried out. |
format | Online Article Text |
id | pubmed-10346613 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103466132023-07-15 Camera-Aided Orientation of Mobile Lidar Point Clouds Acquired from an Uncrewed Water Vehicle Sardemann, Hannes Blaskow, Robert Maas, Hans-Gerd Sensors (Basel) Article This article presents a system for recording 3D point clouds of riverbanks with a mobile lidar mounted on an uncrewed water vehicle. The focus is on the orientation of the platform and the lidar sensor. Rivers are areas where the conditions for highly accurate GNSS can be sub-optimal due to multipath effects from the water and shadowing effects by bridges, steep valleys, trees, or other objects at the riverbanks. Furthermore, a small measurement platform may have an effect on the accuracy of orientations measured by an IMU; for instance, caused by electromagnetic fields emitted by the boat rotors, the lidar, and other hardware decreasing IMU accuracy. As an alternative, we use exterior orientation parameters obtained by photogrammetric methods from the images of a camera on the boat capturing the riverbanks in time-lapse mode. Using control points and tie points on the riverbanks enables georeferenced position and orientation determination from the image data, which can then be used to transform the lidar data into a global coordinate system. The main influences on the accuracy of the camera orientations are the distance to the riverbanks, the size of the banks, and the amount of vegetation on them. Moreover, the quality of the camera orientation-based lidar point cloud also depends on the time synchronization of camera and lidar. The paper describes the data processing steps for the geometric lidar–camera integration and delivers a validation of the accuracy potential. For quality assessment of a point cloud acquired with the described method, a comparison with terrestrial laser scanning has been carried out. MDPI 2023-06-28 /pmc/articles/PMC10346613/ /pubmed/37447858 http://dx.doi.org/10.3390/s23136009 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sardemann, Hannes Blaskow, Robert Maas, Hans-Gerd Camera-Aided Orientation of Mobile Lidar Point Clouds Acquired from an Uncrewed Water Vehicle |
title | Camera-Aided Orientation of Mobile Lidar Point Clouds Acquired from an Uncrewed Water Vehicle |
title_full | Camera-Aided Orientation of Mobile Lidar Point Clouds Acquired from an Uncrewed Water Vehicle |
title_fullStr | Camera-Aided Orientation of Mobile Lidar Point Clouds Acquired from an Uncrewed Water Vehicle |
title_full_unstemmed | Camera-Aided Orientation of Mobile Lidar Point Clouds Acquired from an Uncrewed Water Vehicle |
title_short | Camera-Aided Orientation of Mobile Lidar Point Clouds Acquired from an Uncrewed Water Vehicle |
title_sort | camera-aided orientation of mobile lidar point clouds acquired from an uncrewed water vehicle |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346613/ https://www.ncbi.nlm.nih.gov/pubmed/37447858 http://dx.doi.org/10.3390/s23136009 |
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