A Multisensor UAV Payload and Processing Pipeline for Generating Multispectral Point Clouds
Over the last two decades, UAVs have become an indispensable acquisition platform in the remote sensing community. Meanwhile, advanced lightweight sensors have been introduced in the market, including LiDAR scanners with multiple beams and hyperspectral cameras measuring reflectance using many diffe...
| الحاوية / القاعدة: | Remote Sensing |
|---|---|
| المؤلفون الرئيسيون: | , , , , , , |
| التنسيق: | مقال |
| اللغة: | الإنجليزية |
| منشور في: |
MDPI AG
2023-03-01
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www.mdpi.com/2072-4292/15/6/1524 |
| _version_ | 1850385214417666048 |
|---|---|
| author | Michiel Vlaminck Laurens Diels Wilfried Philips Wouter Maes René Heim Bart De Wit Hiep Luong |
| author_facet | Michiel Vlaminck Laurens Diels Wilfried Philips Wouter Maes René Heim Bart De Wit Hiep Luong |
| author_sort | Michiel Vlaminck |
| collection | DOAJ |
| container_title | Remote Sensing |
| description | Over the last two decades, UAVs have become an indispensable acquisition platform in the remote sensing community. Meanwhile, advanced lightweight sensors have been introduced in the market, including LiDAR scanners with multiple beams and hyperspectral cameras measuring reflectance using many different narrow-banded filters. To date, however, few fully fledged drone systems exist that combine different sensing modalities in a way that complements the strengths and weaknesses of each. In this paper, we present our multimodal drone payload and sensor fusion pipeline, which allows multispectral point clouds to be generated at subcentimeter accuracy. To that end, we combine high-frequency navigation outputs from a professional-grade GNSS with photogrammetric bundle adjustment and a dedicated point cloud registration algorithm that takes full advantage of LiDAR’s specifications. We demonstrate that the latter significantly improves the quality of the reconstructed point cloud in terms of fewer ghosting effects and less noise. Finally, we thoroughly discuss the impact of the quality of the GNSS/INS system on the structure from the motion and LiDAR SLAM reconstruction process. |
| format | Article |
| id | doaj-art-4d211aa7dc5d47ce927dbfcc33dc3df6 |
| institution | Directory of Open Access Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| spelling | doaj-art-4d211aa7dc5d47ce927dbfcc33dc3df62025-08-19T22:55:52ZengMDPI AGRemote Sensing2072-42922023-03-01156152410.3390/rs15061524A Multisensor UAV Payload and Processing Pipeline for Generating Multispectral Point CloudsMichiel Vlaminck0Laurens Diels1Wilfried Philips2Wouter Maes3René Heim4Bart De Wit5Hiep Luong6IPI-URC-imec, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, BelgiumIPI-URC-imec, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, BelgiumIPI-URC-imec, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, BelgiumDepartment of Plants and Crops—URC, Ghent University, Coupure Links 653, 9000 Ghent, BelgiumInstitut für Zuckerrübenforschung An der Universität Göttingen, Holtenser Landstraße 77, D-37079 Göttingen, GermanyDepartment of Geography, Ghent University, Krijgslaan 281 S8, 9000 Ghent, BelgiumIPI-URC-imec, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, BelgiumOver the last two decades, UAVs have become an indispensable acquisition platform in the remote sensing community. Meanwhile, advanced lightweight sensors have been introduced in the market, including LiDAR scanners with multiple beams and hyperspectral cameras measuring reflectance using many different narrow-banded filters. To date, however, few fully fledged drone systems exist that combine different sensing modalities in a way that complements the strengths and weaknesses of each. In this paper, we present our multimodal drone payload and sensor fusion pipeline, which allows multispectral point clouds to be generated at subcentimeter accuracy. To that end, we combine high-frequency navigation outputs from a professional-grade GNSS with photogrammetric bundle adjustment and a dedicated point cloud registration algorithm that takes full advantage of LiDAR’s specifications. We demonstrate that the latter significantly improves the quality of the reconstructed point cloud in terms of fewer ghosting effects and less noise. Finally, we thoroughly discuss the impact of the quality of the GNSS/INS system on the structure from the motion and LiDAR SLAM reconstruction process.https://www.mdpi.com/2072-4292/15/6/1524LiDAR scanningSLAMpoint cloudslocalizationmappingmultispectral imaging |
| spellingShingle | Michiel Vlaminck Laurens Diels Wilfried Philips Wouter Maes René Heim Bart De Wit Hiep Luong A Multisensor UAV Payload and Processing Pipeline for Generating Multispectral Point Clouds LiDAR scanning SLAM point clouds localization mapping multispectral imaging |
| title | A Multisensor UAV Payload and Processing Pipeline for Generating Multispectral Point Clouds |
| title_full | A Multisensor UAV Payload and Processing Pipeline for Generating Multispectral Point Clouds |
| title_fullStr | A Multisensor UAV Payload and Processing Pipeline for Generating Multispectral Point Clouds |
| title_full_unstemmed | A Multisensor UAV Payload and Processing Pipeline for Generating Multispectral Point Clouds |
| title_short | A Multisensor UAV Payload and Processing Pipeline for Generating Multispectral Point Clouds |
| title_sort | multisensor uav payload and processing pipeline for generating multispectral point clouds |
| topic | LiDAR scanning SLAM point clouds localization mapping multispectral imaging |
| url | https://www.mdpi.com/2072-4292/15/6/1524 |
| work_keys_str_mv | AT michielvlaminck amultisensoruavpayloadandprocessingpipelineforgeneratingmultispectralpointclouds AT laurensdiels amultisensoruavpayloadandprocessingpipelineforgeneratingmultispectralpointclouds AT wilfriedphilips amultisensoruavpayloadandprocessingpipelineforgeneratingmultispectralpointclouds AT woutermaes amultisensoruavpayloadandprocessingpipelineforgeneratingmultispectralpointclouds AT reneheim amultisensoruavpayloadandprocessingpipelineforgeneratingmultispectralpointclouds AT bartdewit amultisensoruavpayloadandprocessingpipelineforgeneratingmultispectralpointclouds AT hiepluong amultisensoruavpayloadandprocessingpipelineforgeneratingmultispectralpointclouds AT michielvlaminck multisensoruavpayloadandprocessingpipelineforgeneratingmultispectralpointclouds AT laurensdiels multisensoruavpayloadandprocessingpipelineforgeneratingmultispectralpointclouds AT wilfriedphilips multisensoruavpayloadandprocessingpipelineforgeneratingmultispectralpointclouds AT woutermaes multisensoruavpayloadandprocessingpipelineforgeneratingmultispectralpointclouds AT reneheim multisensoruavpayloadandprocessingpipelineforgeneratingmultispectralpointclouds AT bartdewit multisensoruavpayloadandprocessingpipelineforgeneratingmultispectralpointclouds AT hiepluong multisensoruavpayloadandprocessingpipelineforgeneratingmultispectralpointclouds |