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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1780
DC FieldValueLanguage
dc.contributor.authorLe Thanh Vu Truen_US
dc.date.accessioned2024-03-14T04:21:13Z-
dc.date.accessioned2024-03-14T04:21:17Z-
dc.date.available2024-03-14T04:21:13Z-
dc.date.available2024-03-14T04:21:17Z-
dc.date.issued2024-
dc.identifier.urihttps://epub.vgu.edu.vn/handle/dlibvgu/1780-
dc.description.abstractThis thesis delves into the development and integration of a georeferenced point cloud generation system, centering on the Velodyne VLP-16 LiDAR unit mounted on a UAV platform, specifically the DJI Matrice M200. The objective is to create a LAS-formatted point cloud dataset adhering to specified standards. Components, including a Raspberry Pi 4, LiDAR sensor, IMU, and GPS sensor, contribute to autonomous data gathering during UAV surveys. Challenges such as time synchronization, recalibration, and error identification are addressed to ensure a cohesive hardware and software system. The Robotic Operating System (ROS) facilitates data collection during flight missions, and accuracy is verified through comparisons with reference points. The methodology encompasses the intricacies of direct geo-referencing for 3D point clouds, covering data collection using remote sensing techniques, sensor integration, and transformation of sensor-centric to geographical coordinates through sensor fusion and calibration. A topographic 3D point cloud map is generated, validated through comparative analyses with photogrammetric survey topographic maps. The findings showcase the LiDAR system's exceptional spatial attribute capture, with a maximal error of 3.75 meters and an RMSE of 1.85 meters. The georeferenced point cloud exhibits an RMSE of 0.92 meters, demonstrating high precision and accuracy. This research contributes significant insights into georeferenced LiDAR-based point cloud generation systems, providing a foundation for enhanced spatial mapping and analysis applicationsen_US
dc.language.isoenen_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectLiDAR technologyen_US
dc.subjectDirect geo-referencingen_US
dc.subjectKalman Filteren_US
dc.subjectReal-time kinematicen_US
dc.title3D point clouds terrain mapping system using lidar on UAV droneen_US
dc.typeThesisen_US
item.fulltextWith Fulltext-
item.languageiso639-1other-
item.grantfulltextrestricted-
Appears in Collections:Electrical Engineering and Information Technology (EEIT)
Electrical Engineering and Information Technology (EEIT)
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