Uncertainty analysis of a LiDAR-based MTLS point cloud using a high-resolution ground-truth
| dc.contributor.author | Lavaquiol Colell, Bernat | |
| dc.contributor.author | Llorens Calveras, Jordi | |
| dc.contributor.author | Sanz Cortiella, Ricardo | |
| dc.contributor.author | Arnó Satorra, Jaume | |
| dc.contributor.author | Escolà i Agustí, Alexandre | |
| dc.date.accessioned | 2023-08-02T07:03:16Z | |
| dc.date.available | 2023-08-02T07:03:16Z | |
| dc.date.issued | 2023 | |
| dc.description | Poster Proceedings of the 14th European Conference on Precision Agriculture, 2-6 July 2023, Bologna, Italy | |
| dc.description.abstract | The study of plant geometry is crucial to design specific management by providing the optimal quantities of nutrients, fertilizers, pesticides and irrigation rates. Before the advent of the first 3D characterization systems, it was very laborious to obtain accurate commercial scale 3D crop data. Nowadays, there are sensing systems which allow 3D canopy characterization to be performed in a relatively simple and fast way. LiDAR (light detection and ranging) sensors have been widely used in agriculture. When 3D scanning techniques are used, it is essential to be aware of the total measurement error. One of the limitations when using real data is the absence of ground-truth (GT) to compare the obtained measurements . In a previous research [1], validated a high-resolution 3D point cloud on an actual defoliated tree obtained from RGB images and stereo-photogrammetry techniques. This accurate 3D point cloud can be used as digital ground-truth (DGT) to validate 3D LiDAR point. The accuracy of the scanning system includes the errors committed by the sensor, the positioning system (GNSS), the data acquisition set up, the point cloud generation algorithms and the georeferentiation of the DGT. | |
| dc.description.sponsorship | The present study is part of the PAgPROTECT project PID2021-126648OB-I00 funded by the Spanish Ministry of Science and Innovation / AEI /10.13039/501100011033 / FEDER, UE. | |
| dc.identifier.uri | https://repositori.udl.cat/handle/10459.1/463872 | |
| dc.language.iso | eng | |
| dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/PID2021-126648OB-I00/ES/PROTECCION DE CULTIVOS DE PRECISION PARA CONSEGUIR OBJETIVOS DEL PACTO VERDE EUROPEO EN USO EFICIENTE Y REDUCCION DE FITOSANITARIOS MEDIANTE AGRICULTURA DE PRECISION/ | |
| dc.relation.ispartof | The 14th European Conference on Precision Agriculture. 2-6 July 2023, Bologna, Italy. (https://www.ecpa2023.it/) | |
| dc.rights | (c) Bernat Lavaquiol et al., 2023 | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | LiDAR-based MTLS point clouds | |
| dc.subject | Highresolution ground-truth | |
| dc.title | Uncertainty analysis of a LiDAR-based MTLS point cloud using a high-resolution ground-truth | |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dc.type.version | info:eu-repo/semantics/publishedVersion |