Exploring the Frequency Domain Point Cloud Processing for Localisation Purposes in Arboreal Environments

Simple item page
dc.catalogadorvdr
dc.contributor.authorDevanna, Rosa Pia
dc.contributor.authorTorres Torriti, Miguel Attilio
dc.contributor.authorSacilik, Kamil
dc.contributor.authorCetin, Necati
dc.contributor.authorAuat Cheein, Fernando
dc.date.accessioned2025-08-29T17:16:42Z
dc.date.available2025-08-29T17:16:42Z
dc.date.issued2025
dc.description.abstractPoint clouds from 3D sensors such as LiDAR are increasingly used in agriculture for tasks like crop characterisation, pest detection, and leaf area estimation. While traditional point cloud processing typically occurs in Cartesian space using methods such as principal component analysis (PCA), this paper introduces a novel frequency-domain approach for point cloud registration. The central idea is that point clouds can be transformed and analysed in the spectral domain, where key frequency components capture the most informative spatial structures. By selecting and registering only the dominant frequencies, our method achieves significant reductions in localisation error and computational complexity. We validate this approach using public datasets and compare it with standard Iterative Closest Point (ICP) techniques. Our method, which applies ICP only to points in selected frequency bands, reduces localisation error from 4.37 m to 1.22 m (MSE), an improvement of approximately 72%. These findings highlight the potential of frequency-domain analysis as a powerful and efficient tool for point cloud registration in agricultural and other GNSS-challenged environments.
dc.fechaingreso.objetodigital2025-08-29
dc.format.extent15 páginas
dc.fuente.origenORCID
dc.identifier.doi10.3390/a18080522
dc.identifier.urihttps://doi.org/10.3390/a18080522
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/105386
dc.issue.numero8
dc.language.isoen
dc.nota.accesoContenido completo
dc.pagina.final15
dc.pagina.inicio1
dc.revistaAlgorithms
dc.rightsacceso abierto
dc.rights.licenseCC BY 4.0 Attribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectPoint cloud processing
dc.subjectFrequency domain
dc.subjectLocalisation
dc.subjectAutonomous machinery
dc.subjectAgricultural applications
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.subject.ods09 Industry, innovation and infrastructure
dc.subject.odspa09 Industria, innovación e infraestructura
dc.titleExploring the Frequency Domain Point Cloud Processing for Localisation Purposes in Arboreal Environments
dc.typeartículo
dc.volumen18
Files
Collections
Artículos de revistas