Show simple item record

dc.contributor.authorYandun, Francisco J.
dc.contributor.authorGregorio López, Eduard
dc.contributor.authorZúñiga, Marcos
dc.contributor.authorEscolà i Agustí, Alexandre
dc.contributor.authorRosell Polo, Joan Ramon
dc.contributor.authorAuat Cheein, Fernando A.
dc.date.accessioned2017-03-27T07:07:31Z
dc.date.available2017-03-27T07:07:31Z
dc.date.issued2016
dc.identifier.issn2405-8963
dc.identifier.urihttp://hdl.handle.net/10459.1/59397
dc.description.abstractThe detection of the type of soil surface where a robotic vehicle is navigating on is an important issue for performing several agricultural tasks. Satisfactory results in activities such as seeding, plowing, fertilizing, among others depend on a correct identification of the vehicle environment, specially its contact interface with the ground. In the this work, the implementation of a supervised image texture classifier to recognize five different classes of typical agricultural soil surfaces is presented and analysed. The sensing device is the Microsoft Kinect for Windows V2, which allows to acquire RGB, IR and depth data. Only IR and depth data were used for the processing, since color information becomes unreliable under different illumination conditions. Two data acquisition modes allowed to validate and to apply the system in real operation conditions. The accuracy of the classifier was assessed under different configuration parameters, obtaining up to 93 percent of success rate, in ideal conditions. Real field conditions were simulated by placing the sensor over a moving wagon, obtaining up to 86 percent of success rate, showing in this way the usability of a low cost sensor such as the Kinect V2 for agricultural robotics.ca_ES
dc.language.isoengca_ES
dc.publisherElsevierca_ES
dc.publisherIFAC (International Federation of Automatic Control)ca_ES
dc.relation.isformatofVersió postprint del document publicat a https://doi.org/10.1016/j.ifacol.2016.10.083ca_ES
dc.relation.ispartofIFAC-PapersOnLine, 2016, vol. 49, núm. 16, p. 457-462ca_ES
dc.rights(c) IFAC (International Federation of Automatic Control) Hosting by Elsevier, 2016ca_ES
dc.subjectAgricultural roboticsca_ES
dc.subjectTerrain classificationca_ES
dc.subjectTerramechanics modellingca_ES
dc.subjectPattern recognitionca_ES
dc.titleClassifying Agricultural Terrain for Machinery Traversability Purposesca_ES
dc.typearticleca_ES
dc.identifier.idgrec024838
dc.type.versionacceptedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.1016/j.ifacol.2016.10.083


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record