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Deriving precise orchard maps for unmanned ground vehicles from UAV images

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dc.contributor.authorSchuette, Tjark
dc.contributor.authorDworak, Volker
dc.contributor.authorWeltzien, Cornelia
dc.contributor.editorGandorfer, Markus
dc.contributor.editorHoffmann, Christa
dc.contributor.editorEl Benni, Nadja
dc.contributor.editorCockburn, Marianne
dc.contributor.editorAnken, Thomas
dc.contributor.editorFloto, Helga
dc.date.accessioned2022-02-24T13:34:51Z
dc.date.available2022-02-24T13:34:51Z
dc.date.issued2022
dc.description.abstractMapping and environment representation are two of the main challenges in agricultural robotics and are vital to navigation tasks like localisation and path planning. In this work, we present a new method that enables the offline creation of orchard maps for unmanned ground vehicles based on unmanned aerial vehicle imagery. We employ photogrammetry to generate high-resolution 3D point clouds from aerial images. A cloth simulation filter is then used to classify ground and off-ground points. In order to obtain detailed probabilistic occupancy grid maps, per cell statistics are evaluated. First results show promising performance when compared to ground truth positions of orchard bushes and manual labelling.en
dc.identifier.isbn978-3-88579-711-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/38410
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-317
dc.subjectPrecision Horticulture
dc.subjectphotogrammetry
dc.subjectUAV
dc.subjectmapping
dc.subjectUGV navigation
dc.titleDeriving precise orchard maps for unmanned ground vehicles from UAV imagesen
dc.typeText/Conference Paper
gi.citation.endPage276
gi.citation.publisherPlaceBonn
gi.citation.startPage271
gi.conference.date21.-22. Februar 2022
gi.conference.locationTänikon, Online

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P317 - 42. GIL-Jahrestagung 2022 - Fokus: Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft