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Auflistung nach Schlagwort "photogrammetry"

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  • Konferenzbeitrag
    Deriving precise orchard maps for unmanned ground vehicles from UAV images
    (42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft, 2022) Schuette, Tjark; Dworak, Volker; Weltzien, Cornelia
    Mapping 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.
  • Konferenzbeitrag
    For5G: Systematic approach for creating digital twins of cherry orchards
    (43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme, 2023) Meyer, Lukas; Gilson, Andreas; Uhrmann, Franz; Weule, Mareike; Keil, Fabian; Haunschild, Bernhard; Oschek, Joachim; Steglich, Marco; Hansen, Jonathan; Stamminger, Marc; Scholz, Oliver
    We present a systematic approach for creating digital twins of cherry trees in orchards as part of the project “For5G: Digital Twin”. We aim to develop a basic concept for 5G applications in orchards using a mobile campus network. Digital twins monitor the status of individual trees in every aspect and are a crucial step for the digitalization of processes in horticulture. Our framework incorporates a transformation of photometric data to a 3D reconstruction, which is subsequently segmented and modeled using learning-based approaches. Collecting objective phenotypic features from individual trees over time and storing them in a knowledge graph offers a convenient foundation for gaining new insights. Our approach shows promising results at this point for creating a detailed digital twin of a cherry tree and ultimately the entire orchard.