Precision Agriculture (PA) is now a term used throughout the agricultural domain worldwide. It gained popularity and increasing interest from the research community due to the wide range of potential benefits and to the availability of new off-the-shelf sensing technologies. PA methods, indeed, promise to increase the quantity and quality of agricultural outputs, while using less input (e.g., water, energy, fertilizers, pesticides, . . . ). The aim is to save costs, reduce environmental impact and produce more and better food. In this domain, a promising solution that is rapidly growing up is robotic farming. By combining the aerial survey capabilities of Unmanned Aerial Vehicles (UAVs) with multi-purpose agricultural Unmanned Ground Vehicles (UGVs), a robotic system will be able to survey a field from the air, perform a targeted intervention on the ground, and provide detailed information for decision support, all with minimal user intervention. In the last years, despite great progress in automating farming activities by using robotic platforms, most of the existing systems do not provide a sufficient autonomy level. Making farming robots more autonomous brings the benefits of completing tasks faster and adapting to different purposes and farm fields, which make them more useful and increase their profitability. However, making farming robots more autonomous involves increasing their perception and awareness of their surrounding environment. A typical agricultural scenario presents unique characteristics, such as highly repetitive visual and geometrical patterns, and the lack of distinguishable landmarks. These features do not allow to directly apply most of the state-of-the-art perception methods from other robotic domains. This thesis focuses on perception methods that enable robots to autonomously operate in farming environments, specifically a localization method and a collaborative mapping between aerial and ground robots. They improve the robot perception capabilities by exploiting the unique ...


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    Titel :

    Perception and environment modeling in robotic agriculture contexts



    Erscheinungsdatum :

    2020-02-28


    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




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