The aim of this work is to define a procedure for the automatic labeling of images used for the training of a deep neural network which is used to learn a direct mapping from images to steering angles and collision probabilities. A state-of-the-art convolutional neural network for robotic vehicle navigation is used, which has been adapted to work for ground vehicles. Steering angles and collision probabilities are then used to generate respectively the angular and the linear velocity commands to drive a tracked vehicle through rough unstructured terrains, as those typically encountered in agricultural applications.


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    Title :

    Automatic Image Labelling for Deep-Learning-Based Navigation of Agricultural Robots


    Additional title:

    Lecture Notes in Civil Engineering



    Conference:

    International Conference on Safety, Health and Welfare in Agriculture and Agro-food Systems ; 2020 ; Ragusa, Italy September 16, 2020 - September 19, 2020



    Publication date :

    2022-03-23


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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