Selective weeding is one of the key challenges in the field of agriculture robotics. To accomplish this task, a farm robot should be able to accurately detect plants and to distinguish them between crop and weeds. Most of the promising state-of-the-art approaches make use of appearance-based models trained on large annotated datasets. Unfortunately, creating large agricultural datasets with pixel-level annotations is an extremely time consuming task, actually penalizing the usage of data-driven techniques.In this paper, we face this problem by proposing a novel and effective approach that aims to dramatically minimize the human intervention needed to train the detection and classification algorithms. The idea is to procedurally generate large synthetic training datasets randomizing the key features of the target environment (i.e., crop and weed species, type of soil, light conditions). More specifically, by tuning these model parameters, and exploiting a few real-world textures, it is possible to render a large amount of realistic views of an artificial agricultural scenario with no effort.The generated data can be directly used to train the model or to supplement real-world images. We validate the proposed methodology by using as testbed a modern deep learning based image segmentation architecture. We compare the classification results obtained using both real and synthetic images as training data. The reported results confirm the effectiveness and the potentiality of our approach.
This paper describes an unsupervised approach to retrieve the kinematic parameters of a wheeled mobile robot. The robot chooses which action to take in order to minimize the uncertainty in the parameter estimate and to fully explore the parameter space.Our method explores the effects of a set of elementary motion on the platform to dynamically select the best action and to stop the process when the estimate can be no further improved.We tested our approach both in simulation and with real robots. Our method is reported to obtain in shorter time parameter estimates that are statistically more accurate than the ones obtained by steering the robot on predefined patterns.
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