Weakly Supervised Fruit Counting for Yield Estimation Using Spatial Consistency

Fruit counting is a fundamental component for yield estimation applications. Most of the existing approaches address this problem by relying on fruit models (i.e., by using object detectors) or by explicitly learning to count. Despite the impressive results achieved by these approaches, all of them need strong supervision information during the training phase. In agricultural applications, manual labeling may require a huge effort or, in some cases, it could be impossible to acquire fine-grained ground truth labels. In this letter, we tackle this problem by proposing a weakly supervised framework that learns to count fruits without the need for task-specific supervision labels. In particular, we devise a novel convolutional neural network architecture that requires only a simple image level binary classifier to detect whether the image contains instances of the fruits or not and combines this information with image spatial consistency constraints. The result is an architecture that learns to count without task-specific labels (e.g., object bounding boxes or the multiplicity of fruit instances in the image). The experiments on three different varieties of fruits (i.e., olives, almonds, and apples) show that our approach reaches performances that are comparable with SotA approaches based on the supervised paradigm

Modelling and simulation of a quadrotor in V-tail configuration

Standard quad-rotors are the most common and versatile unmanned aerial vehicles (UAVs) thanks to their simple control and mechanics. However, the common coplanar rotor configurations are designed for maximising hovering and loitering performances, and not for fast and aggressive manoeuvrings. Since the expanding field of application of micro aerial vehicles (MAVs) requires ever-increasing speed and agility, the question whether there are better configurations for aggressive flight arises. In this work, we address this question by studying the energetics and dynamics of fixed tilted rotor configurations compared to standard quad-rotor. To do so we chose a specific configuration, called V-tail, which is as mechanically simple as the standard X-4 quad-rotor, but has back rotors tilted by a known fixed angle, and developed the dynamical model to test its properties both through software simulation and with actual experiments. Mathematical modelling and field experiments suggest that this configuration is able to achieve better performance in manoeuvring control, while losing some power in hovering owing to less vertical thrust. In addition, these increases in performance are obtained with the same attitude control as the standard quad-rotor, making this configuration very easy to set up

Robust visual semi-semantic loop closure detection by a covisibility graph and CNN features

Visual Self-localization in unknown environments is a crucial capability for an autonomous robot. Real life scenarios often present critical challenges for autonomous vision-based localization, such as robustness to viewpoint and appearance changes. To address these issues, this paper proposes a novel strategy that models the visual scene by preserving its geometric and semantic structure and, at the same time, improves appearance invariance through a robust visual representation. Our method relies on high level visual landmarks consisting of appearance invariant descriptors that are extracted by a pre-trained Convolutional Neural Network (CNN) on the basis of image patches. In addition, during the exploration, the landmarks are organized by building an incremental covisibility graph that, at query time, is exploited to retrieve candidate matching locations improving the robustness in terms of viewpoint invariance. In this respect, through the covisibility graph, the algorithm finds, more effectively, location similarities by exploiting the structure of the scene that, in turn, allows the construction of virtual locations i.e., artificially augmented views from a real location that are useful to enhance the loop closure ability of the robot. The proposed approach has been deeply analysed and tested in different challenging scenarios taken from public datasets. The approach has also been compared with a state-of-the-art visual navigation algorithm