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Found 18 papers, 5 papers with code
Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues
For a service robot, it is crucial to perceive as early as possible that an approaching person intends to interact: in this case, it can proactively enact friendly behaviors that lead to an improved user experience.
On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors
Sub-\SI{50}{\gram} nano-drones are gaining momentum in both academia and industry.
High-throughput Visual Nano-drone to Nano-drone Relative Localization using Onboard Fully Convolutional Networks
Relative drone-to-drone localization is a fundamental building block for any swarm operations.
Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones
Palm-sized nano-drones are an appealing class of edge nodes, but their limited computational resources prevent running large deep-learning models onboard.
Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors
Precise relative localization is a crucial functional block for swarm robotics.
Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVs
In this work, we leverage a novel neural architecture search (NAS) technique to automatically identify several Pareto-optimal convolutional neural networks (CNNs) for a visual pose estimation task.
Challenges in Visual Anomaly Detection for Mobile Robots
We consider the task of detecting anomalies for autonomous mobile robots based on vision.
An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots
We consider the problem of building visual anomaly detection systems for mobile robots.
Training Lightweight CNNs for Human-Nanodrone Proximity Interaction from Small Datasets using Background Randomization
We consider the task of visually estimating the pose of a human from images acquired by a nearby nano-drone; in this context, we propose a data augmentation approach based on synthetic background substitution to learn a lightweight CNN model from a small real-world training set.
Sensing Anomalies as Potential Hazards: Datasets and Benchmarks
We consider the problem of detecting, in the visual sensing data stream of an autonomous mobile robot, semantic patterns that are unusual (i. e., anomalous) with respect to the robot's previous experience in similar environments.
Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset
We introduce a new outdoor urban 3D pointcloud dataset, covering a total area of 2. 7 $km^2$, sampled from three Swiss cities with different characteristics.
Learning to predict metal deformations in hot-rolling processes
Hot-rolling is a metal forming process that produces a workpiece with a desired target cross-section from an input workpiece through a sequence of plastic deformations; each deformation is generated by a stand composed of opposing rolls with a specific geometry.
Vision-based Control of a Quadrotor in User Proximity: Mediated vs End-to-End Learning Approaches
We consider the task of controlling a quadrotor to hover in front of a freely moving user, using input data from an onboard camera.
Learning Long-range Perception using Self-Supervision from Short-Range Sensors and Odometry
We introduce a general self-supervised approach to predict the future outputs of a short-range sensor (such as a proximity sensor) given the current outputs of a long-range sensor (such as a camera); we assume that the former is directly related to some piece of information to be perceived (such as the presence of an obstacle in a given position), whereas the latter is information-rich but hard to interpret directly.
Robotics
Efficient Classifier Training to Minimize False Merges in Electron Microscopy Segmentation
The prospect of neural reconstruction from Electron Microscopy (EM) images has been elucidated by the automatic segmentation algorithms.
Assessment of algorithms for mitosis detection in breast cancer histopathology images
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers.
Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks
Deep Neural Networks now excel at image classification, detection and segmentation.
Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images
The input layer maps each window pixel to a neuron.
