1,000 research outputs found
Learning Ground Traversability from Simulations
Mobile ground robots operating on unstructured terrain must predict which
areas of the environment they are able to pass in order to plan feasible paths.
We address traversability estimation as a heightmap classification problem: we
build a convolutional neural network that, given an image representing the
heightmap of a terrain patch, predicts whether the robot will be able to
traverse such patch from left to right. The classifier is trained for a
specific robot model (wheeled, tracked, legged, snake-like) using simulation
data on procedurally generated training terrains; the trained classifier can be
applied to unseen large heightmaps to yield oriented traversability maps, and
then plan traversable paths. We extensively evaluate the approach in simulation
on six real-world elevation datasets, and run a real-robot validation in one
indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation
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. Standard anomaly detection models are trained using large
datasets composed only of non-anomalous data. However, in robotics
applications, it is often the case that (potentially very few) examples of
anomalies are available. We tackle the problem of exploiting these data to
improve the performance of a Real-NVP anomaly detection model, by minimizing,
jointly with the Real-NVP loss, an auxiliary outlier exposure margin loss. We
perform quantitative experiments on a novel dataset (which we publish as
supplementary material) designed for anomaly detection in an indoor patrolling
scenario. On a disjoint test set, our approach outperforms alternatives and
shows that exposing even a small number of anomalous frames yields significant
performance improvements
Challenges in Visual Anomaly Detection for Mobile Robots
We consider the task of detecting anomalies for autonomous mobile robots
based on vision. We categorize relevant types of visual anomalies and discuss
how they can be detected by unsupervised deep learning methods. We propose a
novel dataset built specifically for this task, on which we test a
state-of-the-art approach; we finally discuss deployment in a real scenario.Comment: Workshop paper presented at the ICRA 2022 Workshop on Safe and
Reliable Robot Autonomy under Uncertainty
https://sites.google.com/umich.edu/saferobotautonomy/hom
Path planning for mobile robots in the real world: handling multiple objectives, hierarchical structures and partial information
Autonomous robots in real-world environments face a number of challenges even to accomplish apparently simple tasks like moving to a given location. We present four realistic scenarios in which robot navigation takes into account partial information, hierarchical structures, and multiple objectives. We start by discussing navigation in indoor environments shared with people, where routes are characterized by effort, risk, and social impact. Next, we improve navigation by computing optimal trajectories and implementing human-friendly local navigation behaviors. Finally, we move to outdoor environments, where robots rely on uncertain traversability estimations and need to account for the risk of getting stuck or having to change route
Charm in Deep-Inelastic Scattering
We show how to extend systematically the FONLL scheme for inclusion of heavy quark mass effects in DIS to account for the possible effects of an intrinsic charm component in the nucleon. We show that when there is no intrinsic charm, FONLL is equivalent to S-ACOT to any order in perturbation theory, while when an intrinsic charm component is included FONLL is identical to ACOT, again to all orders in perturbation theory. We discuss in detail the inclusion of top and bottom quarks to construct a variable flavour number scheme, and give explicit expressions for the construction of the structure functions , and to NNLO
Preface of the 31st Italian Symposium on Advanced Database Systems
This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p
Preface of the 31st Italian Symposium on Advanced Database Systems
This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p
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