Zobrazeno 1 - 10
of 371
pro vyhledávání: '"MENEGATTI, EMANUELE"'
Semantic segmentation of point clouds is an essential task for understanding the environment in autonomous driving and robotics. Recent range-based works achieve real-time efficiency, while point- and voxel-based methods produce better results but ar
Externí odkaz:
http://arxiv.org/abs/2410.10510
Autor:
Bacchin, Alberto, Barcellona, Leonardo, Terreran, Matteo, Ghidoni, Stefano, Menegatti, Emanuele, Kiyokawa, Takuya
Robotic waste sorting poses significant challenges in both perception and manipulation, given the extreme variability of objects that should be recognized on a cluttered conveyor belt. While deep learning has proven effective in solving complex tasks
Externí odkaz:
http://arxiv.org/abs/2409.16999
Out-of-Distribution (OOD) detection in computer vision is a crucial research area, with related benchmarks playing a vital role in assessing the generalizability of models and their applicability in real-world scenarios. However, existing OOD benchma
Externí odkaz:
http://arxiv.org/abs/2409.01109
Autor:
Barcellona, Leonardo, Bacchin, Alberto, Terreran, Matteo, Menegatti, Emanuele, Ghidoni, Stefano
The ability of a robot to pick an object, known as robot grasping, is crucial for several applications, such as assembly or sorting. In such tasks, selecting the right target to pick is as essential as inferring a correct configuration of the gripper
Externí odkaz:
http://arxiv.org/abs/2404.12717
Autor:
Evangelista, Daniele, Olivastri, Emilio, Allegro, Davide, Menegatti, Emanuele, Pretto, Alberto
Publikováno v:
2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 11474-11480
Hand-eye calibration is the problem of estimating the spatial transformation between a reference frame, usually the base of a robot arm or its gripper, and the reference frame of one or multiple cameras. Generally, this calibration is solved as a non
Externí odkaz:
http://arxiv.org/abs/2303.04747
Autor:
Fusaro, Daniel, Olivastri, Emilio, Evangelista, Daniele, Imperoli, Marco, Menegatti, Emanuele, Pretto, Alberto
Publikováno v:
Proceedings of the 17th International Conference on Intelligent Autonomous Systems (IAS 2022)
Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation. This paper proposes and evaluates a real-time machine learning-based Traversab
Externí odkaz:
http://arxiv.org/abs/2206.03083
Publikováno v:
Proceedings of the 17th International Conference on Intelligent Autonomous Systems (IAS 2022)
A guiding robot aims to effectively bring people to and from specific places within environments that are possibly unknown to them. During this operation the robot should be able to detect and track the accompanied person, trying never to lose sight
Externí odkaz:
http://arxiv.org/abs/2206.02735
Autor:
Saviolo, Alessandro, Bonotto, Matteo, Evangelista, Daniele, Imperoli, Marco, Lazzaro, Jacopo, Menegatti, Emanuele, Pretto, Alberto
Publikováno v:
Proceedings of the 16th International Conference on Intelligent Autonomous Systems (IAS 2021)
This paper presents a new framework for human body part segmentation based on Deep Convolutional Neural Networks trained using only synthetic data. The proposed approach achieves cutting-edge results without the need of training the models with real
Externí odkaz:
http://arxiv.org/abs/2102.01460
Publikováno v:
Robotics and Autonomous Systems, Volume 145, November 2021, 103863
Complex manipulation tasks require careful integration of symbolic reasoning and motion planning. This problem, commonly referred to as Task and Motion Planning (TAMP), is even more challenging if the workspace is non-static, e.g. due to human interv
Externí odkaz:
http://arxiv.org/abs/2009.03139