5 papers · 1 filter
Self-supervised Domain Adaptation for Visual 3D Pose Estimation of Nano-drone Racing Gates by Enforcing Geometric Consistency
Nicholas Carlotti, Michele Antonazzi, Elia Cereda +4
We consider the task of visually estimating the relative pose of a drone racing gate in front of a nano-quadrotor, using a convolutional neural network pre-trained on simulated dat…
Instance-Guided Unsupervised Domain Adaptation for Robotic Semantic Segmentation
Michele Antonazzi, Lorenzo Signorelli, Matteo Luperto +1
Semantic segmentation networks, which are essential for robotic perception, often suffer from performance degradation when the visual distribution of the deployment environment dif…
Development and Adaptation of Robotic Vision in the Real-World: the Challenge of Door Detection
Michele Antonazzi, Matteo Luperto, N. Alberto Borghese +1
Mobile service robots are increasingly prevalent in human-centric, real-world domains, operating autonomously in unconstrained indoor environments. In such a context, robotic visio…
Frontier-Based Exploration for Multi-Robot Rendezvous in Communication-Restricted Unknown Environments
Mauro Tellaroli, Matteo Luperto, Michele Antonazzi +1
Multi-robot rendezvous and exploration are fundamental challenges in the domain of mobile robotic systems. This paper addresses multi-robot rendezvous within an initially unknown e…
R2SNet: Scalable Domain Adaptation for Object Detection in Cloud-Based Robotic Ecosystems via Proposal Refinement
Michele Antonazzi, Matteo Luperto, N. Alberto Borghese +1
We introduce a novel approach for scalable domain adaptation in cloud robotics scenarios where robots rely on third-party AI inference services powered by large pre-trained deep ne…