5 citations · 8 across the 7 of their papers we have counts for
8 papers
When to Adapt: Multi-Signal Domain Shift Detection for Efficient Training-Free Adaptation in Open-Vocabulary Segmentation
Michele Antonazzi, Alejandra C. Hernandez, José Araujo +2
Robust and reliable perception is essential for autonomous robots operating in real-world environments, particularly in long-term missions where environmental conditions may change…
Probabilistic Scene Graphs: Hierarchical Representation and Real-time System
Waqas Ali, Michele Antonazzi, Timon Homberger +4
3D scene graphs provide semantically rich and hierarchical representations for robot perception. However, existing systems do not maintain uncertainty as an explicit belief or prop…
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…
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…
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…
Enhancing Door-Status Detection for Autonomous Mobile Robots during Environment-Specific Operational Use
Michele Antonazzi, Matteo Luperto, Nicola Basilico +1
Door-status detection, namely recognizing the presence of a door and its status (open or closed), can induce a remarkable impact on a mobile robot's navigation performance, especia…