Mirko Leomanni — accepted AI-conference papers

2024

D-VAT: End-to-End Visual Active Tracking for Micro Aerial Vehicles

Alberto Dionigi, Simone Felicioni, Mirko Leomanni, Gabriele Costante

RA-L 2024

Visual active tracking is a growing research topic in robotics due to its key role in applications such as human assistance, disaster recovery, and surveillance. In contrast to passive tracking, active tracking approaches combine vision and control capabilities to detect and actively track the targe

Cited by 19 Source ↗code ↗Scholar ↗

2023

Monocular Reactive Collision Avoidance for MAV Teleoperation with Deep Reinforcement Learning

Raffaele Brilli, Marco Legittimo, Francesco Crocetti, Mirko Leomanni, Mario Luca Fravolini, Gabriele Costante

ICRA 2023poster

Enabling Micro Aerial Vehicles (MAVs) with semi-autonomous capabilities to assist their teleoperation is crucial in several applications. Remote human operators do not have, in general, the situational awareness to perceive obstacles near the drone, nor the readiness to provide commands to avoid col…

Cited by 6 Source ↗Scholar ↗