Giuseppe Loianno

About

Giuseppe Loianno is a pioneering robotics researcher whose work centers on autonomous unmanned aerial vehicles (UAVs), multi-robot systems, and aerial perception in challenging environments. His research has fundamentally advanced the ability of micro aerial vehicles (MAVs) to operate reliably without GPS, using onboard vision and inertial sensing to achieve feats once considered beyond reach for lightweight platforms. Loianno's most influential contributions include developing systems for fast, autonomous flight in cluttered, GPS-denied environments (196 citations) and enabling UAVs to autonomously land on moving vehicles using monocular cameras (158 citations). His work on visual inertial odometry for quadrotor swarms has pushed the boundaries of decentralized, infrastructure-free multi-robot coordination, while his research on avian-inspired aerial grasping opened new frontiers in agile manipulation. He has also made notable strides in cooperative payload transportation, multi-robot collaborative perception using graph neural networks, and autonomous target tracking on miniature platforms. Collectively, his papers have accumulated over 1,250 citations, reflecting broad influence across the robotics community. His research consistently bridges the gap between theoretical control frameworks and real-world deployment, making autonomous aerial systems more capable, practical, and robust for applications ranging from search-and-rescue to delivery logistics.

Research Focus

Key Achievements

Top Papers

1. 1
   Fast, autonomous flight in GPS‐denied and cluttered environments

   196 citations · 2017
2. 2
   Autonomous landing on a moving vehicle with an unmanned aerial vehicle

   158 citations · 2019
3. 3
   System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

   154 citations · 2016
4. 4
   Toward autonomous avian-inspired grasping for micro aerial vehicles

   117 citations · 2014
5. 5
   Autonomous Flight for Detection, Localization, and Tracking of Moving Targets With a Small Quadrotor

   116 citations · 2017
6. 6
   Visual Inertial Odometry Swarm: An Autonomous Swarm of Vision-Based Quadrotors

   116 citations · 2018
7. 7
   Cooperative autonomous search, grasping, and delivering in a treasure hunt scenario by a team of unmanned aerial vehicles

   110 citations · 2018
8. 8
   Visual Servoing of Quadrotors for Perching by Hanging From Cylindrical Objects

   109 citations · 2015
9. 9
   Multi-Robot Collaborative Perception With Graph Neural Networks

   98 citations · 2022
10. 10
    Cooperative Transportation of Cable Suspended Payloads With MAVs Using Monocular Vision and Inertial Sensing

    78 citations · 2021

Key Collaborators

Contact & Links