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About

Christos Papaioannidis is a researcher at the intersection of computer vision, deep learning, and aerial robotics. His work focuses on enabling intelligent perception and interaction for autonomous systems, particularly in industrial inspection and human-robot collaboration. He has made key contributions to 3D object pose estimation, developing a multi-objective quaternion learning framework that uses CNNs to recognize objects and estimate their spatial orientation from color images—a critical capability for robotic grasping and positioning. His research also advances human-machine communication through gesture recognition, employing deep neural networks to interpret hand gestures from RGB video feeds. Papaioannidis has been instrumental in the AERIAL-CORE project, which deploys AI-powered aerial robots for the inspection and maintenance of electrical power infrastructures. His work on gesture-controlled aerial robot formations enables intuitive human-swarm interaction for safety monitoring, particularly for workers at height. With over 35 citations across his most-cited papers, Papaioannidis’s research is shaping the future of autonomous aerial systems and human-robot interaction in critical infrastructure applications.