Antonio Lundgren
Papers
3
Total Citations
25
H-Index
2
About
Antonio Lundgren’s research sits at the intersection of computer vision, robotics, and human-robot interaction, with a particular focus on enabling socially assistive robots to perceive and interact with their environments through text understanding. His major contributions include pioneering work on end-to-end multilingual text detection and recognition for resource-constrained robotic platforms, most notably through the OctShuffleMLT architecture—a compact octave-based neural network designed to overcome hardware limitations in real-world applications. This work, cited 11 times, addresses a critical gap in deploying deep networks on robots and embedded systems. Lundgren also led the development of NAO-Read, a system that empowers the humanoid robot NAO to recognize text on objects in natural scenes, advancing the field of autonomous robotic manipulation and navigation. His 2022 systematic review on computer vision semantic analysis in socially assistive robotics, with 12 citations, synthesizes the state of the art and highlights the growing societal need for intelligent care robots. Through these efforts, Lundgren has established himself as a key figure in making robots more perceptive, autonomous, and capable of assisting humans in everyday environments.
Research Focus
Key Achievements
Top Papers
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