Gaetano Pernisco
Papers
2
Total Citations
9
H-Index
2
About
Gaetano Pernisco is a researcher whose work sits at the intersection of robotics, computer vision, and deep learning. His primary focus is on developing intelligent, data-driven solutions for robot localization—the fundamental challenge of enabling a robot to determine its position and orientation within an environment. Pernisco’s major contribution is the introduction of the Optical Encoder Neural Network (OE-net), a novel convolutional neural network (CNN) architecture specifically designed to process optical encoder data for relative pose estimation. This approach cleverly repurposes CNNs—typically used for image analysis—to interpret sequential sensor readings, offering a robust alternative to traditional localization methods. His most-cited paper, published in 2022, has accumulated 6 citations, demonstrating early recognition within the field. A second, related publication on the same topic has garnered 3 citations. By bridging the gap between classical sensor processing and modern deep learning, Pernisco’s work provides a practical, efficient framework for enhancing robotic autonomy, making it a valuable reference for students and researchers exploring neural-network-based localization techniques.
Research Focus
Key Achievements
Top Papers
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