Pedro Nuno Leite
Papers
4
Total Citations
59
H-Index
4
About
Pedro Nuno Leite is a leading researcher at the intersection of autonomous maritime systems and robotic perception, with a focus on enabling intelligent decision-making for unmanned surface vehicles. His work addresses critical challenges in offshore operations, particularly in autonomous docking and wind farm inspection. Leite’s most influential contribution, “Advancing Autonomous Surface Vehicles: A 3D Perception System for the Recognition and Assessment of Docking-Based Structures” (35 citations), pioneers AI-driven 3D perception to automate complex docking maneuvers, reducing accident risks and operational costs. He further advances depth estimation with “Exploiting Motion Perception in Depth Estimation Through a Lightweight Convolutional Neural Network” (15 citations), developing efficient deep learning models that extract 3D scene information from single images—a breakthrough for real-time robotic applications. Leite also tackles multi-agent coordination in “Multi-Agent Optimization for Offshore Wind Farm Inspection using an Improved Population-based Metaheuristic” (5 citations), optimizing inspection routes to enhance safety and cost-efficiency. His work on dense disparity maps from sparse depth data (4 citations) demonstrates a commitment to robust perception under real-world constraints. With a portfolio spanning AI, robotics, and maritime engineering, Leite is shaping the future of autonomous systems for hazardous offshore environments.
Research Focus
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Top Papers
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