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About

Hugo Silva is a robotics researcher whose work spans marine autonomy, visual navigation, and underwater robotics systems. Based at the Autonomous Systems Lab at ISEP/IPP in Porto, Portugal, Silva has built a distinguished research portfolio centered on enabling robots to perceive, navigate, and operate intelligently in challenging real-world environments — particularly aquatic ones. Silva's early contributions focused on autonomous surface vehicles, most notably the design and implementation of the ROAZ and ROAZ II platforms and pioneering hybrid docking manoeuvres using visual feedback between autonomous surface and underwater vehicles — work that together has attracted over 85 citations. His research subsequently evolved toward robust visual navigation, encompassing Visual Odometry, Real-Time SLAM for ROVs, and stereo egomotion estimation across both sparse and dense methodologies. More recently, Silva has embraced deep learning as a transformative tool for underwater scene understanding and egomotion estimation, reflecting a forward-looking integration of modern AI techniques into persistent autonomous navigation. His 2020 paper on deep learning for underwater visual odometry has already garnered 37 citations, signaling strong community interest. A standout applied contribution is his autonomous robotic system for fish farming biomass estimation, demonstrating his ability to translate fundamental research into practical industry solutions. With nearly 190 total citations, Silva represents a significant voice in marine and mobile robotics.