Eric Guerrero-Font

About

Eric Guerrero-Font is a leading figure in autonomous marine robotics, specializing in the intersection of computer vision, deep learning, and robust estimation for underwater exploration. His most impactful work, "Combining Deep Learning and Robust Estimation for Outlier-Resilient Underwater Visual Graph SLAM" (21 citations), introduces a novel approach to Visual Loop Detection that dramatically improves the reliability of Simultaneous Localization and Mapping in challenging benthic environments. Guerrero-Font further advanced the field with his "Adaptive Visual Information Gathering for Autonomous Exploration of Underwater Environments" (15 citations), a framework that enables AUVs to dynamically adjust their exploration strategies based on real-time visual data, significantly enhancing data collection efficiency. He also led the development of the Xiroi II ASV platform (11 citations), a modular system designed for multi-robot marine coordination. His recent work on assessing Posidonia oceanica habitats (8 citations) demonstrates a complete pipeline from autonomous data gathering to deep learning-based classification, showcasing his commitment to solving real-world ecological challenges. With a career defined by field-tested, open-source solutions, Guerrero-Font’s research is essential reading for anyone working on resilient, adaptive marine autonomy.

Research Focus

Key Achievements

Top Papers

1. 1
   Combining Deep Learning and Robust Estimation for Outlier-Resilient Underwater Visual Graph SLAM

   21 citations · 2022
2. 2
   Adaptive Visual Information Gathering for Autonomous Exploration of Underwater Environments

   15 citations · 2021
3. 3
   Xiroi II, an Evolved ASV Platform for Marine Multirobot Operations

   11 citations · 2022
4. 4
   Assessing benthic marine habitats colonized with posidonia oceanica using autonomous marine robots and deep learning: A Eurofleets campaign

   8 citations · 2023

Key Collaborators

Contact & Links