Antoni Burguera

About

Antoni Burguera is a prominent robotics and autonomous systems researcher whose work sits at the intersection of mobile robot localization, underwater vehicle navigation, and marine environmental monitoring. His foundational contributions center on developing probabilistic frameworks for sonar-based localization, including scan matching techniques using ultrasonic range finders and imaging sonar systems — work that has collectively garnered over 200 citations. His 2011 paper on imaging systems for advanced underwater vehicles (78 citations) established key principles for visual sensing in sub-sea conditions, while his series of sonar scan matching publications from 2005 to 2012 systematically advanced the reliability of robot self-localization in challenging acoustic environments. Beyond sensor methodology, Burguera has made meaningful contributions to marine conservation through robotics, developing automated approaches for mapping and detecting *Posidonia oceanica* meadows — critically important Mediterranean ecosystems — using lightweight autonomous underwater vehicles (AUVs) and neural network-based image segmentation. His more recent work integrates deep learning with robust estimation for Visual SLAM, demonstrating a consistent evolution toward intelligent, resilient autonomous systems. His research uniquely bridges theoretical probabilistic modeling with real-world ecological applications, making him a distinctive voice in both robotics and marine science communities.

Research Focus

Key Achievements

Top Papers

1. 1
   Imaging Systems for Advanced Underwater Vehicles

   78 citations · 2011
2. 2
   Sonar Sensor Models and Their Application to Mobile Robot Localization

   51 citations · 2009
3. 3
   On the use of likelihood fields to perform sonar scan matching localization

   49 citations · 2009
4. 4
   Visual Discrimination and Large Area Mapping of Posidonia Oceanica Using a Lightweight AUV

   41 citations · 2017
5. 5
   A Probabilistic Framework for Sonar Scan Matching Localization

   36 citations · 2008
6. 6
   Robust scan matching localization using ultrasonic range finders

   30 citations · 2005
7. 7
   Probabilistic Sonar Scan Matching for Robust Localization

   29 citations · 2007
8. 8
   The UspIC: Performing Scan Matching Localization Using an Imaging Sonar

   28 citations · 2012
9. 9
   Segmentation through patch classification: A neural network approach to detect Posidonia oceanica in underwater images

   26 citations · 2020
10. 10
    Combining Deep Learning and Robust Estimation for Outlier-Resilient Underwater Visual Graph SLAM

    21 citations · 2022

Key Collaborators

Contact & Links