Scholar
Maurice Fallon
Google Scholar ID: BqV8LaoAAAAJ
Professor, University of Oxford
Robotics
Computer Vision
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Citations & Impact
All-time
Citations
5,249
H-index
39
i10-index
76
Publications
20
Co-authors
168
list available
Contact
Email
mfallon@robots.ox.ac.uk
Publications
13 items
TreeLoc++: Robust 6-DoF LiDAR Localization in Forests with a Compact Digital Forest Inventory
2026
Cited
0
Sapling-NeRF: Geo-Localised Sapling Reconstruction in Forests for Ecological Monitoring
2026
Cited
0
GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation
2026
Cited
0
TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching
2026
Cited
0
LT-Exosense: A Vision-centric Multi-session Mapping System for Lifelong Safe Navigation of Exoskeletons
2025
Cited
0
PlanarMesh: Building Compact 3D Meshes from LiDAR using Incremental Adaptive Resolution Reconstruction
2025
Cited
0
Building Forest Inventories with Autonomous Legged Robots -- System, Lessons, and Challenges Ahead
2025
Cited
0
Seeing in the Dark: Benchmarking Egocentric 3D Vision with the Oxford Day-and-Night Dataset
2025
Cited
0
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Resume (English only)
Research Experience
Postdoc and later Research Scientist at MIT's Marine Robotics Group (2008–2012)
Perception lead for MIT’s team in the DARPA Robotics Challenge (2012–2015), developing semi-autonomous humanoid robots for disaster response
Former Lecturer at the University of Edinburgh
Joined Oxford in October 2017 as a Royal Society University Research Fellow
Promoted to Full Professor in 2025
Principal/Co-Investigator on major UK and EU projects including ORCA, RAIN, THING, MEMMO, and the DARPA SubT Challenge-winning team CERBERUS
Currently involved in the EU Horizon Europe project DigiForest and collaborations with UKAEA (RACE)
Background
Professor of Robotics and Royal Society University Research Fellow
Leads the Dynamic Robot Systems Group (Perception) at the University of Oxford
Research focuses on probabilistic methods for localization and mapping
Has made significant contributions to state estimation for legged robots, dynamic motion planning, and control
Aims to develop robust methods in highly challenging environments through sensor fusion
Co-authors
168 total
Co-author 1
Marco Camurri
Associate Professor, University of Trento
Co-author 3
Matías Mattamala
Research Associate, University of Edinburgh
Michael Kaess
Associate Professor, Carnegie Mellon University
Co-author 6
Milad Ramezani
Team Leader | Senior Research Scientist, CSIRO Data61
Nived Chebrolu
Oxford Robotics Institute, University of Oxford
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