Biography
Maurice Fallon (IEEE, Senior Member) is a Professor of Robotics and a Royal Society University Research Fellow. He leads the Dynamic Robot Systems Group (Perception). You will find more information about his research on the DRS website.
His research is focused on probabilistic methods for localization and mapping. He has also made research contributions to state estimation for legged robots and is interested in dynamic motion planning and control. Of particular concern is developing methods which are robust in the most challenging situations by leveraging sensor fusion.
Dr. Fallon studied Electronic Engineering at University College Dublin. His PhD research in the field of acoustic source tracking was carried out in the Engineering Department of the University of Cambridge.
Immediately after his PhD he moved to MIT as a post-doc and later research scientist in the Marine Robotics Group (2008-2012). From 2012-2015 he was the perception lead of MIT’s team in the DARPA Robotics Challenge – a multi-year competition developing technologies for semi-autonomous humanoid exploration and manipulation in disaster situations.
After a period as a Lecturer at University of Edinburgh, he moved to Oxford and took up the Royal Society University Research Fellowship in October 2017. He was promoted to full professor in 2025.
He has been PI/co-I on several large UK and EU collaborative projects including ORCA, RAIN, THING, MEMMO as well as the DARPA SubT Challenge winning team CERBERUS. Current ongoing projects include the EU Horizon Europe project DigiForest as well as collaborations with UKAEA (RACE).
Most Recent Publications
Wild visual navigation: fast traversability learning via pre-trained models and online self-supervision
Wild visual navigation: fast traversability learning via pre-trained models and online self-supervision
The Oxford Spires Dataset: Benchmarking large-scale LiDAR-visual localisation, reconstruction and radiance field methods
The Oxford Spires Dataset: Benchmarking large-scale LiDAR-visual localisation, reconstruction and radiance field methods
Building Forest Inventories With Autonomous Legged Robots—System, Lessons, and Challenges Ahead
Building Forest Inventories With Autonomous Legged Robots—System, Lessons, and Challenges Ahead
Boxi: Design Decisions in the Context of Algorithmic Performance for Robotics
Boxi: Design Decisions in the Context of Algorithmic Performance for Robotics
DigiForests: A Longitudinal LiDAR Dataset for Forestry Robotics
DigiForests: A Longitudinal LiDAR Dataset for Forestry Robotics
Most Recent Publications
Wild visual navigation: fast traversability learning via pre-trained models and online self-supervision
Wild visual navigation: fast traversability learning via pre-trained models and online self-supervision
The Oxford Spires Dataset: Benchmarking large-scale LiDAR-visual localisation, reconstruction and radiance field methods
The Oxford Spires Dataset: Benchmarking large-scale LiDAR-visual localisation, reconstruction and radiance field methods
Building Forest Inventories With Autonomous Legged Robots—System, Lessons, and Challenges Ahead
Building Forest Inventories With Autonomous Legged Robots—System, Lessons, and Challenges Ahead
Boxi: Design Decisions in the Context of Algorithmic Performance for Robotics
Boxi: Design Decisions in the Context of Algorithmic Performance for Robotics
DigiForests: A Longitudinal LiDAR Dataset for Forestry Robotics
DigiForests: A Longitudinal LiDAR Dataset for Forestry Robotics