Biography
Maurice Fallon (IEEE, Senior Member) is an Associate Professor in Engineering Science 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 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
DigiForests: A Longitudinal LiDAR Dataset for Forestry Robotics
DigiForests: A Longitudinal LiDAR Dataset for Forestry Robotics
Markerless aerial-terrestrial co-registration of forest point clouds using a deformable pose graph
Markerless aerial-terrestrial co-registration of forest point clouds using a deformable pose graph
Online tree reconstruction and forest inventory on a mobile robotic system
Online tree reconstruction and forest inventory on a mobile robotic system
LiSTA: geometric object-based change detection in cluttered environments
LiSTA: geometric object-based change detection in cluttered environments
Tree instance segmentation and traits estimation for forestry environments exploiting LiDAR data collected by mobile robots
Tree instance segmentation and traits estimation for forestry environments exploiting LiDAR data collected by mobile robots
Most Recent Publications
DigiForests: A Longitudinal LiDAR Dataset for Forestry Robotics
DigiForests: A Longitudinal LiDAR Dataset for Forestry Robotics
Markerless aerial-terrestrial co-registration of forest point clouds using a deformable pose graph
Markerless aerial-terrestrial co-registration of forest point clouds using a deformable pose graph
Online tree reconstruction and forest inventory on a mobile robotic system
Online tree reconstruction and forest inventory on a mobile robotic system
LiSTA: geometric object-based change detection in cluttered environments
LiSTA: geometric object-based change detection in cluttered environments
Tree instance segmentation and traits estimation for forestry environments exploiting LiDAR data collected by mobile robots
Tree instance segmentation and traits estimation for forestry environments exploiting LiDAR data collected by mobile robots