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Dr Maurice Fallon

Associate Professor

Maurice Fallon PhD (Cantab)

Royal Society University Research Fellow

Associate Professor

Biography

Maurice Fallon is an Associate Professor and Royal Society University Research Fellowship. He is also a Research Fellow of Wolfson College. He leads the Dynamic Robot Systems Group within Oxford Robotics Institute. DRS develops algorithms for navigating quadrupeds, motion planning algorithms to allow quadrupeds to move more smoothly and accurately.

Prof. 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 as a research scientist in the Marine Robotics Group (2008-2012) working in robot mapping. 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.

Recent Developments

The Oxford Robotics Institute are playing a key role in two of the four Robotics Hubs funded by the EPSRC as part of their £44.5 million investment over the next three and a half years into Robotics and Artificial intelligence (AI). More details here.

Most Recent Publications

Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry

Wisth D et al. (2021), IEEE ROBOTICS AND AUTOMATION LETTERS, 6(2), 1004-1011

SKD: keypoint detection for point clouds using saliency estimation

Tinchev G, Penate Sanchez A & Fallon M (2021), IEEE Robotics and Automation Letters, 6(2), 3785-3792

Haptic sequential Monte Carlo localization for quadrupedal locomotion in vision-denied scenarios

Buchanan R, Camurri M & Fallon M (2021), 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3657-3663

The newer college dataset: Handheld LiDAR, inertial and vision with ground truth

Ramezani M et al. (2020), IEEE International Conference on Intelligent Robots and Systems, 4353-4360

Online LiDAR-SLAM for legged robots with robust registration and deep-learned loop closure

Ramezani M et al. (2020), IEEE Robotics and Automation Letters, 4158-4164

View all

Research Interests

Dr. Fallon's 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.

Current Projects

THING: Haptic locomotion and sensing for quadrupeds (EU H2020)

MEMMO: Generating complex robot motions with a Memory of Motion (EU H2020)

RAIN: Robotics and AI research for the Nuclear Industry (EPSRC Research Hub)

ORCA: Robotics for the Oil and Gas Industry (EPSRC Research Hub)

 

Most Recent Publications

Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry

Wisth D et al. (2021), IEEE ROBOTICS AND AUTOMATION LETTERS, 6(2), 1004-1011

SKD: keypoint detection for point clouds using saliency estimation

Tinchev G, Penate Sanchez A & Fallon M (2021), IEEE Robotics and Automation Letters, 6(2), 3785-3792

Haptic sequential Monte Carlo localization for quadrupedal locomotion in vision-denied scenarios

Buchanan R, Camurri M & Fallon M (2021), 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3657-3663

The newer college dataset: Handheld LiDAR, inertial and vision with ground truth

Ramezani M et al. (2020), IEEE International Conference on Intelligent Robots and Systems, 4353-4360

Online LiDAR-SLAM for legged robots with robust registration and deep-learned loop closure

Ramezani M et al. (2020), IEEE Robotics and Automation Letters, 4158-4164

View all

DPhil Opportunities

I am interested in supervising research students in navigation, mapping and motion planning for robots, particularly dynamic and/or legged robots.

Most Recent Publications

Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry

Wisth D et al. (2021), IEEE ROBOTICS AND AUTOMATION LETTERS, 6(2), 1004-1011

SKD: keypoint detection for point clouds using saliency estimation

Tinchev G, Penate Sanchez A & Fallon M (2021), IEEE Robotics and Automation Letters, 6(2), 3785-3792

Haptic sequential Monte Carlo localization for quadrupedal locomotion in vision-denied scenarios

Buchanan R, Camurri M & Fallon M (2021), 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3657-3663

The newer college dataset: Handheld LiDAR, inertial and vision with ground truth

Ramezani M et al. (2020), IEEE International Conference on Intelligent Robots and Systems, 4353-4360

Online LiDAR-SLAM for legged robots with robust registration and deep-learned loop closure

Ramezani M et al. (2020), IEEE Robotics and Automation Letters, 4158-4164

View all