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Matt Gadd Headshot.

Matt Gadd DPhil (Oxon)

Postdoctoral Researcher

Junior Research Fellow

Stipendiary Lecturer

Biography

Matt is a Postdoctoral Research Assistant in the Mobile Robotics Group and a Junior Research Fellow at Kellogg College.

In terms of enabling autonomy of intelligent machines, Matt is interested in navigation and scene understanding in general, including mapping, localisation, detection, and segmentation. In all of these cases, he is particularly interested in achieving more robust performance in challenging conditions for vision and LiDAR and is similarly an enthusiastic adopter of FMCW scanning radar.

Matt has contributed to various projects at ORI, including Europa2DRIVEN, and SAX.

Matt holds a BSc in Mechatronics Engineering from the University of Cape Town and a DPhil in Engineering Science from Keble College.

Personal website

Publications

2021

  • [PDF] S. Saftescu, M. Gadd, and P. Newman, "Look Here: Learning Geometrically Consistent Refinement of Inverse-Depth Images for 3D Reconstruction", International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), 2021
  • [PDF] M. Gadd, D. De Martini, and P. Newman, "Contrastive Learning for Unsupervised Radar Place Recognition", in Proceedings of the IEEE International Conference on Advanced Robotics (ICAR), 2021
  • [PDF] T. Suleymanov, M. Gadd, D. De Martini, P. Newman, "The Oxford Road Boundaries Dataset", in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Workshop on 3D-Deep Learning for Automated Driving (3D-DLAD), (Nagoya, Japan), July 2021
  • [PDF] M. Gadd, D. De Martini, P. Newman, "Unsupervised Place Recognition with Deep Embedding Learning over Radar Videos", Workshop on Radar Perception for All-Weather Autonomy at the IEEE International Conference on Robotics and Automation (ICRA), 2021
  • [PDF] D. Williams, M. Gadd, D. De Martini, and P. Newman. “Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2021.

2020

  • [PDF]M. Broome, M. Gadd, D. De Martini, and P. Newman, “On the Road: Route Proposal from Radar Self-Supervised by Fuzzy LiDAR Traversability,” AI, vol. 1, no. 4, pp. 558–58, 2020
  • [PDF]D. De Martini, M. Gadd, and P. Newman, “kRadar++: Coarse-to-fine FMCW Scanning Radar Localisation,” Sensors, Special Issue on Sensing Applications in Robotics, vol. 20, no. 21, p. 6002, 2020
  • [PDF]D. Barnes, M. Gadd, P. Murcutt, P. Newman, and I. Posner, “The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Paris, 2020.
  • [PDF]S. Saftescu, M. Gadd, D. De Martini, D. Barnes, and P. Newman, “Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Paris, 2020.
  • [PDF]W. Maddern, G. Pascoe, M. Gadd, D. Barnes, B. Yeomans, and P. Newman, “Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset,” arXiv preprint arXiv: 2002.10152, 2020.
  • [PDF]M. Gadd, D. De Martini, and P. Newman, “Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance,” in IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, OR, USA, 2020.
  • [PDF]T. Suleymanov, M. Gadd, L. Kunze, and P. Newman, “LiDAR Lateral Localisation Despite Challenging Occlusion from Traffic,” in IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, OR, USA, 2020.
  • [PDF]P. Kaul, D. De Martini, M. Gadd, and P. Newman, “RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA, 2020.
  • [PDF]M. Gadd, D. De Martini, L. Marchegiani, L. Kunze, and P. Newman, “Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Workshop on Ensuring and Validating Safety for Automated Vehicles (EVSAV), 2020.
  • [PDF]D. Williams, D. De Martini, M. Gadd, L. Marchegiani, and P. Newman, “Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision,” in IEEE Intelligent Transportation Systems Conference (ITSC), Rhodes, Greece, 2020.

2019

  • [PDF] R. Aldera, D. De Martini, M. Gadd, and P. Newman, “Fast Radar Motion Estimation with a Learnt Focus of Attention using Weak Supervision,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada, 2019.
  • [PDF] R. Aldera, D. De Martini, M. Gadd, and P. Newman, “What Could Go Wrong? Introspective Radar Odometry in Challenging Environments,” in IEEE Intelligent Transportation Systems (ITSC) Conference, Auckland, New Zealand, 2019.
  • [PDF] S. Kyberd, J. Attias, P. Get, P. Murcutt, C. Prahacs, M. Towlson, S. Venn, A. Vasconcelos, M. Gadd, D. De Martini, and P. Newman, “The Hulk: Design and Development of a Weather-proof Vehicle for Long-term Autonomy in Outdoor Environments,” in International Conference on Field and Service Robotics (FSR), Tokyo, Japan, 2019.
  • [PDF] M. R. Balme, M. C. Curtis-Rouse, S. Banham, D. Barnes, R. Barnes, A. Bauer, C. C. Bedford, J. C. Bridges, F. E. G. Butcher, P. Caballo, A. Caldwell, A. J. Coates, C. Cousins, J. M. Davis, J. Dequaire, P. Edwards, P. Fawdon, K. Furuya, M. Gadd, P. Get, A. Griffiths, P. M. Grindrod, M. Gunn, S. Gupta, R. Hansen, J. K. Harris, L. J. Hicks, J. Holt, B. Huber, C. Huntly, I. Hutchinson, L. Jackson, S. Kay, S. Kyberd, H. N. Lerman, M. McHugh, W. J. McMahon, J. P. Muller, T. Ortner, G. Osinski, G. Paar, L. J. Preston, S. P. Schwenzer, R. Stabbins, Y. Tao, C. Traxler, S. Turner, L. Tyler, S. Venn, H. Walker, T. Wilcox, J. Wright, and B. Yeomans, “The 2016 UK Space Agency Mars Utah Rover Field Investigation (MURFI),” Planetary and Space Science, vol. 165, pp. 31-56, 2019.

2017

  • [PDF] B. Yeomans, H. Porav, M. Gadd, D. Barnes, J. Dequaire, T. Wilcox, S. Kyberd, S. Venn, and P. Newman, “MURFI 2016 – From Cars to Mars: Applying Autonomous Vehicle Navigation Methods To a Space Rover Mission,” in Proceedings of the 14th Symposium on Advanced Space Technologies in Robotics and Automation (ASTRA), Leiden, Netherlands, 2017.
  • [PDF] M. R. Balme, M. C. Curtis-Rouse, S. Banham, D. Barnes, R. Barnes, A. Bauer, C. Bedford, J. Bridges, F. E. G. Butcher, P. Caballo, A. Caldwell, A. Coates, C. Cousins, J. Davis, J. Dequaire, P. Edwards, P. Fawdon, K. Furuya, M. Gadd, P. Get, A. Griffiths, P. M. Grindrod, M. Gunn, S. Gupta, R. Hansen, J. K. Harris, J. Holt, B. Huber, C. Huntly, I. Hutchinson, L. Jackson, S. Kay, S. Kybert, H. N. Lerman, M. McHugh, W. McMahon, J. P. Muller, G. Paar, L. J. Preston, S. Schwenzer, R. Stabbins, Y. Tao, C. Traxler, S. Turner, L. Tyler, S. Venn, H. Walker, J. Wright, and B. Yeomans, “UK Space Agency Mars Utah Rover Field Investigation 2016 (MURFI 2016): Overview of mission, aims and progress,” in 48th Lunar and Planetary Science Conference (LPSC), The Woodlands, TX, USA, 2017.

2016

  • [PDF] M. Gadd and P. Newman, “Checkout My Map: Version Control for Fleetwide Visual Localisation,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea, 2016, pp. 5729-5736.

2015

  • [PDF] M. Gadd and P. Newman, “A Framework for Infrastructure-Free Warehouse Navigation,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, 2015, pp. 3271-3278.