Biography
Paul Newman is the BP Professor of Information Engineering at the University of Oxford. He is a member of the Oxford Robotics Institute within the Department of Engineering Science. The ORI enjoys a world leading reputation in mobile autonomy, developing machines which roll, walk, poke, swim and fly in the real world.
His focus lies on pushing the boundaries of navigation and autonomy techniques in terms of both endurance and scale. In 2014 he founded Oxbotica, a spinout company focused on Mobile Autonomy. He was elected fellow of the Royal Academy of Engineering and the IEEE with a citation for outstanding contributions to robot navigation.
Research Interests
- Mobile Robotics
- Computer Vision
- ML
- Navigation
- Systems
- Autonomous Vehicles
Research Groups
Recent Publications
Depth-SIMS: semi-parametric image and depth synthesis
Musat V, De Martini D, Gadd M & Newman P (2022), 2022 International Conference on Robotics and Automation (ICRA), 2388-2394
BibTeX
@inproceedings{depthsimssemipa-2022/7,
title={Depth-SIMS: semi-parametric image and depth synthesis},
author={Musat V, De Martini D, Gadd M & Newman P},
booktitle={ International Conference on Robotics and Automation (ICRA 2022)},
pages={2388-2394},
year = "2022"
}
Fast-MbyM: leveraging translational invariance of the fourier transform for efficient and accurate radar odometry
Weston R, Gadd M, De Martini D, Newman P & Posner H (2022), Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2022), 2186-2192
BibTeX
@inproceedings{fastmbymleverag-2022/7,
title={Fast-MbyM: leveraging translational invariance of the fourier transform for efficient and accurate radar odometry},
author={Weston R, Gadd M, De Martini D, Newman P & Posner H},
booktitle={IEEE International Conference on Robotics and Automation (ICRA 2022)},
pages={2186-2192},
year = "2022"
}
What goes around: leveraging a constant-curvature motion constraint in radar odometry
Aldera R, Gadd M, De Martini D & Newman P (2022), IEEE Robotics and Automation Letters, 7(3), 7865-7872
BibTeX
@article{whatgoesaroundl-2022/6,
title={What goes around: leveraging a constant-curvature motion constraint in radar odometry},
author={Aldera R, Gadd M, De Martini D & Newman P},
journal={IEEE Robotics and Automation Letters},
volume={7},
pages={7865-7872},
publisher={IEEE},
year = "2022"
}
Contrastive learning for unsupervised radar place recognition
Gadd M, De Martini D & Newman P (2022), 2021 20th International Conference on Advanced Robotics (ICAR), 344-349
BibTeX
@inproceedings{contrastivelear-2022/1,
title={Contrastive learning for unsupervised radar place recognition},
author={Gadd M, De Martini D & Newman P},
booktitle={20th International Conference on Advanced Robotics (ICAR 2021)},
pages={344-349},
year = "2022"
}
The Oxford Road Boundaries Dataset
Suleymanov T, Gadd M, De Martini D & Newman P (2022)
BibTeX
@inproceedings{theoxfordroadbo-2022/1,
title={The Oxford Road Boundaries Dataset},
author={Suleymanov T, Gadd M, De Martini D & Newman P},
booktitle={32nd IEEE Intelligent Vehicles Symposium (IV21) -- Workshop on 3D-Deep Learning for Automated Driving (3D-DLAD)},
year = "2022"
}
BoxGraph: semantic place recognition and pose estimation from 3D LiDAR
Pramatarov G, De Martini D, Gadd M & Newman P (2021), Proceedings of IEEE International Conference on Intelligent Robots and Systems, 7004-7011
BibTeX
@inproceedings{boxgraphsemanti-2021/12,
title={BoxGraph: semantic place recognition and pose estimation from 3D LiDAR},
author={Pramatarov G, De Martini D, Gadd M & Newman P},
booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems},
pages={7004-7011},
year = "2021"
}
Fool me once: robust selective segmentation via out-of-distribution detection with contrastive learning
Williams D, Gadd M, De Martini D & Newman P (2021), 2021 IEEE International Conference on Robotics and Automation (ICRA), 9536-9542
BibTeX
@inproceedings{foolmeoncerobus-2021/10,
title={Fool me once: robust selective segmentation via out-of-distribution detection with contrastive learning},
author={Williams D, Gadd M, De Martini D & Newman P},
booktitle={ICRA 2021},
pages={9536-9542},
year = "2021"
}
Self-supervised learning for using overhead imagery as maps in outdoor range sensor localization
Tang TY, De Martini D, Wu S & Newman P (2021), The International Journal of Robotics Research, 40(12-14), 1488-1509
BibTeX
@article{selfsupervisedl-2021/9,
title={Self-supervised learning for using overhead imagery as maps in outdoor range sensor localization},
author={Tang TY, De Martini D, Wu S & Newman P},
journal={The International Journal of Robotics Research},
volume={40},
pages={1488-1509},
publisher={SAGE Publications},
year = "2021"
}
Get to the point: learning lidar place recognition and metric localisation using overhead imagery
Tang TY, De Martini D & Newman P (2021), Proceedings of Robotics: Science and Systems, 2021
BibTeX
@article{gettothepointle-2021/7,
title={Get to the point: learning lidar place recognition and metric localisation using overhead imagery},
author={Tang TY, De Martini D & Newman P},
journal={Proceedings of Robotics: Science and Systems, 2021},
publisher={Robotics: Science and Systems},
year = "2021"
}
Unsupervised place recognition with deep embedding learning over radar videos
Gadd M, De Martini D & Newman P (2021)
BibTeX
@inproceedings{unsupervisedpla-2021/5,
title={Unsupervised place recognition with deep embedding learning over radar videos},
author={Gadd M, De Martini D & Newman P},
booktitle={2021 ICRA Workshop: Radar Perception for All-Weather Autonomy},
year = "2021"
}
