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Professor Paul Newman

Professor

Paul Newman BA FIET FREng FIEEE

BP Professor of Information Engineering

Founder of Oxbotica

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.

Oxford Robotics Institute

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

Altmetric score is
BibTeX View PDF
@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

Altmetric score is
BibTeX View PDF
@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

Altmetric score is
BibTeX View PDF
@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

Altmetric score is
BibTeX View PDF
@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)

Altmetric score is
BibTeX View PDF
@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

Altmetric score is
BibTeX View PDF
@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

Altmetric score is
BibTeX View PDF
@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

Altmetric score is
BibTeX View PDF
@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

Altmetric score is
BibTeX View PDF
@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)

Altmetric score is
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"
}
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