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Paul Newman
Researcher at University of Oxford
Publications - 287
Citations - 21374
Paul Newman is an academic researcher from University of Oxford. The author has contributed to research in topics: Mobile robot & Radar. The author has an hindex of 59, co-authored 278 publications receiving 18608 citations. Previous affiliations of Paul Newman include University of Sydney & Carnegie Mellon University.
Papers
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Journal ArticleDOI
Editorial Special Issue on Robotic Vision
Posted Content
Meshed Up: Learnt Error Correction in 3D Reconstructions
TL;DR: A machine learning technique to identify errors in three dimensional (3D) meshes is proposed, and beyond simply identifying errors, this method quantifies both the magnitude and the direction of depth estimate errors when viewing the scene.
Patent
Localising portable apparatus
TL;DR: In this article, a method of localising portable apparatus in an environment is proposed, which consists of obtaining captured image data representing an image captured by an imaging device associated with the portable apparatus, and obtaining mesh data representing a 3-dimensional textured mesh of at least part of the environment.
Posted Content
Resource-Performance Trade-off Analysis for Mobile Robot Design
Morteza Lahijanian,Maria Svorenova,Akshay A. Morye,Brian Yeomans,Dushyant Rao,Ingmar Posner,Paul Newman,Hadas Kress-Gazit,Marta Kwiatkowska +8 more
TL;DR: This paper provides a framework to aid designers in exploring such resource-performance trade-offs and finding schedules for mobile robots, guided by questions such as "what is the minimum resource budget required to achieve a given level of performance?"
Proceedings ArticleDOI
Opportunistic Radio Assisted Navigation for Autonomous Ground Vehicles
TL;DR: A novel navigation approach is proposed, which exploits side-channel information of co-location to thread up visually-separated experiences with short exploration phases, and achieves much better navigation performance in both reach ability and cost, comparing with the state of the arts that only use visual information.