P
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
More filters
Book ChapterDOI
Describing, Navigating and Recognising Urban Spaces - Building an End-to-End SLAM System
Paul Newman,Manjari Chandran-Ramesh,Dave Cole,Mark Cummins,Alastair Harrison,Ingmar Posner,Derik Schroeter +6 more
TL;DR: This paper examines sibling problems which remain central to the mobile autonomy agenda, and considers the problem detecting loop-closure from an extensible, appearance-based probabilistic view point and the use of visual geometry to impose topological constraints.
A Tour of MOOS-IvP Autonomy Software Modules
TL;DR: This paper provides an overview of the MOOS-IvP autonomy software modules and provides for each a general description of functionality, dependency relationships to other modules, rough order of magnitude in complexity or size, authorship, and current and planned distribution access.
Practical Route Planning Under Delay Uncertainty: Stochastic Shortest Path Queries
TL;DR: An algorithm is described that, given a directed planar network with edge lengths characterized by expected travel time and variance, pre-computes a data structure in quasi-linear time such that approximate stochastic shortest-path queries can be answered in poly-logarithmic time.
Proceedings ArticleDOI
Planning most-likely paths from overhead imagery
Liz Murphy,Paul Newman +1 more
TL;DR: In this paper, the authors use a multi-class Gaussian process classifier to predict the probability of class membership at a particular grid location and then combine with a terrain cost evaluated at that location using a spatial Gaussian Process.
Proceedings ArticleDOI
Stochastic mapping frameworks
TL;DR: A number of extensions to the stochastic mapping framework are described, made possible by the incorporation of past vehicle states into the state vector to explicitly represent the robot's trajectory.