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.
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Journal ArticleDOI
Robust Mapping and Localization in Indoor Environments Using Sonar Data
TL;DR: A perceptual grouping process that permits the robust identification and localization of environmental features from the sparse and noisy sonar data, and a map joining technique that allows the system to build a sequence of independent limited-size stochastic maps and join them in a globally consistent way.
Proceedings ArticleDOI
An Atlas framework for scalable mapping
TL;DR: Atlas is described, a hybrid metrical/topological approach to SLAM that achieves efficient mapping of large-scale environments using a graph of coordinate frames that captures the local environment and the current robot pose along with the uncertainties of each.
Proceedings ArticleDOI
Using laser range data for 3D SLAM in outdoor environments
Dave Cole,Paul Newman +1 more
TL;DR: It is demonstrated that with a few augmentations, existing 2DSLAM technology can be extended to perform full 3D SLAM in less benign, outdoor, undulating environments with data acquired with a 3D laser range finder.
Proceedings ArticleDOI
Outdoor SLAM using visual appearance and laser ranging
TL;DR: A 3D SLAM system using information from an actuated laser scanner and camera installed on a mobile robot to detect loop closure events using a novel appearance-based retrieval system that is robust to repetitive visual structure and provides a probabilistic measure of confidence.
Proceedings ArticleDOI
Highly scalable appearance-only SLAM - FAB-MAP 2.0
Mark Cummins,Paul Newman +1 more
TL;DR: A new formulation of appearance-only SLAM suitable for very large scale navigation that naturally incorporates robustness against perceptual aliasing is described and demonstrated performing reliable online appearance mapping and loop closure detection over a 1,000 km trajectory.