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Yvan Petillot
Researcher at Heriot-Watt University
Publications - 251
Citations - 6162
Yvan Petillot is an academic researcher from Heriot-Watt University. The author has contributed to research in topics: Sonar & Remotely operated underwater vehicle. The author has an hindex of 36, co-authored 234 publications receiving 5006 citations. Previous affiliations of Yvan Petillot include University of Colorado Boulder & University of Edinburgh.
Papers
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Journal ArticleDOI
Path Planning for Autonomous Underwater Vehicles
TL;DR: This work develops an algorithm, called FM*, to efficiently extract a 2-D continuous path from a discrete representation of the environment and takes underwater currents into account thanks to an anisotropic extension of the original FM algorithm.
Proceedings ArticleDOI
The SLAM problem: a survey
TL;DR: This paper surveys the most recent published techniques in the field of Simultaneous Localization and Mapping (SLAM) and focuses on the existing techniques available to speed up the process, with the purpose to handel large scale scenarios.
Journal ArticleDOI
Underwater vehicle obstacle avoidance and path planning using a multi-beam forward looking sonar
TL;DR: A new framework for segmentation of sonar images, tracking of underwater objects and motion estimation, applied to the design of an obstacle avoidance and path planning system for underwater vehicles based on a multi-beam forward looking sonar sensor is described.
Journal ArticleDOI
An automatic approach to the detection and extraction of mine features in sidescan sonar
Scott Reed,Yvan Petillot,J. Bell +2 more
TL;DR: This paper presents unsupervised models for both the detection and the shadow extraction phases of an automated classification system for mine detection and classification using high-resolution sidescan sonar.
Proceedings ArticleDOI
StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments
TL;DR: A method for robust dense RGB-D SLAM in dynamic environments which detects moving objects and simultaneously reconstructs the background structure and achieves similar performance in static environments and improved accuracy and robustness in dynamic scenes is proposed.