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Scan matching SLAM in underwater environments

This paper proposes a pose-based algorithm to solve the full simultaneous localization and mapping problem for autonomous underwater vehicle (AUV) navigating in unknown and possibly unstructured environments. The proposed method first estimates the local path traveled by the robot while forming the acoustic image (scan) with range data coming from a mono-beam rotating sonar head, providing position estimates for correcting the distortions that the vehicle motion produces in the scans. Then, consecutive scans are cross-registered under a probabilistic scan matching technique for estimating the displacements of the vehicle including the uncertainty of the scan matching result. Finally, an augmented state extended Kalman filter estimates and keeps the registered scans poses. No prior structural information or initial pose are considered. The viability of the proposed approach has been tested reconstructing the trajectory of a guided AUV operating along a 600 m path within a marina environment

This research work was partially sponsored by the Spanish project DPI2011-27977-C03-02 (COMAROB) and two European Commission’s Seventh Framework Program 2007-2013 Projects: ICT-248497 (TRIDENT) and Marie Curie PERG-GA-2010-276778 (Surf3DSLAM). The dataset was acquired with the help of the members (staff and students) of the Computer Vision and Robotics research group at the University of Girona

© Autonomous Robots, 2014, vol. 36, p. 181-198

Institute of Electrical and Electronics Engineers (IEEE)

Author: Mallios, Angelos
Ridao Rodríguez, Pere
Ribas Romagós, David
Hernàndez Bes, Emili
Date: 2014
Abstract: This paper proposes a pose-based algorithm to solve the full simultaneous localization and mapping problem for autonomous underwater vehicle (AUV) navigating in unknown and possibly unstructured environments. The proposed method first estimates the local path traveled by the robot while forming the acoustic image (scan) with range data coming from a mono-beam rotating sonar head, providing position estimates for correcting the distortions that the vehicle motion produces in the scans. Then, consecutive scans are cross-registered under a probabilistic scan matching technique for estimating the displacements of the vehicle including the uncertainty of the scan matching result. Finally, an augmented state extended Kalman filter estimates and keeps the registered scans poses. No prior structural information or initial pose are considered. The viability of the proposed approach has been tested reconstructing the trajectory of a guided AUV operating along a 600 m path within a marina environment
This research work was partially sponsored by the Spanish project DPI2011-27977-C03-02 (COMAROB) and two European Commission’s Seventh Framework Program 2007-2013 Projects: ICT-248497 (TRIDENT) and Marie Curie PERG-GA-2010-276778 (Surf3DSLAM). The dataset was acquired with the help of the members (staff and students) of the Computer Vision and Robotics research group at the University of Girona
Format: application/pdf
ISSN: 0929-5593
Document access: http://hdl.handle.net/10256/10212
Language: eng
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Collection: MICINN/PN 2012-2014/DPI2011-27977-C03-02
Reproducció digital del document publicat a: http://dx.doi.org/10.1007/s10514-013-9345-0
Articles publicats (D-ATC)
info:eu-repo/grantAgreement/EC/FP7/276778
info:eu-repo/grantAgreement/EC/FP7/248497
Is part of: © Autonomous Robots, 2014, vol. 36, p. 181-198
Rights: Tots els drets reservats
Subject: Robots mòbils
Mobile robots
Robots autònoms
Autonomous robots
Vehicles submergibles
Submersibles
Title: Scan matching SLAM in underwater environments
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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