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Visual SLAM for underwater vehicles using video velocity log and natural landmarks

A visual SLAM system has been implemented and optimised for real-time deployment on an AUV equipped with calibrated stereo cameras. The system incorporates a novel approach to landmark description in which landmarks are local sub maps that consist of a cloud of 3D points and their associated SIFT/SURF descriptors. Landmarks are also sparsely distributed which simplifies and accelerates data association and map updates. In addition to landmark-based localisation the system utilises visual odometry to estimate the pose of the vehicle in 6 degrees of freedom by identifying temporal matches between consecutive local sub maps and computing the motion. Both the extended Kalman filter and unscented Kalman filter have been considered for filtering the observations. The output of the filter is also smoothed using the Rauch-Tung-Striebel (RTS) method to obtain a better alignment of the sequence of local sub maps and to deliver a large-scale 3D acquisition of the surveyed area. Synthetic experiments have been performed using a simulation environment in which ray tracing is used to generate synthetic images for the stereo system

© OCEANS 2008, 2008, p. 1-6

IEEE

Autor: Salvi, Joaquim
Petillot, Yvan R.
Thomas, Stephen
Aulinas Masó, Josep M.
Data: 2008
Resum: A visual SLAM system has been implemented and optimised for real-time deployment on an AUV equipped with calibrated stereo cameras. The system incorporates a novel approach to landmark description in which landmarks are local sub maps that consist of a cloud of 3D points and their associated SIFT/SURF descriptors. Landmarks are also sparsely distributed which simplifies and accelerates data association and map updates. In addition to landmark-based localisation the system utilises visual odometry to estimate the pose of the vehicle in 6 degrees of freedom by identifying temporal matches between consecutive local sub maps and computing the motion. Both the extended Kalman filter and unscented Kalman filter have been considered for filtering the observations. The output of the filter is also smoothed using the Rauch-Tung-Striebel (RTS) method to obtain a better alignment of the sequence of local sub maps and to deliver a large-scale 3D acquisition of the surveyed area. Synthetic experiments have been performed using a simulation environment in which ray tracing is used to generate synthetic images for the stereo system
Format: application/pdf
Cita: Salvi, J., Petillot, Y., Thomas, S., i Aulinas, J. (2008). Visual SLAM for underwater vehicles using video velocity log and natural landmarks. OCEANS 2008, 1-6. Recuperat 04 juny 2010, a http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5151887
ISBN: 978-1-4244-2619-5
Accés al document: http://hdl.handle.net/10256/2494
Llenguatge: eng
Editor: IEEE
Col·lecció: Reproducció digital del document publicat a: http://dx.doi.org/10.1109/OCEANS.2008.5151887
Articles publicats (D-ATC)
És part de: © OCEANS 2008, 2008, p. 1-6
Drets: Tots els drets reservats
Matèria: Imatges -- Processament
Kalman, Filtre de
Robots mòbils
Robots submarins
Vehicles submergibles
Image processing
Kalman filtering G
Mobile robots
Submersibles
Underwater robots
Títol: Visual SLAM for underwater vehicles using video velocity log and natural landmarks
Tipus: info:eu-repo/semantics/article
Repositori: DUGiDocs

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