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Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach

© OCEANS 2009-EUROPE : 2009 : OCEANS ’09, 2009, p. 1-6

IEEE

Author: Mallios, Angelos
Ridao Rodríguez, Pere
Hernàndez Bes, Emili
Ribas Romagós, David
Maurelli, Francesco
Petillot, Yvan R.
Date: 2009
Abstract: This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach
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Citation: Mallios, A., Ridao, P., Hernández, E., Ribas, D., Maurelli, F., i Petillot, Y. (2009). Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar . OCEANS 2009-EUROPE : 2009 : OCEANS ’09, 1-6. Recuperat 04 maig 2010, a http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5278219
ISBN: 978-1-4244-2522-8
Document access: http://hdl.handle.net/10256/2160
Language: eng
Publisher: IEEE
Collection: Reproducció digital del document publicat a: http://dx.doi.org/10.1109/OCEANSE.2009.5278219
Articles publicats (D-ATC)
Is part of: © OCEANS 2009-EUROPE : 2009 : OCEANS ’09, 2009, p. 1-6
Rights: Tots els drets reservats
Subject: Robots mòbils
Robots mòbils -- Sistemes de control
Vehicles submergibles -- Sistemes de control
Vehicles submergibles -- Telecontrol
Mobile robots
Mobile robots -- Control systems
Submersibles -- Control systems
Submersibles -- Remote control
Title: Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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