Robust vision‐based underwater homing using self‐similar landmarks
Identifieur interne : 008749 ( Main/Exploration ); précédent : 008748; suivant : 008750Robust vision‐based underwater homing using self‐similar landmarks
Auteurs : Amaury Negre [France] ; Cedric Pradalier [Australie] ; Matthew Dunbabin [Australie]Source :
- Journal of Field Robotics [ 1556-4959 ] ; 2008-06.
Descripteurs français
- Wicri :
- topic : Acoustique, Conférence internationale, Milieu marin, Robotique.
English descriptors
- KwdEn :
- Acoustics, Algorithm, Apparent radius, Autonomous, Autonomous docking, Blur, Briggs, Brightness normalization, Cambridge university press, Camera model, Circular landmark, Circular target, Computation time, Computer vision, Contrast change, Corke, Detection performance, Detection properties, Directional blur, Displacement model, Docking, Dunbabin, Electromagnetic guidance, Equilateral triangle, Experimental results, Field figure, Field robotics, Function decreases, Function exhibits, Function response, Hartley zisserman, Homing, Homing experiment, Horizontal blur, Horizontal direction, Image contrast, Image frame, Image intensity, Image plane, Incorrect solutions, International conference, Landmark, Lighting conditions, Lighting discontinuity, Lighting problem, Limited processing power, Longitudinal motion, Lookup tables, Lter, Many times, Marine environment, Monocular vision, Monocular vision system, Motion blur, Negre, Observation model, Opencv toolkit, Original landmark, Original landmark application, Outdoor experiments, Passive targets, Perspective transformation, Pixel, Poor lighting conditions, Practical limitations, Range estimation, Real size, Real time, Relative target position, Robotics, Robust, Robust target, Standoff, Standoff distance, Stereo vision system, Target, Target algorithm, Target detection, Target localization, Target location, Target moves, Target position, Target system, Test tank, Underwater docking, Underwater images, Underwater targets, Various transformations, Vehicle control, Visible landmarks, Vision systems, Wiley periodicals, Yates sorrell.
- Teeft :
- Acoustics, Algorithm, Apparent radius, Autonomous, Autonomous docking, Blur, Briggs, Brightness normalization, Cambridge university press, Camera model, Circular landmark, Circular target, Computation time, Computer vision, Contrast change, Corke, Detection performance, Detection properties, Directional blur, Displacement model, Docking, Dunbabin, Electromagnetic guidance, Equilateral triangle, Experimental results, Field figure, Field robotics, Function decreases, Function exhibits, Function response, Hartley zisserman, Homing, Homing experiment, Horizontal blur, Horizontal direction, Image contrast, Image frame, Image intensity, Image plane, Incorrect solutions, International conference, Landmark, Lighting conditions, Lighting discontinuity, Lighting problem, Limited processing power, Longitudinal motion, Lookup tables, Lter, Many times, Marine environment, Monocular vision, Monocular vision system, Motion blur, Negre, Observation model, Opencv toolkit, Original landmark, Original landmark application, Outdoor experiments, Passive targets, Perspective transformation, Pixel, Poor lighting conditions, Practical limitations, Range estimation, Real size, Real time, Relative target position, Robotics, Robust, Robust target, Standoff, Standoff distance, Stereo vision system, Target, Target algorithm, Target detection, Target localization, Target location, Target moves, Target position, Target system, Test tank, Underwater docking, Underwater images, Underwater targets, Various transformations, Vehicle control, Visible landmarks, Vision systems, Wiley periodicals, Yates sorrell.
Abstract
Next‐generation autonomous underwater vehicles (AUVs) will be required to robustly identify underwater targets for tasks such as inspection, localization, and docking. Given their often unstructured operating environments, vision offers enormous potential in underwater navigation over more traditional methods; however, reliable target segmentation often plagues these systems. This paper addresses robust vision‐based target recognition by presenting a novel scale and rotationally invariant target design and recognition routine based on self‐similar landmarks that enables robust target pose estimation with respect to a single camera. These algorithms are applied to an AUV with controllers developed for vision‐based docking with the target. Experimental results show that the system performs exceptionally on limited processing power and demonstrates how the combined vision and controller system enables robust target identification and docking in a variety of operating conditions. © 2008 Wiley Periodicals, Inc.
Url:
DOI: 10.1002/rob.20246
Affiliations:
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Le document en format XML
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type="ISSN">1556-4959</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Acoustics</term><term>Algorithm</term><term>Apparent radius</term><term>Autonomous</term><term>Autonomous docking</term><term>Blur</term><term>Briggs</term><term>Brightness normalization</term><term>Cambridge university press</term><term>Camera model</term><term>Circular landmark</term><term>Circular target</term><term>Computation time</term><term>Computer vision</term><term>Contrast change</term><term>Corke</term><term>Detection performance</term><term>Detection properties</term><term>Directional blur</term><term>Displacement model</term><term>Docking</term><term>Dunbabin</term><term>Electromagnetic guidance</term><term>Equilateral triangle</term><term>Experimental results</term><term>Field figure</term><term>Field robotics</term><term>Function decreases</term><term>Function exhibits</term><term>Function response</term><term>Hartley zisserman</term><term>Homing</term><term>Homing experiment</term><term>Horizontal blur</term><term>Horizontal direction</term><term>Image contrast</term><term>Image frame</term><term>Image intensity</term><term>Image plane</term><term>Incorrect solutions</term><term>International conference</term><term>Landmark</term><term>Lighting conditions</term><term>Lighting discontinuity</term><term>Lighting problem</term><term>Limited processing power</term><term>Longitudinal motion</term><term>Lookup tables</term><term>Lter</term><term>Many times</term><term>Marine environment</term><term>Monocular vision</term><term>Monocular vision system</term><term>Motion blur</term><term>Negre</term><term>Observation model</term><term>Opencv toolkit</term><term>Original landmark</term><term>Original landmark application</term><term>Outdoor experiments</term><term>Passive targets</term><term>Perspective transformation</term><term>Pixel</term><term>Poor lighting conditions</term><term>Practical limitations</term><term>Range estimation</term><term>Real size</term><term>Real time</term><term>Relative target position</term><term>Robotics</term><term>Robust</term><term>Robust target</term><term>Standoff</term><term>Standoff distance</term><term>Stereo vision system</term><term>Target</term><term>Target algorithm</term><term>Target detection</term><term>Target localization</term><term>Target location</term><term>Target moves</term><term>Target position</term><term>Target system</term><term>Test tank</term><term>Underwater docking</term><term>Underwater images</term><term>Underwater targets</term><term>Various transformations</term><term>Vehicle control</term><term>Visible landmarks</term><term>Vision systems</term><term>Wiley periodicals</term><term>Yates sorrell</term></keywords><keywords scheme="Teeft" xml:lang="en"><term>Acoustics</term><term>Algorithm</term><term>Apparent radius</term><term>Autonomous</term><term>Autonomous docking</term><term>Blur</term><term>Briggs</term><term>Brightness normalization</term><term>Cambridge university press</term><term>Camera model</term><term>Circular landmark</term><term>Circular target</term><term>Computation time</term><term>Computer vision</term><term>Contrast change</term><term>Corke</term><term>Detection performance</term><term>Detection properties</term><term>Directional blur</term><term>Displacement model</term><term>Docking</term><term>Dunbabin</term><term>Electromagnetic guidance</term><term>Equilateral triangle</term><term>Experimental results</term><term>Field figure</term><term>Field robotics</term><term>Function decreases</term><term>Function exhibits</term><term>Function response</term><term>Hartley zisserman</term><term>Homing</term><term>Homing experiment</term><term>Horizontal blur</term><term>Horizontal direction</term><term>Image contrast</term><term>Image frame</term><term>Image intensity</term><term>Image plane</term><term>Incorrect solutions</term><term>International conference</term><term>Landmark</term><term>Lighting conditions</term><term>Lighting discontinuity</term><term>Lighting problem</term><term>Limited processing power</term><term>Longitudinal motion</term><term>Lookup tables</term><term>Lter</term><term>Many times</term><term>Marine environment</term><term>Monocular vision</term><term>Monocular vision system</term><term>Motion blur</term><term>Negre</term><term>Observation model</term><term>Opencv toolkit</term><term>Original landmark</term><term>Original landmark application</term><term>Outdoor experiments</term><term>Passive targets</term><term>Perspective transformation</term><term>Pixel</term><term>Poor lighting conditions</term><term>Practical limitations</term><term>Range estimation</term><term>Real size</term><term>Real time</term><term>Relative target position</term><term>Robotics</term><term>Robust</term><term>Robust target</term><term>Standoff</term><term>Standoff distance</term><term>Stereo vision system</term><term>Target</term><term>Target algorithm</term><term>Target detection</term><term>Target localization</term><term>Target location</term><term>Target moves</term><term>Target position</term><term>Target system</term><term>Test tank</term><term>Underwater docking</term><term>Underwater images</term><term>Underwater targets</term><term>Various transformations</term><term>Vehicle control</term><term>Visible landmarks</term><term>Vision systems</term><term>Wiley periodicals</term><term>Yates sorrell</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Acoustique</term><term>Conférence internationale</term><term>Milieu marin</term><term>Robotique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Next‐generation autonomous underwater vehicles (AUVs) will be required to robustly identify underwater targets for tasks such as inspection, localization, and docking. Given their often unstructured operating environments, vision offers enormous potential in underwater navigation over more traditional methods; however, reliable target segmentation often plagues these systems. This paper addresses robust vision‐based target recognition by presenting a novel scale and rotationally invariant target design and recognition routine based on self‐similar landmarks that enables robust target pose estimation with respect to a single camera. These algorithms are applied to an AUV with controllers developed for vision‐based docking with the target. Experimental results show that the system performs exceptionally on limited processing power and demonstrates how the combined vision and controller system enables robust target identification and docking in a variety of operating conditions. © 2008 Wiley Periodicals, Inc.</div></front></TEI><affiliations><list><country><li>Australie</li><li>France</li></country></list><tree><country name="France"><noRegion><name sortKey="Negre, Amaury" sort="Negre, Amaury" uniqKey="Negre A" first="Amaury" last="Negre">Amaury Negre</name></noRegion><name sortKey="Negre, Amaury" sort="Negre, Amaury" uniqKey="Negre A" first="Amaury" last="Negre">Amaury Negre</name><name sortKey="Negre, Amaury" sort="Negre, Amaury" uniqKey="Negre A" first="Amaury" last="Negre">Amaury Negre</name></country><country name="Australie"><noRegion><name sortKey="Pradalier, Cedric" sort="Pradalier, Cedric" uniqKey="Pradalier C" first="Cedric" last="Pradalier">Cedric Pradalier</name></noRegion><name sortKey="Dunbabin, Matthew" sort="Dunbabin, Matthew" uniqKey="Dunbabin M" first="Matthew" last="Dunbabin">Matthew Dunbabin</name><name sortKey="Dunbabin, Matthew" sort="Dunbabin, Matthew" uniqKey="Dunbabin M" first="Matthew" last="Dunbabin">Matthew Dunbabin</name><name sortKey="Dunbabin, Matthew" sort="Dunbabin, Matthew" uniqKey="Dunbabin M" first="Matthew" last="Dunbabin">Matthew Dunbabin</name><name sortKey="Pradalier, Cedric" sort="Pradalier, Cedric" uniqKey="Pradalier C" first="Cedric" last="Pradalier">Cedric Pradalier</name><name sortKey="Pradalier, Cedric" sort="Pradalier, Cedric" uniqKey="Pradalier C" first="Cedric" last="Pradalier">Cedric Pradalier</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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