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Robust Vision-based Underwater Target Identification & Homing Using Self-Similar Landmarks

Abstract : Next generation Autonomous Underwater Vehicles (AUVs) will be required to robustly identify underwater targets for tasks such as inspection, localisation 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 (SSL) 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 system performs exceptionally on limited processing power and demonstrates how the combined vision and controller systems enables robust target identification and docking in a variety of operating conditions.
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Submitted on : Tuesday, January 22, 2008 - 10:33:00 AM
Last modification on : Saturday, June 25, 2022 - 7:36:04 PM
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  • HAL Id : inria-00211881, version 1



Amaury Nègre, Cédric Pradalier, Matthew Dunbabin. Robust Vision-based Underwater Target Identification & Homing Using Self-Similar Landmarks. Field And Service Robotics, Jul 2007, Chamonix, France. ⟨inria-00211881⟩



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