Premium
Robust vision‐based underwater homing using self‐similar landmarks
Author(s) -
Negre Amaury,
Pradalier Cedric,
Dunbabin Matthew
Publication year - 2008
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.20246
Subject(s) - artificial intelligence , computer vision , underwater , computer science , robustness (evolution) , machine vision , pose , segmentation , engineering , biochemistry , oceanography , chemistry , gene , geology
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.