JBoost Optimization of Color Detectors for Autonomous Underwater Vehicle Navigation

In the world of autonomous underwater vehicles (AUV) the prominent form of sensing is sonar due to cloudy water conditions and dispersion of light. Although underwater conditions are highly suitable for sonar, this does not mean that optical sensors should be completely ignored. There are situations where visibility is high, such as in calm waters, and where light dispersion is not significant, such as in shallow water or near the surface. In addition, even when visibility is low, once a certain proximity to an object exists, visibility can increase. The focus of this paper is this gap in capability for AUVs, with an emphasis on computer-aided detection through classifier optimization via machine learning. This paper describes the development of color-based classification algorithm and its application as a cost-sensitive alternative for navigation on the small Stingray AUV.

[1]  Peter I. Corke,et al.  Data muling over underwater wireless sensor networks using an autonomous underwater vehicle , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[2]  Gian Luca Foresti,et al.  A vision-based system for autonomous underwater vehicle navigation , 1998, IEEE Oceanic Engineering Society. OCEANS'98. Conference Proceedings (Cat. No.98CH36259).

[3]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..

[4]  Tamaki Ura,et al.  Vision based autonomous underwater vehicle navigation: underwater cable tracking , 1997, Oceans '97. MTS/IEEE Conference Proceedings.

[5]  Primo Zingaretti,et al.  Robust real-time detection of an underwater pipeline , 1998 .

[6]  Yonina C. Eldar,et al.  A probabilistic Hough transform , 1991, Pattern Recognit..

[7]  Hayato Kondo,et al.  Navigation of autonomous underwater vehicles based on artificial underwater landmarks , 2001, MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295).

[8]  C. Saloma,et al.  Image classification of coral reef components from underwater color video , 2001, MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295).