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 Jakuba, M., Steinberg, D., Kinsey, James C., Yoerger, Dana R., Camilli, Richard, Pizarro, O. & Williams, S.B. |
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Toward automatic classification of chemical sensor data from autonomous underwater vehicles
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems Conference Proceedings, pp. 4722-4727, Sep, 2011
Presented at 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, USA, 25 Sep. - 30 Sep. 2011
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Abstract
Autonomous underwater vehicles (AUVs) are commonly used to support oceanographic science by providing water-column mapping, seafloor bathymetric and photographic survey, and deep-sea exploration capabilities. In practice, the mapping activities carried out by AUVs consist of flying either pre-programmed tracklines (most propeller-driven AUVs), or else reporting data to human operators at regular intervals that permit retasking (typical for month-long underwater glider deployments). AUVs equipped with the ability to reason about scientific objectives in real time could significantly increase the value of individual deployments by enabling sampling efforts to be focused on targets or areas identified autonomously or semi-autonomously as scientifically interesting [1]. In this paper, we focus on AUV autonomy as it pertains to watercolumn sensing and argue that the classification of water-column sensor data represents an important enabling capability. We demonstrate practical, semi-supervised classification of watercolumn sensor data using a particular Bayesian, non-parametric clustering method, the Variational Dirichlet Process, combined with operator-supplied semantic labeling. The method is applied to the detection of a deep subsea hydrocarbon plume using data collected by theWoods Hole Oceanographics Sentry AUV during an expedition to the Gulf of Mexico following the Deepwater Horizon blowout disaster.
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