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2009
IEEE

Unsupervised Learning of Terrain Appearance for Automated Coral Reef Exploration

4 years 4 months ago
Unsupervised Learning of Terrain Appearance for Automated Coral Reef Exploration
We describe a navigation and coverage system based on unsupervised learning driven by visual input. Our objective is to allow a robot to remain continuously moving above a terrain of interest using visual feedback to avoid leaving this region. As a particular application domain, we are interested in doing this in open water, but the approach makes few domain-specific assumptions. Specifically, our system employed an unsupervised learning technique to train a kNearest Neighbor classifier to distinguish between images of different terrain types through image segmentation. A simple random exploration strategy was used with this classifier to allow the robot to collect data while remaining confined above a coral reef, without the need to maintain pose estimates. We tested the technique in simulation, and a live deployment was conducted in open water. During the latter, the robot successfully navigated autonomously above a coral reef during a 20 minutes period.
Philippe Giguère, Gregory Dudek, Chris Prah
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where CRV
Authors Philippe Giguère, Gregory Dudek, Chris Prahacs, Nicolas Plamondon, Katrine Turgeon
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