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Robots often operate in built environments containing underlying structure that can be exploited to help predict future observations. In this work, we present a framework based on convolutional neural networks to predict point of interest locations in structured environments. The proposed technique exploits the inherent structure of the environment to train a convolutional neural network that is leveraged...
Robots often operate in built environments containing underlying structure that can be exploited to help predict future observations. In this work, we present a deep learning based approach to predict exit locations of buildings. This technique exploits the inherent structure of buildings to create a model. A convolutional neural network is trained using a database of building blueprints and used...
Monitoring marine ecosystems is challenging due to the dynamic and unpredictable nature of environmental phenomena. In this work we survey a series of techniques used in information gathering that can be used to increase experts' understanding of marine ecosystems through dynamic monitoring. To achieve this, an underwater glider simulator is constructed, and four different path planning algorithms...
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