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Making Sense of Indoor Spaces Using Semantic Web Mining and Situated Robot Perception

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Abstract

Intelligent Autonomous Robots deployed in human environments must have understanding of the wide range of possible semantic identities associated with the spaces they inhabit – kitchens, living rooms, bathrooms, offices, garages, etc. We believe robots should learn this information through their own exploration and situated perception in order to uncover and exploit structure in their environments – structure that may not be apparent to human engineers, or that may emerge over time during a deployment. In this work, we combine semantic web-mining and situated robot perception to develop a system capable of assigning semantic categories to regions of space. This is accomplished by looking at web-mined relationships between room categories and objects identified by a Convolutional Neural Network trained on 1000 categories. Evaluated on real-world data, we show that our system exhibits several conceptual and technical advantages over similar systems, and uncovers semantic structure in the environment overlooked by ground-truth annotators.
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Dates and versions

hal-01657672 , version 1 (07-12-2017)

Identifiers

Cite

Jay Young, Valerio Basile, Markus Suchi, Lars Kunze, Nick Hawes, et al.. Making Sense of Indoor Spaces Using Semantic Web Mining and Situated Robot Perception. AnSWeR 2017 - 1st International Workshop on Application of Semantic Web technologies in Robotics, May 2017, Portoroz, Slovenia. pp.299-313, ⟨10.1007/978-3-319-70407-4_39⟩. ⟨hal-01657672⟩
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