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.

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

Basile V.;
2017-01-01

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.
2017
14th International Conference on Semantic Web, ESWC 2017
Portoroz
2017
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Springer Verlag
10577
299
313
978-3-319-70406-7
978-3-319-70407-4
Artificial intelligence; Convolutional Neural Networks; Deep vision; Imagenet; Machine learning; Robotics; Semantic mapping; Semantic web-mining; Service robots; Space classification
Young J.; Basile V.; Suchi M.; Kunze L.; Hawes N.; Vincze M.; Caputo B.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1759779
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