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  1. Pubblicazioni

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

Contributo in Atti di convegno
Data di Pubblicazione:
2017
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.
Tipologia CRIS:
04A-Conference paper in volume
Keywords:
Artificial intelligence; Convolutional Neural Networks; Deep vision; Imagenet; Machine learning; Robotics; Semantic mapping; Semantic web-mining; Service robots; Space classification
Elenco autori:
Young J.; Basile V.; Suchi M.; Kunze L.; Hawes N.; Vincze M.; Caputo B.
Autori di Ateneo:
BASILE Valerio
Link alla scheda completa:
https://iris.unito.it/handle/2318/1759779
Link al Full Text:
https://iris.unito.it/retrieve/handle/2318/1759779/671718/valerio.pdf
Titolo del libro:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pubblicato in:
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
Journal
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
Series
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