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J. Young, V. Basile, M. Suchi, L. Kunze, N. Hawes, M. Vincze, B. Caputo:
"Making Sense of Indoor Spaces using Semantic Web Mining and Situated Robot Perception";
Talk: ESWC European Semantic Web Conference 2017, Portoroz, Slowenien; 05-28-2017 - 06-01-2017; in: "European Semantic Web Conference ESWC 2017", (2017), 10 pages.

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, o ces, 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 webmining
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
identi ed 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.

robotics, arti cial intelligence, semantic web-mining, deep vision, service robots, machine learning, space classi cation, semantic mapping, imagenet, convolutional neural networks