Talks and Poster Presentations (with Proceedings-Entry):

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.

English 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, 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

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

Created from the Publication Database of the Vienna University of Technology.