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

  • Jay Young
  • Valerio Basile
  • Markus Suchi
  • Lars Kunze
  • Nick Hawes
  • Markus Vincze
  • Barbara Caputo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10577)

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.

Keywords

Robotics Artificial intelligence Semantic web-mining Deep vision Service robots Machine learning Space classification Semantic mapping Imagenet Convolutional Neural Networks 

Notes

Acknowledgments

The research leading to these results has received funding from EU FP7 grant agreement No. 600623, STRANDS, and CHIST-ERA Project ALOOF.

The authors also wish to thank Daniel Hayes for his careful and attentive contributions upon which this work depends.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jay Young
    • 1
  • Valerio Basile
    • 2
  • Markus Suchi
    • 3
  • Lars Kunze
    • 4
  • Nick Hawes
    • 1
  • Markus Vincze
    • 3
  • Barbara Caputo
    • 5
  1. 1.The University of BirminghamBirminghamUK
  2. 2.Université Côte d’Azur, Inria, CNRS, I3SSophia AntipolisFrance
  3. 3.Technische Universität WienViennaAustria
  4. 4.Oxford Robotics Institute, Department of Engineering ScienceUniversity of OxfordOxfordUK
  5. 5.Università di Roma - SapienzaRomeItaly

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