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Exploiting Structural Properties of Buildings Towards General Semantic Mapping Systems

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Intelligent Autonomous Systems 13

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 302))

Abstract

Semantic mapping is one of the most active and promising research areas within autonomous mobile robotics. Informally, a semantic map associates a high-level human-understandable label (like “office” or “corridor”) to a portion of an environment. Most semantic mapping approaches are based on classifiers that, given some features perceived by robot sensors in a physical place, associate a semantic label to the place. These approaches are often tested on a limited number of homogeneous places (e.g., few rooms within a single building). This line of action seems to hinder the development of methods for constructing semantic maps that can be (re)used in a number of previously unseen environments. In this paper, we aim at contributing to make semantic mapping methods more general. In particular, we focus on indoor environments and we consider the following research question: to what extent are the semantic mapping approaches shown to label rooms in a single building expected to work when applied to different buildings?

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Notes

  1. 1.

    http://home.deib.polimi.it/luperto/datasets/floorplans.

  2. 2.

    We use parameters \(\textit{MinPts} = 6\) and \(\epsilon =3\); \(\epsilon \) is selected using a K-distance plot.

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Acknowledgments

The authors gratefully thanks A. Aydemir and P. Jensfelt for providing the KTH data set and for their collaboration, and S. Teller and E. Whiting for providing the MIT data set.

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Authors and Affiliations

  1. Artificial Intelligence and Robotics Laboratory, Politecnico di Milano, Milan, Italy

    Matteo Luperto & Francesco Amigoni

Authors
  1. Matteo Luperto
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  2. Francesco Amigoni
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Corresponding author

Correspondence to Francesco Amigoni .

Editor information

Editors and Affiliations

  1. Information Engineering, University of Padua, Padua, Italy

    Emanuele Menegatti

  2. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Nathan Michael

  3. Fachbereich Informatik, Technische Universität Kaiserslautern Arbeitsgruppe Robotersysteme, Kaiserslautern, Germany

    Karsten Berns

  4. Integrated Information Technology, Aoyama Gakuin University, Tokyo, Japan

    Hiroaki Yamaguchi

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Luperto, M., Amigoni, F. (2016). Exploiting Structural Properties of Buildings Towards General Semantic Mapping Systems. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_28

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  • DOI: https://doi.org/10.1007/978-3-319-08338-4_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08337-7

  • Online ISBN: 978-3-319-08338-4

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