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Exploiting Structural Properties of Buildings Towards General Semantic Mapping Systems

  • Matteo Luperto
  • Francesco Amigoni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (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?

Keywords

Semantic mapping Place categorization Density-based spatial clustering 

Notes

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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Artificial Intelligence and Robotics LaboratoryPolitecnico di MilanoMilanItaly

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