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Spatially-Constrained Semantic Segmentation with Topological Maps and Visual Embeddings

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Book cover Computer Vision Systems (ICVS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12899))

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Abstract

Semantic mapping has received much attention in the recent years due to the fact that more and more robots need to operate in complex environments and co-exist with humans or other robots. This requires contemporary robots not only to be able to navigate safely in their environment, but also to adopt a human-like understanding of their surroundings i.e. to have a semantic apprehension of the mapped environment. This paper at hand, focuses on building successfully semantic maps by combining spatial knowledge on the occupancy grid map with deep learning and computer vision techniques. The presented method exploits the vision based data captured by robot’s perception system, to ease its installation in the operational environment. Unlike previous methods, the proposed one does not require any prior knowledge of the environment concerning the semantics of places, as it is semi-supervised and the labeling of the segmented areas is being performed manually after the process is completed. The method has been evaluated in real indoor environments by measuring the Jaccard and Dice indices for the performed segmentation and exhibited remarkable performance.

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Acknowledgments

This work has been supported from General Secretariat for Research and Technology under Grant agreement no. T6YB-00238 “Q-CONPASS: Dynamic Quality CONtrol on Production lines using intelligent AutonomouS vehicleS”.

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Authors and Affiliations

  1. Centre for Research and Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi Road, 57001, Thermi-Thessaloniki, Greece

    Christina Theodoridou, Andreas Kargakos, Ioannis Kostavelis, Dimitrios Giakoumis & Dimitrios Tzovaras

Authors
  1. Christina Theodoridou
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  2. Andreas Kargakos
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  3. Ioannis Kostavelis
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  4. Dimitrios Giakoumis
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  5. Dimitrios Tzovaras
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Corresponding author

Correspondence to Christina Theodoridou .

Editor information

Editors and Affiliations

  1. TU Wien, Vienna, Austria

    Markus Vincze

  2. University of Technology Sydney, Sydney, Australia

    Timothy Patten

  3. University of California San Diego, La Jolla, CA, USA

    Henrik I Christensen

  4. Technical University of Denmark, Kongens Lyngby, Denmark

    Lazaros Nalpantidis

  5. Hong Kong University of Science and Technology, Hong Kong, China

    Ming Liu

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Theodoridou, C., Kargakos, A., Kostavelis, I., Giakoumis, D., Tzovaras, D. (2021). Spatially-Constrained Semantic Segmentation with Topological Maps and Visual Embeddings. In: Vincze, M., Patten, T., Christensen, H.I., Nalpantidis, L., Liu, M. (eds) Computer Vision Systems. ICVS 2021. Lecture Notes in Computer Science(), vol 12899. Springer, Cham. https://doi.org/10.1007/978-3-030-87156-7_10

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  • DOI: https://doi.org/10.1007/978-3-030-87156-7_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87155-0

  • Online ISBN: 978-3-030-87156-7

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