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Appearance-Based Loop Closure Detection with Scale-Restrictive Visual Features

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Computer Vision Systems (ICVS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11754))

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Abstract

In this paper, an appearance-based loop closure detection pipeline for autonomous robots is presented. Our method uses scale-restrictive visual features for image representation with a view to reduce the computational cost. In order to achieve this, a training process is performed, where a feature matching technique indicates the features’ repeatability with respect to scale. Votes are distributed into the database through a nearest neighbor method, while a binomial probability function is responsible for the selection of the most suitable loop closing pair. Subsequently, a geometrical consistency check on the chosen pair follows. The method is subjected into an extensive evaluation via a variety of outdoor, publicly-available datasets revealing high recall rates for 100\(\%\) precision, as compared against its baseline version, as well as, other state-of-the-art approaches.

This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK-00737). The paper was partially supported by project ETAA, DUTH Research Committee 81328.

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Author information

Authors and Affiliations

  1. Laboratory of Robotics and Automation, School of Engineering, Department of Production and Management Engineering, Democritus University of Thrace, 67132, Xanthi, Greece

    Konstantinos A. Tsintotas, Panagiotis Giannis, Loukas Bampis & Antonios Gasteratos

Authors
  1. Konstantinos A. Tsintotas
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  2. Panagiotis Giannis
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  3. Loukas Bampis
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  4. Antonios Gasteratos
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Corresponding author

Correspondence to Konstantinos A. Tsintotas .

Editor information

Editors and Affiliations

  1. Centre for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Greece

    Dimitrios Tzovaras

  2. Centre for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Greece

    Dimitrios Giakoumis

  3. Vienna University of Technology, Vienna, Austria

    Markus Vincze

  4. Foundation for Research and Technology Hellas (FORTH), Heraklion, Greece

    Antonis Argyros

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Tsintotas, K.A., Giannis, P., Bampis, L., Gasteratos, A. (2019). Appearance-Based Loop Closure Detection with Scale-Restrictive Visual Features. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_7

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  • DOI: https://doi.org/10.1007/978-3-030-34995-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34994-3

  • Online ISBN: 978-3-030-34995-0

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