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International Work-Conference on Artificial Neural Networks

IWANN 2019: Advances in Computational Intelligence pp 313–324Cite as

Integration of CNN into a Robotic Architecture to Build Semantic Maps of Indoor Environments

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11507))

Abstract

In robotics, semantic mapping refers to the construction of a rich representation of the environment that includes high level information needed by the robot to accomplish its tasks. Building a semantic map requires algorithms to process sensor data at different levels: geometric, topological and object detections/categories, which must be integrated into an unified model. This paper describes a robotic architecture that successfully builds such semantic maps for indoor environments. For this purpose, within a ROS-based ecosystem, we apply a state-of-the-art Convolutional Neural Network (CNN), concretely YOLOv3, for detecting objects in images. The detection results are placed within a geometric map of the environment making use of a number of modules of the architecture: robot localization, camera extrinsic calibration, data form a depth camera, etc. We demonstrate the suitability of the proposed framework by building semantic maps of several home environments from the Robot@Home dataset, using Unity 3D as a tool to visualize the maps as well as to provide future robotic developments.

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Notes

  1. 1.

    http://wiki.ros.org/amcl.

  2. 2.

    http://wiki.ros.org/gmapping.

  3. 3.

    https://unity3d.com.

  4. 4.

    http://wiki.ros.org/rosbridge_suite.

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Acknowledgments

This work has been supported by the research projects WISER (DPI2017-84827-R), funded by the Spanish Government and financed by the European Regional Development’s funds (FEDER), MoveCare (ICT-26-2016b-GA-732158), funded by the European H2020 program, and by a postdoc contract from the I-PPIT program of the University of Málaga, and the UG PHD scholarship program from the University of Groningen.

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Authors and Affiliations

  1. Machine Perception and Intelligent Robotics group (MAPIR), Department of System Engineering and Automation, Biomedical Research Institute of Malaga (IBIMA), University of Malaga, Málaga, Spain

    D. Chaves, J. R. Ruiz-Sarmiento & J. Gonzalez-Jimenez

  2. Johann Bernoulli Institute of Mathematics and Computing Science, University of Groningen, Groningen, The Netherlands

    D. Chaves & N. Petkov

Authors
  1. D. Chaves
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  2. J. R. Ruiz-Sarmiento
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  3. N. Petkov
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  4. J. Gonzalez-Jimenez
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Corresponding author

Correspondence to D. Chaves .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Chaves, D., Ruiz-Sarmiento, J.R., Petkov, N., Gonzalez-Jimenez, J. (2019). Integration of CNN into a Robotic Architecture to Build Semantic Maps of Indoor Environments. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_27

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  • DOI: https://doi.org/10.1007/978-3-030-20518-8_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20517-1

  • Online ISBN: 978-3-030-20518-8

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