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Simultaneous Localization, Mapping and Self-body Shape Estimation by a Mobile Robot

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Intelligent Autonomous Systems 14 (IAS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 531))

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Abstract

This paper describes a new method for estimating the body shape of a mobile robot by using sensory-motor information. In many biological systems, it is important to be able to estimate body shapes to allow it to appropriately behave in a complex environment. Humans and other animals can form their body image and determine actions based on their recognized body shape. However, conventional mobile robots have not had the ability to estimate body shape, and instead, developers have provided body shape information to the robots. In this paper, we describe a new method that enables a robot to obtain only subjective information, e.g., motor commands and distance sensor information, and automatically estimate its self-body shape. We call the method simultaneous localization, mapping, and self-body shape estimation (SLAM-SBE). The method is based on Bayesian statistics. In particular, the method is obtained by extending the simultaneous localization and mapping (SLAM) method. Experimental results show that a mobile robot can obtain a self-body shape image represented by an occupancy grid by using only its sensory-motor information (i.e., without any objective measurement of its body).

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Notes

  1. 1.

    We admit that the question of whether a biological system should have a self-body model independent of particular behaviors is open to discussion in embodied cognitive science and related fields.

  2. 2.

    Turtlebot2: http://www.turtlebot.com/.

  3. 3.

    https://www.playstation.com/en-us/explore/accessories/dualshock-3-ps3/.

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Acknowledgements

This research was partially supported by a Grant-in-Aid for Scientific Research on Innovative Areas 2015–2017 (15H01670) funded by the Ministry of Education, Culture, Sports, Science, and Technology, Japan.

Author information

Authors and Affiliations

  1. Ritsumeikan University, 1-1-1 Noji Higashi, Kusatsu, Shiga, 525-8577, Japan

    Akira Taniguchi, Lv WanPeng, Tadahiro Taniguchi, Toshiaki Takano & Yoshinobu Hagiwara

  2. Tokyo University of Agriculture and Technology, 2-24-16 Nakamachi, Koganei, Tokyo, 184-8588, Japan

    Shiro Yano

Authors
  1. Akira Taniguchi
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  2. Lv WanPeng
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  3. Tadahiro Taniguchi
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  4. Toshiaki Takano
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  5. Yoshinobu Hagiwara
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  6. Shiro Yano
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Corresponding authors

Correspondence to Akira Taniguchi , Tadahiro Taniguchi , Toshiaki Takano , Yoshinobu Hagiwara or Shiro Yano .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University , Shanghai, China

    Weidong Chen

  2. Osaka University , Osaka, Japan

    Koh Hosoda

  3. University of Padua , Padua, Italy

    Emanuele Menegatti

  4. Osaka University , Osaka, Japan

    Masahiro Shimizu

  5. Shanghai Jiao Tong University , Shanghai, China

    Hesheng Wang

Appendix

Appendix

The recursive property of the update formula shown in (9) and (10) enables us to derive particle-filter-based SLAM-SBE in the same way as conventional MCL and SLAM. The outline of particle-filter-based SLAM-SBE is presented in Algorithm 3 for reference. We exclude precise descriptions of each function in Algorithm 3. They can be easily derived based on the definitions of SLAM and the derived Bayesian filter.

figure c

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Taniguchi, A., WanPeng, L., Taniguchi, T., Takano, T., Hagiwara, Y., Yano, S. (2017). Simultaneous Localization, Mapping and Self-body Shape Estimation by a Mobile Robot. In: Chen, W., Hosoda, K., Menegatti, E., Shimizu, M., Wang, H. (eds) Intelligent Autonomous Systems 14. IAS 2016. Advances in Intelligent Systems and Computing, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-319-48036-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-48036-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48035-0

  • Online ISBN: 978-3-319-48036-7

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