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Field and Service Robotics pp 629–642Cite as

A Self-Learning Ground Classifier Using Radar Features

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Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 92))

Abstract

Autonomous off-road ground vehicles require advanced perception systems in order to sense and understand the surrounding environment, while ensuring robustness under compromised visibility conditions. In this paper, the use of millimeter wave radar is proposed as a possible solution for all-weather off-road perception. A self-learning ground classifier is developed that segments radar data for scene understanding and autonomous navigation tasks. The proposed system comprises two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate appearance of radar data with class labels. Then, it makes predictions based on past observations. The training set is continuously updated online using the latest radar readings, thus making it feasible to use the system for long range and long duration navigation, over changing environments. Experimental results, obtained with an unmanned ground vehicle operating in a rural environment, are presented to validate this approach. Conclusions are drawn on the utility of millimeter-wave radar as a robotic sensor for persistent and accurate perception in natural scenarios.

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Notes

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    http://www.nav-tech.com/Industrial%20Sensors2.htm

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Acknowledgments

The authors are thankful to the Australian Department of Education, Employment and Workplace Relations for supporting the project through the 2010 Endeavour Research Fellowship 1745_2010. The authors would like also to thank the National Research Council, Italy, for supporting this work under the CNR 2010 Short Term Mobility program. This research was undertaken through the Centre for Intelligent Mobile Systems (CIMS), and was funded by BAE Systems as part of an ongoing partnership with the University of Sydney. The financial support of the ERA-NET ICT-AGRI through the grant Ambient Awareness for Autonomous Agricultural Vehicles (QUAD-AV) is also gratefully acknowledged.

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Authors and Affiliations

  1. Department of Engineering for Innovation, University of Salento, 73100, Lecce, Italy

    Giulio Reina

  2. Institute of Intelligent Systems for Automation (ISSIA), National Research Council (CNR), via G. Amendola 122/D, 70126, Bari, Italy

    Annalisa Milella

  3. Australian Centre for Field Robotics, School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW, 2006, Australia

    James Underwood

Authors
  1. Giulio Reina
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  2. Annalisa Milella
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  3. James Underwood
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Corresponding author

Correspondence to Giulio Reina .

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Editors and Affiliations

  1. Department of Aerospace Engineering, Tohoku University, Sendai, Japan

    Kazuya Yoshida

  2. Tohoku University Graduate School of Information Sciences, Sendai, Japan

    Satoshi Tadokoro

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Reina, G., Milella, A., Underwood, J. (2014). A Self-Learning Ground Classifier Using Radar Features. In: Yoshida, K., Tadokoro, S. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 92. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40686-7_42

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  • DOI: https://doi.org/10.1007/978-3-642-40686-7_42

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40685-0

  • Online ISBN: 978-3-642-40686-7

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