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Improving Traversability Estimation Through Autonomous Robot Experimentation

  • Christos Sevastopoulos
  • Katerina Maria Oikonomou
  • Stasinos KonstantopoulosEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)

Abstract

The ability to have unmanned ground vehicles navigate unmapped off-road terrain has high impact potential in application areas ranging from supply and logistics, to search and rescue, to planetary exploration. To achieve this, robots must be able to estimate the traversability of the terrain they are facing, in order to be able to plan a safe path through rugged terrain. In the work described here, we pursue the idea of fine-tuning a generic visual recognition network to our task and to new environments, but without requiring any manually labelled data. Instead, we present an autonomous data collection method that allows the robot to derive ground truth labels by attempting to traverse a scene and using localization to decide if the traversal was successful. We then present and experimentally evaluate two deep learning architectures that can be used to adapt a pre-trained network to a new environment. We prove that the networks successfully adapt to their new task and environment from a relatively small dataset.

Keywords

Robot vision Adaptability Self-assessment 

References

  1. 1.
    Adhikari, S.P., Yang, C., Slot, K., Kim, H.: Accurate natural trail detection using a combination of a deep neural network and dynamic programming. Sensors 18(1) (2018).  https://doi.org/10.3390/s18010178CrossRefGoogle Scholar
  2. 2.
    Angelova, A., Matthies, L., Helmick, D., Perona, P.: Fast terrain classification using variable-length representation for autonomous navigation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1–8. IEEE (2007)Google Scholar
  3. 3.
    Baldi, P., Sadowski, P.J.: Understanding dropout. In: Proceedings of the 2013 Conference on Neural Information Processing Systems (NIPS 2013) (2013)Google Scholar
  4. 4.
    Bellone, M.: Watch your step! terrain traversability for robot control. In: Gorrostieta Hurtado, E. (ed.) Robot Control, Chap. 6. InTech (Oct 2016)Google Scholar
  5. 5.
    Bellutta, P., Manduchi, R., Matthies, L., Owens, K., Rankin, A.: Terrain perception for DEMO III. In: Proceedings of the IEEE Intelligent Vehicles Symposium, IV 2000, pp. 326–331. IEEE (2000)Google Scholar
  6. 6.
    Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)Google Scholar
  7. 7.
    Howard, A., Seraji, H., Tunstel, E.: A rule-based fuzzy traversability index for mobile robot navigation. In: Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation, vol. 3, pp. 3067–3071. IEEE (2001)Google Scholar
  8. 8.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of ICLR 2015 (2015). arXiv:1412.6980
  9. 9.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  10. 10.
    Rasmussen, C., Lu, Y., Kocamaz, M.: Appearance contrast for fast, robust trail-following. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), St. Louis, MO, USA, 10–15 October 2009Google Scholar
  11. 11.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 2015 International Conference on Learning Representations (2015). arXiv:1409.1556
  12. 12.
    Tieleman, T., Hinton, G.: RMSProp: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks Mach. Learn. 4(2), 26–31 (2012)Google Scholar
  13. 13.
    Wermelinger, M., Fankhauser, P., Diethelm, R., Krüsi, P., Siegwart, R., Hutter, M.: Navigation planning for legged robots in challenging terrain. In: Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), Daejeon, South Korea, October 2016Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Christos Sevastopoulos
    • 1
  • Katerina Maria Oikonomou
    • 1
  • Stasinos Konstantopoulos
    • 1
    Email author
  1. 1.Institute of Informatics and Telecommunications, NCSR ‘Demokritos’Ag. PartaskeviGreece

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