Skip to main content
Springer Nature Link
Log in

Using Particle Filter and Machine Learning for Accuracy Estimation of Robot Localization

  • Conference paper
  • First Online:
Advances and Trends in Artificial Intelligence. From Theory to Practice (IEA/AIE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11606))

  • 2355 Accesses

Abstract

Robot localization is a fundamental capability of all mobile robots. Because of uncertainties in acting and sensing and environmental factors such as people flocking around robots there is always the risk that a robot loses its localization. Very often behaviors of robots rely on a valid position estimation. Thus, for dependability of robot systems it is of great interest for the system to know the state of its localization component. In this paper we present an approach that allows a robot to asses if the localization is still valid. The approach assumes that the underlying localization approach is based on a particle filter. We use deep learning to identify temporal patterns in the particles in the case of losing/lost localization in combination with weak classifiers from the particle set and perception for boosted learning of a localization monitor. The approach is evaluated in a simulated transport robot environment where a degraded localization is provoked by disturbances cased by dynamic obstacles.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Carata, S.V., Neagoe, V.E.: A pulse-coupled neural network approach for image segmentation and its pattern recognition application. In: 2016 International Conference on Communications (COMM), pp. 61–64, June 2016

    Google Scholar 

  2. Dolezel, P., Skrabanek, P., Gago, L.: Pattern recognition neural network as a tool for pest birds detection. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–6, December 2016

    Google Scholar 

  3. Eder, M.: Using particle filters and machine learning approaches for state estimation on robot localization scoring. Master’s thesis, Faculty for Computer Science and Biomedical Engineering, Graz University of Technology (2017)

    Google Scholar 

  4. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 38(2), 337–407 (2000)

    Article  MathSciNet  Google Scholar 

  5. Gerkey, B., Vaughan, R.T., Howard, A.: The player/stage project: tools for multi-robot and distributed sensor systems. In: Proceedings of the 11th International Conference on Advanced Robotics, pp. 317–323, June 2003

    Google Scholar 

  6. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  7. Sanguino, T.D.J.M., Gomez, F.P.: Toward simple strategy for optimal tracking and localization of robots with adaptive particle filtering. IEEE/ASME Trans. Mechatron. 21(6), 2793–2804 (2016)

    Article  Google Scholar 

  8. Morales, N., Toledo, J., Acosta, L., Sánchez-Medina, J.: A combined voxel and particle filter-based approach for fast obstacle detection and tracking in automotive applications. IEEE Trans. Intell. Transp. Syst. 18(7), 1824–1834 (2017)

    Article  Google Scholar 

  9. Polani, D.: Kullback-Leibler Divergence, pp. 1087–1088. Springer, New York (2013)

    Google Scholar 

  10. Quigley, M., et al.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)

    Google Scholar 

  11. Röwekämper, J., Sprunk, C., Tipaldi, G.D., Stachniss, C., Pfaff, P., Burgard, W.: On the position accuracy of mobile robot localization based on particle filters combined with scan matching. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3158–3164, October 2012

    Google Scholar 

  12. Schapire, R.E.: A brief introduction to boosting. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, IJCAI 1999, vol. 2, pp. 1401–1406. Morgan Kaufmann Publishers Inc., San Francisco (1999)

    Google Scholar 

  13. Schapire, R.E.: Explaining AdaBoost. In: Schölkopf, B., Luo, Z., Vovk, V. (eds.) Empirical Inference, pp. 37–52. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41136-6_5

    Chapter  Google Scholar 

  14. Shamov, I.A., Shelest, P.S.: Application of the convolutional neural network to design an algorithm for recognition of tower lighthouses. In: 2017 24th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS), pp. 1–2, May 2017

    Google Scholar 

  15. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press, Cambridge (2005)

    MATH  Google Scholar 

  16. Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust Monte Carlo localization for mobile robots. Artif. Intell. 128(1), 99–141 (2001)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IncubedIT Gmbh, Hart bei Graz, Austria

    Matthias Eder & Michael Reip

  2. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Gerald Steinbauer

  3. Institute of Innovation and Industrial Management, Graz University of Technology, Graz, Austria

    Matthias Eder

Authors
  1. Matthias Eder
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Reip
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gerald Steinbauer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthias Eder .

Editor information

Editors and Affiliations

  1. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Franz Wotawa

  2. Department of Applied Informatics, University of Klagenfurt, Klagenfurt, Austria

    Gerhard Friedrich

  3. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Ingo Pill

  4. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Roxane Koitz-Hristov

  5. Department of Computer Science, Texas State University, San Marcos, TX, USA

    Moonis Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Eder, M., Reip, M., Steinbauer, G. (2019). Using Particle Filter and Machine Learning for Accuracy Estimation of Robot Localization. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_60

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-22999-3_60

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22998-6

  • Online ISBN: 978-3-030-22999-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics