Skip to main content
SpringerLink
Log in

Behavior-Based Obstacle Detection in Off-Road Environments Considering Data Quality

  • Conference paper
  • First Online:
Book cover Informatics in Control, Automation and Robotics (ICINCO 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 495))

Abstract

In off-road environments, the assessment and classification in travers-able and non-traversable areas is a challenging task. Not only does the drive-ability depend on the vehicle’s state in combination with the environmental geometry, but also the assessment is complicated by noisy and wrong sensor data. Thereby, faulty evaluation may lead to severe harm to goods or people since either safety issues or reliability problems are caused. While for the control part behavior-based systems proved to be suitable due to their inherent robustness against unforeseen situations, robust perception is still an unsolved problem leading to severe system failures. This paper faces the perception problem by introducing a new data quality-based perception module based on the integrated Behavior-Based Control (iB2C) architecture. Therefore, a new concept of data quality in behavior-based systems and methods for quality aware data fusion are developed while taking advantage of the modularity, extensibility and traceability of the existing architecture.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Berns, K., Kuhnert, K.D., Armbrust, C.: Off-road robotics - an overview. KI - Künstliche Intell. 25, 109–116 (2011)

    Article  Google Scholar 

  2. Mouhagir, H., Cherfaoui, V., Talj, R., Aioun, F., Guillemard, F.: Using evidential occupancy grid for vehicle trajectory planning under uncertainty with tentacles. In: IEEE 20th International Conference on Intelligent Transportation (ITSC 2017), Yokohama, Japan (2017)

    Google Scholar 

  3. Khaleghi, B., Khamis, A., Karray, F.O., Razavi, S.N.: Multisensor data fusion: a review of the state-of-the-art. Inf. Fusion 14, 28–44 (2013)

    Article  Google Scholar 

  4. Bader, K., Lussier, B., Schön, W.: A fault tolerant architecture for data fusion: a real application of Kalman filters for mobile robot localization. Robot. Auton. Syst. 88, 11–23 (2017)

    Article  Google Scholar 

  5. Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82, 35–45 (1960)

    Article  Google Scholar 

  6. Brooks, R.A.: A robust layered control system for a mobile robot. IEEE J. Robot. Autom. 1, 14–23 (1986)

    Article  Google Scholar 

  7. Matarić, M.J.: Behavior-based control: examples from navigation, learning, and group behavior. J. Exp. Theor. Artif. Intell. 9, 323–336 (1997)

    Article  Google Scholar 

  8. Arkin, R.: Behavior-Based Robotics. MIT Press, Cambridge, MA, USA (1998)

    Google Scholar 

  9. Jones, J.: Robot Programming: A Practical Guide to Behavior-Based Robotics. McGraw-Hill, New York (2004)

    Google Scholar 

  10. Lenser, S., Bruce, J., Veloso, M.: A Modular Hierarchical Behavior-Based Architecture, pp. 423–428. Springer, Berlin, Heidelberg (2002)

    MATH  Google Scholar 

  11. Mantz, F., Jonker, P.: Behavior-based perception for soccer robots. In: Vision Systems: Applications. I-Tech Education and Publishing, pp. 147–164 (2007)

    Google Scholar 

  12. Proetzsch, M.: Development process for complex behavior-based robot control systems. Ph.D. thesis, Robotics Research Laboratory, University of Kaiserslautern, München, Germany (2010)

    Google Scholar 

  13. Ropertz, T., Wolf, P., Berns, K.: Quality-based behavior-based control for autonomous robots in rough environments. In Gusikhin, O., Madani, K., (eds.) Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2017), vol. 1, Madrid, Spain, SCITEPRESS – Science and Technology Publications, Lda, pp. 513–524 (2017). ISBN: 978-989-758-263-9

    Google Scholar 

  14. Schäfer, B.H.: Robot Control Design Schemata and their Applications in Off-road Robotics. Ph.D. thesis, Robotics Research Lab, University of Kaiserslautern, München, Germany (2011)

    Google Scholar 

  15. Ingibergsson, J.T.M., Kraft, D., Schultz, U.P.: Explicit image quality detection rules for functional safety in computer vision. In: VISIGRAPP (6: VISAPP). pp. 433–444 (2017)

    Google Scholar 

  16. Lee, D.H., Yoon, Y.J., Kang, S.J., Ko, S.J.: Correction of the overexposed region in digital color image. IEEE Trans. Consum. Electron. 60, 173–178 (2014)

    Article  Google Scholar 

  17. Lee, K.H., Ehsani, R.: Comparison of two 2d laser scanners for sensing object distances, shapes, and surface patterns. Comput. Electron. Agric. 60, 250–262 (2008)

    Article  Google Scholar 

  18. Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30, 328–341 (2008)

    Article  Google Scholar 

  19. Kamberova, G., Bajcsy, R.: Sensor errors and the uncertainties in stereo reconstruction. In: Workshop on Empirical Evaluation Methods in Computer Vision, Santa Barbara, California (1998)

    Google Scholar 

  20. Fleischmann, P., Berns, K.: A stereo vision based obstacle detection system for agricultural applications. In: Proceedings of Field and Service Robotics (FSR), Toronto, Canada (2015)

    Google Scholar 

  21. Iagnemma, K., Kang, S., Shibly, H., Dubowsky, S.: Online terrain parameter estimation for wheeled mobile robots with application to planetary rovers. IEEE Trans. Robot. 20, 921–927 (2004)

    Article  Google Scholar 

  22. Langley, R.B.: Dilution of precision. GPS World 10, 52–59 (1999)

    Google Scholar 

  23. Fleischmann, P., Pfister, T., Oswald, M., Berns, K.: Using openstreetmap for autonomous mobile robot navigation. In: Proceedings of the 14th International Conference on Intelligent Autonomous Systems (IAS-14), Shanghai, China (2016) (Best Conference Paper Award - Final List)

    Google Scholar 

  24. Thrun, S., et al.: Winning the darpa grand challenge. J. Field Robot. (2006)

    Google Scholar 

  25. Schäfer, B.H., Proetzsch, M., Berns, K.: Action/perception-oriented robot software design: An application in off-road terrain. In: 10th International Conference on Control, Automation, Robotics and Vision, Hanoi, Vietnam pp. 223–228 (2008)

    Google Scholar 

  26. Hundelshausen, F.v., Himmelsbach, M., Hecker, F., Mueller, A., Wuensche, H.J.: Driving with Tentacles - Integral Structures for Sensing and Motion. Springer Berlin Heidelberg, Berlin, Heidelberg pp. 393–440 (2009)

    Google Scholar 

  27. Reichardt, M., Föhst, T., Berns, K.: Introducing finroc: A convenient real-time framework for robotics based on a systematic design approach. Technical report, Robotics Research Laboratory, Department of Computer Science, University of Kaiserslautern, Kaiserslautern, Germany (2012)

    Google Scholar 

  28. Karis, B., Games, E.: Real shading in unreal engine 4. In: proceedings Physically Based Shading Theory Practice (2013)

    Google Scholar 

  29. Carpin, S., Lewis, M., Wang, J., Balakirsky, S., Scrapper, C.: Usarsim: a robot simulator for research and education. In: 2007 IEEE International Conference on Robotics and Automation, 1400–1405, IEEE (2007)

    Google Scholar 

  30. Shah, S., Dey, D., Lovett, C., Kapoor, A.: Airsim: High-fidelity visual and physical simulation for autonomous vehicles. In: Field and Service Robotics, pp. 621–635, Springer, Berlin (2017)

    Google Scholar 

  31. Mueller, M., Casser, V., Lahoud, J., Smith, N., Ghanem, B.: Ue4sim: a photo-realistic simulator for computer vision applications (2017). arXiv:1708.05869

  32. Carlson, N.A.: Fast triangular formulation of the square root filter. AIAA j. 11, 1259–1265 (1973)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Robotics Research Lab, Department of Computer Science, University of Kaiserslautern, Kaiserslautern, Germany

    Patrick Wolf, Thorsten Ropertz & Karsten Berns

Authors
  1. Patrick Wolf
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thorsten Ropertz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karsten Berns
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patrick Wolf .

Editor information

Editors and Affiliations

  1. Ford Research and Advanced Engineering, Dearborn, MI, USA

    Oleg Gusikhin

  2. University of Paris-EST Créteil (UPEC), Vitry sur Seine, France

    Kurosh Madani

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wolf, P., Ropertz, T., Berns, K. (2020). Behavior-Based Obstacle Detection in Off-Road Environments Considering Data Quality. In: Gusikhin, O., Madani, K. (eds) Informatics in Control, Automation and Robotics . ICINCO 2017. Lecture Notes in Electrical Engineering, vol 495. Springer, Cham. https://doi.org/10.1007/978-3-030-11292-9_39

Download citation

Publish with us

Policies and ethics

Search

Navigation