Advertisement

B-Human 2016 – Robust Approaches for Perception and State Estimation Under More Natural Conditions

  • Thomas Röfer
  • Tim Laue
  • Jesse Richter-Klug
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9776)

Abstract

In 2015 and 2016, the RoboCup Standard Platform League’s major rule changes were mostly concerned with the appearance of important game elements, changing them towards a setup that is more similar to normal football games, for instance a black and white ball and white goals. Furthermore, the 2016 Outdoor Competition was held in a glass hall and thus under natural lighting conditions. These changes rendered many previously established approaches for perception and state estimation useless. In this paper, we present multiple approaches to cope with these challenges, i. e. a color classification for natural lighting conditions, an approach to detect black and white balls, and a self-localization that relies on complex field features that are based on field lines. This combination of perception and state estimation approaches enabled our robots to preserve their high performance in this more challenging new environment and significantly contributed to our success at RoboCup 2016.

References

  1. 1.
    Anderson, P., Hengst, B.: Fast monocular visual compass for a computationally limited robot. In: Behnke, S., Veloso, M., Visser, A., Xiong, R. (eds.) RoboCup 2013. LNCS, vol. 8371, pp. 244–255. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-44468-9_22CrossRefGoogle Scholar
  2. 2.
    Ashar, J., et al.: RoboCup SPL 2014 champion team paper. In: Bianchi, R.A.C., Akin, H.L., Ramamoorthy, S., Sugiura, K. (eds.) RoboCup 2014. LNCS, vol. 8992, pp. 70–81. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-18615-3_6CrossRefGoogle Scholar
  3. 3.
    Coath, G., Musumeci, P.: Adaptive arc fitting for ball detection in RoboCup. In: APRS Workshop on Digital Image Analyzing, pp. 63–68 (2003)Google Scholar
  4. 4.
    Fox, D., Burgard, W., Dellaert, F., Thrun, S.: Monte-Carlo localization: efficient position estimation for mobile robots. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence, pp. 343–349, Orlando, USA (1999)Google Scholar
  5. 5.
    Hanek, R., Schmitt, T., Buck, S., Beetz, M.: Towards RoboCup without color labeling. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds.) RoboCup 2002. LNCS, vol. 2752, pp. 179–194. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-540-45135-8_14CrossRefGoogle Scholar
  6. 6.
    Julier, S.J., Uhlmann, J.K., Durrant-Whyte, H.F.: A new approach for filtering nonlinear systems. In: Proceedings of the American Control Conference, vol. 3, pp. 1628–1632 (1995)Google Scholar
  7. 7.
    Lenser, S., Veloso, M.: Sensor resetting localization for poorly modelled mobile robots. In: Proceedings of the 2000 IEEE International Conference on Robotics and Automation (ICRA 2000), vol. 2, pp. 1225–1232, San Francisco, USA (2000)Google Scholar
  8. 8.
    Martins, D.A., Neves, A.J., Pinho, A.J.: Real-time generic ball recognition in RoboCup domain. In: Proceedings of the 3rd International Workshop on Intelligent Robotics, IROBOT, pp. 37–48 (2008)Google Scholar
  9. 9.
    Müller, J., Frese, U., Röfer, T.: Grab a mug - object detection and grasp motion planning with the NAO robot. In: Proceedings of the IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS 2012), pp. 349–356, Osaka, Japan. IEEE (2012)Google Scholar
  10. 10.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Röfer, T., Laue, T., Richter-Klug, J., Stiensmeier, J., Schünemann, M., Stolpmann, A., Stöwing, A., Thielke, F.: B-Human team description for RoboCup 2015. In: RoboCup 2015: Robot Soccer World Cup XIX Preproceedings. RoboCup Federation, Hefei, China (2015)Google Scholar
  12. 12.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  13. 13.
    Whelan, T., Stüdli, S., McDonald, J., Middleton, R.H.: Efficient localization for robot soccer using pattern matching. In: Hähnle, R., Knoop, J., Margaria, T., Schreiner, D., Steffen, B. (eds.) ISoLA 2011. CCIS, pp. 16–30. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-34781-8_2CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Deutsches Forschungszentrum für Künstliche Intelligenz, Cyber-Physical SystemsBremenGermany
  2. 2.Fachbereich 3 – Mathematik und InformatikUniversität BremenBremenGermany

Personalised recommendations