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Active Scene Recognition

  • Pascal MeißnerEmail author
Chapter
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 135)

Abstract

Detailed technical presentation of our contributions that are related to Active Scene Recognition. This includes our approaches to Object Pose Prediction and Next-Best-View estimation.

References

  1. 1.
    Aumann-Cleres, F.: Markerbasiertes Kalibrieren der kinematischen Kette und Aufstellen der Rückwärtstransformation zwischen der Basis und dem Sensorkopf eines mobilen Roboters. Bachelor’s thesis, Advisor: P. Meißner, Reviewer: R. Dillmann, Karlsruhe Institute of Technology (2016)Google Scholar
  2. 2.
    Bohren, J., Cousins, S.: The smach high-level executive. IEEE Robot. Autom. Mag. (2013)Google Scholar
  3. 3.
    Bourke, P.: Frustum culling. http://paulbourke.net/miscellaneous/frustum (2000). Accessed 01 Dec 2017
  4. 4.
    Bronshtein, I., Semendyayev, K., Musiol, G., Muehlig, H.: Handbook of Mathematics, 5th edn. Springer, Berlin (2007)Google Scholar
  5. 5.
    Devert, A.: Spreading points on a disc and on a sphere—Marmakoide’s Blog. http://blog.marmakoide.org/?p=1 (2012). Accessed 14 Nov 2017
  6. 6.
    Dillmann, R., Huck, M.: Informationsverarbeitung in der Robotik. Springer, Berlin (1991)Google Scholar
  7. 7.
    Eidenberger, R., Grundmann, T., Schneider, M., Feiten, W., Fiegert, M., Wichert, G.V., Lawitzky, G.: Scene analysis for service robots. In: Towards Service Robots for Everyday Environments, pp. 181–213. Springer, Berlin (2012)Google Scholar
  8. 8.
    Gamma, E., Johnson, R., Vlissides, J., Helm, R.: Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley, Boston (1995)Google Scholar
  9. 9.
    Garvey, T.D.: Perceptual strategies for purposive vision. Tech-Technical Note 117, SRI International (1976)Google Scholar
  10. 10.
    Hein, J.L.: Discrete Mathematics, 2nd edn. Jones and Bartlett Publishers, Inc, Burlington (2002)Google Scholar
  11. 11.
    Isard, M., Blake, A.: Condensation–conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)CrossRefGoogle Scholar
  12. 12.
    Karrenbauer, O.: Realisierung und komparative Analyse von alternativen Methoden zum uninformierten Generieren optimaler Folgen von Ansichten für die 3D-Objektsuche. Bachelor’s thesis, Advisor: P. Meißner, Reviewer: R. Dillmann, Karlsruhe Institute of Technology (2017)Google Scholar
  13. 13.
    Kunze, L., Doreswamy, K.K., Hawes, N.: Using qualitative spatial relations for indirect object search. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 163–168. IEEE (2014)Google Scholar
  14. 14.
    Lehmann, A., Leibe, B., Van Gool, L.: Fast prism: branch and bound hough transform for object class detection. Int. J. Comput. Vis. 94(2), 175–197 (2011)CrossRefGoogle Scholar
  15. 15.
    Lorbach, M., Hofer, S., Brock, O.: Prior-assisted propagation of spatial information for object search. In: Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 2904–2909. IEEE (2014)Google Scholar
  16. 16.
    Meißner, P., Reckling, R., Wittenbeck, V., Schmidt-Rohr, S., Dillmann, R.: Active scene recognition for programming by demonstration using next-best-view estimates from hierarchical implicit shape models. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5585–5591. IEEE (2014)Google Scholar
  17. 17.
    Meißner, P., Schleicher, R., Hutmacher, R., Schmidt-Rohr, S., Dillmann, R.: Scene recognition for mobile robots by relational object search using next-best-view estimates from hierarchical implicit shape models. In: Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 137–144. IEEE (2016)Google Scholar
  18. 18.
    Patel, A.: Hexagonal grids. https://www.redblobgames.com/grids/hexagons (2013 & 2015). Accessed 11 Nov 2017
  19. 19.
    Potthast, C., Sukhatme, G.S.: A probabilistic framework for next best view estimation in a cluttered environment. J. Vis. Commun. Image Represent. 25(1), 148–164 (2014)CrossRefGoogle Scholar
  20. 20.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: Ros: an open-source robot operating system. In: ICRA Workshop on Open Source Software, Kobe, p. 5 (2009)Google Scholar
  21. 21.
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd international edn. Prentice Hall Press, Upper Saddle River (2010)Google Scholar
  22. 22.
    Siciliano, B., Khatib, O.: Springer Handbook of Robotics. Springer Science + Business Media, Berlin (2008)Google Scholar
  23. 23.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press, Cambridge (2005)Google Scholar
  24. 24.
    Vasquez-Gomez, J.I., Sucar, L.E., Murrieta-Cid, R.: View planning for 3d object reconstruction with a mobile manipulator robot. In: Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 4227–4233. IEEE (2014)Google Scholar
  25. 25.
    Wixson, L.E., Ballard, D.H.: Using intermediate objects to improve the efficiency of visual search. Int. J. Comput. Vis. 12(2–3), 209–230 (1994)CrossRefGoogle Scholar
  26. 26.
    Ye, Y., Tsotsos, J.K.: Sensor planning for 3d object search. Comput. Vis. Image Underst. 73(2), 145–168 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.IAR-IPRKarlsruhe Institute of TechnologyKarlsruheGermany

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