Skip to main content

Scene Analysis for Service Robots

  • Chapter
  • First Online:
Book cover Towards Service Robots for Everyday Environments

Abstract

A scene analysis module for service robots is presented which uses SIFT in a stereo setting, a systematic handling of uncertainties and an active perception component. The system is integrated and evaluated on the DESIRE two-arm mobile robot. Complex everyday scenes composed of various items from a 100-object database are analyzed successfully and efficiently.

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

Access this chapter

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 PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Grundmann, T., Fiegert, M., Burgard, W.: Probabilistic Rule Set Joint State Update as Approximation to the Full Joint State Estimation Applied to Multi Object Scene Analysis. In: Proceedings 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (2010)

    Google Scholar 

  2. Stuelpnagel, J.: On the Parametrization of the Three-Dimensional Rotation Group. SIAM Review 6(4), 422–430 (1964)

    Article  MathSciNet  MATH  Google Scholar 

  3. Choe, S.: Statistical Analysis of Orientation Trajectories via Quaternions with Applications to Human Motion. Ph.D. thesis. University of Michigan (2006)

    Google Scholar 

  4. Goddard, J.S., Abidi, M.A.: Pose and motion estimation using dual quaternion-based extended kalman filtering. In: Proceedings of SPIE Conference on Three-Dimensional Image Capture and Applications, vol. 3313, pp. 189–200 (1998)

    Google Scholar 

  5. Kavan, L., Collins, S., O’Sullivan, C., Zara, J.: Dual Quaternions for Rigid Transformation Blending., Technical Report (2006)

    Google Scholar 

  6. Antone, M.: Robust Camera Pose Recovery Using Stochastic Geometry. PhD thesis, MIT (2001)

    Google Scholar 

  7. Love, J.: Bingham Statistics. In: Encyclopedia of Geomagnetism and Paleomagnetism, pp. 45–47. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  8. Mardia, K., Taylor, C., Subramaniam, G.: Protein bioinformatics and mixtures of bivariate von mises distributions for angular data. Biometrics 63(2), 505–512 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  9. Kraft, E.: A Quaternion-based Unscented Kalman Filter for Orientation Tracking. In: Proceedings of the Sixth International Conference of Information Fusion, vol. 1, pp. 47–54 (2003)

    Google Scholar 

  10. Feiten, W., Atwal, P., Eidenberger, R., Grundmann, T.: Pose Uncertainty in Robotic Perception. In: Advances in Robotics Research: Theory, Implementation, Application, pp. 89–98. Springer, Heidelberg (2009)

    Google Scholar 

  11. Spong, M.W.: Robot Dynamics and Control. John Wiley & Sons, Inc., New York (1989)

    Google Scholar 

  12. Nayar, S., Nene, S., Murase, H.: Real-time 100 object recognition system. In: Proc. IEEE International Conference on Robotics and Automation, April 22–28, vol. 3, pp. 2321–2325 (1996)

    Google Scholar 

  13. Zhang, J., Schmidt, R., Knoll, A.: Appearance-based visual learning in a neuro-fuzzy model for fine-positioning of manipulators. In: ICRA, pp. 1164–1169 (1999)

    Google Scholar 

  14. Walter, J.A., Arnrich, B.: Gabor filters for object localization and robot grasping. In: ICPR, pp. 4124–4127 (2000)

    Google Scholar 

  15. Kragic, D., Miller, A.T., Allen, P.K.: Real-time tracking meets online grasp planning. In: Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), Seoul, Republic of Korea, pp. 2460–2465 (2001)

    Google Scholar 

  16. Lowe, D.G.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision, Corfu, Greece, pp. 1150–1157 (September 1999)

    Google Scholar 

  17. Azad, P., Asfour, T., Dillmann, R.: Stereo-based 6d object localization for grasping with humanoid robot systems. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), San Diego, CA, USA (2007)

    Google Scholar 

  18. Collet, A., Berenson, D., Srinivasa, S., Ferguson, D.: Object recognition and full pose registration from a single image for robotic manipulation. In: ICRA 2009 (2009)

    Google Scholar 

  19. Pan, Q., Reitmayr, G., Drummond, T.: ProFORMA: Probabilistic Feature-based On-line Rapid Model Acquisition. In: Proc. 20th British Machine Vision Conference (BMVC), London (September 2009)

    Google Scholar 

  20. Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: CVPR 2006: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, pp. 519–528 (2006)

    Google Scholar 

  21. Dementhon, D.F., Davis, L.S.: Model-based object pose in 25 lines of code. International Journal of Computer Vision 15(1-2), 123–141 (1995)

    Article  Google Scholar 

  22. Azad, P., Asfour, T., Dillmann, R.: Stereo-based vs. monocular 6-dof pose estimation using point features: A quantitative comparison. In: Autonome Mobile Systeme 2009. Informatik aktuell. Springer, Karlsruhe (2009)

    Google Scholar 

  23. Xue, Z., Kasper, A., Zoellner, J., Dillmann, R.: An automatic grasp planning system for service robots. In: 14th International Conference on Advanced Robotics (ICAR), June 22-26 (2009)

    Google Scholar 

  24. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004) ISBN: 0521540518

    Google Scholar 

  25. Mount, D., Arya, S.: Ann: A library for approximate nearest neighbor searching. In: Center for Geometric Computing 2nd Annum Fall Workshop on Computational Geometry (1997)

    Google Scholar 

  26. Heyer, L.J., Kruglyak, S., Yooseph, S.: Exploring expression data: Identification and analysis of coexpressed genes. Genome Res. 9(11), 1106–1115 (1999)

    Article  Google Scholar 

  27. Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer Journal 7, 308–313 (1965)

    MATH  Google Scholar 

  28. Grundmann, T., Eidenberger, R., Schneider, M., Fiegert, M.: Robust 6d pose determination in complex environments for one hundred classes. In: Proceedings of the 7th International Conference On Informatics in Control, Automation and Robotics (2010)

    Google Scholar 

  29. Eidenberger, R., Grundmann, T., Zoellner, R.: Probabilistic action planning for active scene modeling in continuous high-dimensional domains. In: Proceedings of the IEEE International Conference On Robotics and Automation (2009)

    Google Scholar 

  30. Grundmann, T., Zoellner, R.: Probabilistic scene estimation and local dependency analysis with particle filters. In: 4th European Conference on Mobile Robots, ECMR (2009)

    Google Scholar 

  31. Denzler, J., Brown, C.M.: Information theoretic sensor data selection for active object recognition and state estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 145–157 (2002)

    Article  Google Scholar 

  32. Vogel, J., de Freitas, N.: Target-directed attention: sequential decision-making for gaze planning. In: International Conference on Robotics and Automation (2008)

    Google Scholar 

  33. Chli, M., Davison, A.J.: Active Matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 72–85. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  34. Deguchi, K.: An information theoretic approach for active and effective object recognitions. SICE Journal of Control, Measurement, and System Integration 1, 30–39 (2008)

    Google Scholar 

  35. Eidenberger, R., Zoellner, R., Scharinger, J.: Probabilistic occlusion estimation in cluttered environments for active perception planning. In: Proceedings of the IEEE International Conference on Advanced Intelligent Mechatronics (2009)

    Google Scholar 

  36. Chen, S.: On Perception Planning for Active Robot Vision. In: IEEE SMC Society eNewsletter, vol. 13 (December 2005)

    Google Scholar 

  37. Dutta Roy, S., Chaudhury, S., Banerjee, S.: Active recognition through next view planning: a survey. Pattern Recognition 37(3), 429–446 (2004)

    Article  Google Scholar 

  38. Ma, J., Burdick, J.W.: Dynamic sensor planning with stereo for model identification on a mobile platform. In: Proceedings of IEEE International Conference Robotics, Automation and Control (2010)

    Google Scholar 

  39. Flandin, G., Chaumette, F.: Visual Data Fusion for Objects Localization by Active Vision. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 312–326. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  40. Feixas, M., Sbert, M., Gonzalez, F.: A unified information-theoretic framework for viewpoint selection and mesh saliency. ACM Trans. Appl. Percept. 6(1), 1–23 (2009)

    Article  Google Scholar 

  41. Eidenberger, R., Grundmann, T., Zoellner, R.: Probabilistic action planning for active scene modeling in continuous high-dimensional domains. In: IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan (2009)

    Google Scholar 

  42. Huber, M.F., Bailey, T., Durrant-Whyte, H., Hanebeck, U.D.: On entropy approximation for gaussian mixture random vectors. In: Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (2008)

    Google Scholar 

  43. Payeur, P., Laurendeau, D., Gosselin, C.: Range data merging for probabilistic octree modeling of 3-d workspaces. In: Proceedings of the IEEE International Conference On Robotics and Automation, pp. 3071–3078 (1998)

    Google Scholar 

  44. Marchand, É., Chaumette, F.: Active vision for complete scene reconstruction and exploration. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(1) (1999)

    Google Scholar 

  45. Visser, A., Slamet, B.A.: Balancing the Information Gain Against the Movement Cost for Multi-robot Frontier Exploration. In: European Robotics Symposium 2008, vol. 44, pp. 43–52. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag GmbH Berlin Heidelberg

About this chapter

Cite this chapter

Eidenberger, R. et al. (2012). Scene Analysis for Service Robots. In: Prassler, E., et al. Towards Service Robots for Everyday Environments. Springer Tracts in Advanced Robotics, vol 76. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25116-0_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-25116-0_15

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25115-3

  • Online ISBN: 978-3-642-25116-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics