Indoor Pose Estimation Using 3D Scene Landmarks for Service Robotics

  • Tiberiu T. Cocias
  • Sorin M. Grigorescu
  • Florin Moldoveanu
Part of the Studies in Computational Intelligence book series (SCI, volume 530)


In this paper, a markerless approach for estimating the pose of a robot using only 3D visual information is presented. As opposite to traditional methods, our approach makes use of 3D features solely for determining a relative position between the imaged scene (e.g. landmarks present on site) and the robot. Such a landmark is calculated from stored 3D map of the environment. The recognition of the landmark is performed via a 3D Object Retrieval (3DOR) search engine. The presented pose estimation technique produces a reliable and accurate pose information which can be further used for complex scene understanding and/or navigation. The performance of the proposed approach has been evaluated against a traditional marker-based position estimation library.


3DOR Shape matching Convexity 3D descriptors Indoor robot navigation Service robotics 


  1. 1.
    Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The Princeton Shape Benchmark. Shape Modeling International June (2004)Google Scholar
  2. 2.
    Grigorescu, S.M., Macesanu, G., Cocias, T.T., Puiu, D., Moldoveanu, F.: Robust camera pose and scene structure analysis for service robotics. Robot. Auton. Syst. 59, 899–909 (2011)CrossRefGoogle Scholar
  3. 3.
    Grigorescu, S.M.: On robust 3D scene perception and camera egomotion estimation. In: Proceedings of the 33rd Colloquium of Automation, Leer, Germany (2011)Google Scholar
  4. 4.
    Grinstead, B., Koschan, A., Gribok, A., Abidi, M.A., Gorsich, D.: Outlier rejection by oriented tracks to aid pose estimation from video. Pattern Recogn. Lett. 27(1), 37–48 (2006)CrossRefGoogle Scholar
  5. 5.
    Grigorescu, S.M., Pangercic, D., Beetz, M.: 2D–3D Collaborative tracking (23CT): towards stable robotic manipulation. In: Proceedings of the 2012 IEEE International Conference on Intelligent Robots and Systems IROS, Workshop on Active Semantic Perception, Vilamoura, Algarve, Portugal (October 7–12, 2012)Google Scholar
  6. 6.
    Malbezin, P., Piekarski, W., Thomas B.: Measuring ARToolKit accuracy in long distance tracking experiments. 1st International Augmented Reality Toolkit Workshop, Darmstadt, Germany (2002)Google Scholar
  7. 7.
    Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of shapes by editing shock graphs. ICCV. 1, 755–762 (2001)Google Scholar
  8. 8.
    Hartley, R.I.: An object-oriented approach to scene reconstruction. IEEE International Conference on Systems, Man, and Cybernetics, New York, 4, 2475–2480 (1996)Google Scholar
  9. 9.
    Zhang, H., Fiume, E.: Shape matching of 3-D contours using normalized fourier descriptors. Proceedings of the Shape Modeling, international (SMI02), 261–268 (2002)Google Scholar
  10. 10.
    Tangelder, J.W., Veltkamp, R.C.: A survey of content based 3D shape retrieval methods. Multimedia Tools Appl. 39(3), 441–471 (September 2008)Google Scholar
  11. 11.
    Tombari, F., Salti S., Di Stefano, L.: Unique signatures of histograms for local surface description. 11th European Conference on Computer Vision (ECCV), Hersonissos, Greece, 356–369 (September 5–11, 2010)Google Scholar
  12. 12.
    Heider, P., Pierre-Pierre, A., Li, R., Grimm, C.: Local shape descriptors, a survey and evaluation. Eurographics Workshop on 3D Object Retrieval, 49–57 (2011)Google Scholar
  13. 13.
    Cech, J., Matas, J., Perdoch, M.: Efficient sequential correspondence selection by cosegmentation. IEEE Trans. PAMI 32(9), 1568–1581 (2010)CrossRefGoogle Scholar
  14. 14.
    Min, P., Halderman, J.A., Kazhdan, M., Funkhouser, T.A.: Early experiences with a 3D model search engineering. Web3D, Symposium, 7–18 (2003)Google Scholar
  15. 15.
    Johnson, A.E.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. PAMI 21(5), 433–449 (1999)CrossRefGoogle Scholar
  16. 16.
    Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: Proceedings of the IEEE international conference on Robotics and Automation (ICRA’09) IEEE Press. Piscataway, NJ, USA, 1848–1853 (2009)Google Scholar
  17. 17.
    Sajjanhar, A., Lu, G., Zhang, D., Hou, J., Zhou, W., Chen, P.: Spectral shape descriptor using spherical harmonics. Integr. Comput.-Aided Eng. 17(2) 167–173 (April 2010).Google Scholar
  18. 18.
    Bhattacharyya, A.: On a measure of divergence between two statistical populations defined by their probability distributions. Bull. Calcutta Math. Soc. 35, 99–109 (1943)Google Scholar
  19. 19.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2004)CrossRefMATHGoogle Scholar
  20. 20.
    Steder, B., Rusu, R.B., Konolige, K., Burgard, W.: NARF: 3D range image features for object recognition. International Conference on Intelligent Robots and Systems (2010).Google Scholar
  21. 21.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tiberiu T. Cocias
    • 1
  • Sorin M. Grigorescu
    • 1
  • Florin Moldoveanu
    • 1
  1. 1.Departament of AutomationTransilvania University of BrasovBrasovRomania

Personalised recommendations