Advertisement

Tactile-Based In-Hand Object Pose Estimation

  • David ÁlvarezEmail author
  • Máximo A. Roa
  • Luis Moreno
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 694)

Abstract

This paper presents a method to estimate the pose of an object inside a robotic hand by exploiting contact and joint position information. Once an initial visual estimation is provided, a Bootstrap Particle Filter is used to evaluate multiple hypothesis for the object pose. The function used to score the hypothesis considers feasibility and physical meaning of the contacts between the object and the hand. The method provides a good estimation of in-hand pose for different 3D objects.

Keywords

Robotic grasping Object pose estimation Tactile sensing 

References

  1. 1.
    Macura, Z., Cangelosi, A., Ellis, R., Bugmann, D., Fischer, M., Myachykov, A.: A cognitive robotic model of grasping. In: Proceedings of International Conference on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems, pp. 89–96 (2009)Google Scholar
  2. 2.
    Rothwell, J., Traub, M., Day, B., Obeso, J., Thomas, P., Marsden, C.: Manual motor performance in a deafferented man. Brain 105, 515–542 (1982)CrossRefGoogle Scholar
  3. 3.
    Bimbo, J., Seneviratne, L., Althoefer, K., Liu, H.: Combining touch and vision for the estimation of an object’s pose during manipulation. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4021–4026 (2013)Google Scholar
  4. 4.
    Haidacher, S., Hirzinger, G.: Estimating finger contact location and object pose from contact measurements in 3-D grasping. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1805–1810 (2003)Google Scholar
  5. 5.
    Chalon, M., Reinecke, J., Pfanne, M.: Online in-hand object localization. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2977–2984 (2013)Google Scholar
  6. 6.
    Koval, M., Dogar, M., Pollard, N., Srinivasa, S.: Pose estimation for contact manipulation with manifold particle filters. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4541–4548 (2013)Google Scholar
  7. 7.
    Aggarwal, A., Kirchner, F.: Object recognition and localization: the role of tactile sensors. Sensors 14, 3227–3266 (2014)CrossRefGoogle Scholar
  8. 8.
    Hebert, P., Hudson, N., Ma, J., Burdick, J.: Fusion of stereo vision, force-torque, and joint sensors for estimation of in-hand object location. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 5935–5941 (2011)Google Scholar
  9. 9.
    Bimbo, J., Kormushev, P., Althoefer, K., Liu, H.: Global estimation of an object’s pose using tactile sensing. Adv. Rob. Syst. 29, 37–41 (2015)Google Scholar
  10. 10.
    Tenzer, Y., Jentoft, L., Howe, R.: The feel of MEMS barometers: inexpensive and easily customized tactile array sensors. IEEE Rob. Autom. Magaz. 21(3), 89–95 (2014)CrossRefGoogle Scholar
  11. 11.
    Kalman, R.: A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 82, 35–45 (1960)CrossRefGoogle Scholar
  12. 12.
    Doucet, A., de Freitas, N., Gordon, N.: An Introduction to Sequential Monte Carlo Methods. Springer, New York (2001)CrossRefzbMATHGoogle Scholar
  13. 13.
    Candy, J.: Bootstrap particle filtering. IEEE Signal Process. Mag. 24, 73–85 (2007)CrossRefGoogle Scholar
  14. 14.
    Ristic, B., Arulampalam, S., Gordon, N.: Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, Norwood (2004)zbMATHGoogle Scholar
  15. 15.
    Calli, B., Singh, A., Walsman, A., Srinivasa, S., Abbeel, P., Dollar, A.: The YCB object and model set: towards common benchmarks for manipulation research. In: Proceedings of IEEE International Conference on Advanced Robotics, pp. 510–517 (2015)Google Scholar
  16. 16.
    Pan, J., Chitta, S., Manocha, D.: FCL: a general purpose library for collision proximity queries. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 3859–3866 (2012)Google Scholar
  17. 17.
    Olson, E.: AprilTag: a robust and flexible visual fiducial system. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 3400–3407 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Robotics LabCarlos III University of MadridLeganesSpain
  2. 2.German Aerospace Center - DLRWesslingGermany

Personalised recommendations