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.
This work has received funding from the Spanish Ministry of Economy, Industry and Competitiveness under the projects DPI2013-47944-C4-3-R and DPI2016-80077-R, and the RoboCity2030-III-CM project, stage III, S2013/MIT-2748), cofunded by “Programas de Actividades I+D en la Comunidad de Madrid”, and by Structural Funds of the EU.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Rothwell, J., Traub, M., Day, B., Obeso, J., Thomas, P., Marsden, C.: Manual motor performance in a deafferented man. Brain 105, 515–542 (1982)
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)
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)
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)
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)
Aggarwal, A., Kirchner, F.: Object recognition and localization: the role of tactile sensors. Sensors 14, 3227–3266 (2014)
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)
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)
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)
Kalman, R.: A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 82, 35–45 (1960)
Doucet, A., de Freitas, N., Gordon, N.: An Introduction to Sequential Monte Carlo Methods. Springer, New York (2001)
Candy, J.: Bootstrap particle filtering. IEEE Signal Process. Mag. 24, 73–85 (2007)
Ristic, B., Arulampalam, S., Gordon, N.: Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, Norwood (2004)
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)
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)
Olson, E.: AprilTag: a robust and flexible visual fiducial system. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 3400–3407 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Álvarez, D., Roa, M.A., Moreno, L. (2018). Tactile-Based In-Hand Object Pose Estimation. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds) ROBOT 2017: Third Iberian Robotics Conference. ROBOT 2017. Advances in Intelligent Systems and Computing, vol 694. Springer, Cham. https://doi.org/10.1007/978-3-319-70836-2_59
Download citation
DOI: https://doi.org/10.1007/978-3-319-70836-2_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70835-5
Online ISBN: 978-3-319-70836-2
eBook Packages: EngineeringEngineering (R0)