In-Hand Object Pose Estimation Using Covariance-Based Tactile To Geometry Matching

This letter presents a strategy to represent data from a tactile array sensor and match it to an object's geometric features. Using that representation, a method is presented to localise a grasped object within a robot hand. The method consists of computing the covariance matrix in the tactile sensors' pressure data and computing the eigenbasis from its principal axes. A global search is carried out to find a pose in which the object's local geometry in the vicinity of the contact is coherent with that basis, i.e., is aligned with the principal axes and has similar variances. This approach, which can be used as a measurement model for tactile sensors, is compared and outperforms methods using the distance between the tactile sensor elements and object surface.

[1]  Kaspar Althoefer,et al.  A computationally fast algorithm for local contact shape and pose classification using a tactile array sensor , 2012, 2012 IEEE International Conference on Robotics and Automation.

[2]  Tsutomu Hasegawa,et al.  Real-time pose estimation of an object manipulated by multi-fingered hand using 3D stereo vision and tactile sensing , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[3]  Florian Schmidt,et al.  Sequential trajectory re-planning with tactile information gain for dexterous grasping under object-pose uncertainty , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Gunther Heidemann,et al.  Dynamic tactile sensing for object identification , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[5]  Ed Anderson,et al.  LAPACK Users' Guide , 1995 .

[6]  Ravinder Dahiya,et al.  Robotic Tactile Sensing: Technologies and System , 2012 .

[7]  Tamim Asfour,et al.  Multi-sensor and prediction fusion for contact detection and localization , 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.

[8]  C. D. Perttunen,et al.  Lipschitzian optimization without the Lipschitz constant , 1993 .

[9]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[10]  Gregory D. Hager,et al.  Tactile-Object Recognition From Appearance Information , 2011, IEEE Transactions on Robotics.

[11]  Jan Wikander,et al.  Tactile sensing in intelligent robotic manipulation - a review , 2005, Ind. Robot.

[12]  Angel P. del Pobil,et al.  Vision-tactile-force integration and robot physical interaction , 2009, 2009 IEEE International Conference on Robotics and Automation.

[13]  Achint Aggarwal,et al.  Object Recognition and Localization: The Role of Tactile Sensors , 2014, Sensors.

[14]  Oliver Kroemer,et al.  Learning robot tactile sensing for object manipulation , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  D. Fox,et al.  Unsupervised Learning of Spatio-Temporal Features for Tactile Data , 2014 .

[16]  Kaspar Althoefer,et al.  Global estimation of an object’s pose using tactile sensing , 2015, Adv. Robotics.

[17]  Oussama Khatib,et al.  Global Localization of Objects via Touch , 2011, IEEE Transactions on Robotics.

[18]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  P. Allen,et al.  Dexterous Grasping via Eigengrasps : A Low-dimensional Approach to a High-complexity Problem , 2007 .

[20]  Siddhartha S. Srinivasa,et al.  Pose estimation for contact manipulation with manifold particle filters , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

[22]  Andrew T. Miller,et al.  Integration of Vision , Force and Tactile Sensing for Grasping , 1999 .

[23]  Heinz Wörn,et al.  Tactile sensing for an anthropomorphic robotic hand: Hardware and signal processing , 2009, 2009 IEEE International Conference on Robotics and Automation.

[24]  Kaspar Althoefer,et al.  Combining touch and vision for the estimation of an object's pose during manipulation , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[25]  Gregory D. Hager,et al.  Object mapping, recognition, and localization from tactile geometry , 2011, 2011 IEEE International Conference on Robotics and Automation.

[26]  Enrique Valero,et al.  In-hand object localization: Simple vs. complex tactile sensors , 2014, IEEE SENSORS 2014 Proceedings.

[27]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1977, TOMS.

[28]  Jens Reinecke,et al.  Online in-hand object localization , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[29]  J C Rothwell,et al.  Manual motor performance in a deafferented man. , 1982, Brain : a journal of neurology.

[30]  Jianwei Zhang,et al.  Precision grasp synergies for dexterous robotic hands , 2013, 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[31]  Kaspar Althoefer,et al.  Object pose estimation and tracking by fusing visual and tactile information , 2012, 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[32]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[33]  J. F. Soechting,et al.  Postural Hand Synergies for Tool Use , 1998, The Journal of Neuroscience.

[34]  Kaspar Althoefer,et al.  Tactile image based contact shape recognition using neural network , 2012, 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[35]  Fulvio Mastrogiovanni,et al.  A framework for representing interaction tasks based on tactile data , 2010, 19th International Symposium in Robot and Human Interactive Communication.