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

Joao Bimbo, Shan Luo, Kaspar Althoefer, Hongbin Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)


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.

Original languageEnglish
Article number7378871
Pages (from-to)570-577
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number1
Publication statusPublished - 12 Jan 2016


  • Contact Modelling
  • Force and Tactile Sensing
  • Grasping


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