TR2011-067

Entropy-Based Motion Selection for Touch-Based Registration Using Rao-Blackwellized Particle Filtering


    •  Taguchi, Y., Marks, T.K., Hershey, J.R., "Entropy-Based Motion Selection for Touch-Based Registration Using Rao-Blackwellized Particle Filtering", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), DOI: 10.1109/​IROS.2011.6094767, September 2011, pp. 4690=4697.
      BibTeX TR2011-067 PDF
      • @inproceedings{Taguchi2011sep,
      • author = {Taguchi, Y. and Marks, T.K. and Hershey, J.R.},
      • title = {Entropy-Based Motion Selection for Touch-Based Registration Using Rao-Blackwellized Particle Filtering},
      • booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      • year = 2011,
      • pages = {4690=4697},
      • month = sep,
      • doi = {10.1109/IROS.2011.6094767},
      • url = {https://www.merl.com/publications/TR2011-067}
      • }
  • MERL Contact:
  • Research Areas:

    Computer Vision, Robotics

Abstract:

Registering an object with respect to a robot's coordinate system is essential to industrial assembly tasks such as grasping and insertion. Touch-based registration algorithms use a probe attached to a robot to measure the positions of contact, then use these measurements to register the robot to a model of the object. In existing work on touch-based registration, the selection of contact positions is not typically addressed. We present an algorithm for selecting the next robot motion to maximize the expected information obtained by the resulting contact with the object. Our method performs 6-DOF registration in a Rao-Blackwellized particle filtering (RBPF) framework. Using the 3D model of the object and the current RBPF distribution, we compute the expected information gain from a proposed robot motion by estimating the expected entropy that the RBPF distribution would have as a result of being updated by the proposed motion. The motion that provides the maximum information gain is selected and used for the next measurement, and the process is repeated. We compare various methods for estimating entropy, including approximations based on kernel density estimation. We demonstrate entropy-based motion selection in fully automatic and human-guided registration, both in simulations and on a real robotic platform.

 

  • Related News & Events

    •  NEWS    IROS 2011: publication by Yuichi Taguchi, John R. Hershey and Tim K. Marks
      Date: September 25, 2011
      Where: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
      MERL Contact: Tim K. Marks
      Brief
      • The paper "Entropy-Based Motion Selection for Touch-Based Registration Using Rao-Blackwellized Particle Filtering" by Taguchi, Y., Marks, T.K. and Hershey, J.R. was presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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