Rao-Blackwellized Particle Filtering for Probing-Based 6-DOF Localization in Robotic Assembly

    •  Taguchi, Y.; Marks, T.K.; Okuda, H., "Rao-Blackwellized Particle Filtering for Probing-based 6-DOF Localization in Robotic Assembly", IEEE International Conference on Robotics and Automation (ICRA), ISSN: 105-4729, May 2010, pp. 2610-2617.
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      • @inproceedings{Taguchi2010may,
      • author = {Taguchi, Y. and Marks, T.K. and Okuda, H.},
      • title = {Rao-Blackwellized Particle Filtering for Probing-based 6-DOF Localization in Robotic Assembly},
      • booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
      • year = 2010,
      • pages = {2610--2617},
      • month = may,
      • issn = {105-4729},
      • url = {}
      • }
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  • Research Areas:

    Computer Vision, Robotics

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This paper presents a probing-based method for probabilistic localization in automated robotic assembly. We consider peg-in-hole problems in which a needle-like peg has a single point of contact with the object that contains the hole, and in which the initial uncertainty in the relative pose (3D position and 3D angle) between the peg and the object is much greater than the required accuracy (assembly clearance). We solve this 6 degree-of-freedom (6-DOF) localization problem using a Rao-Blackwellized particle filter, in which the probability distribution over the peg's pose is factorized into two components: The distribution over position (3-DOF) is represented by particles, while the distribution over angle (3-DOF) is approximated as a Gaussian distribution for each particle, updated using an extended Kalman filter. This factorization reduces the number of particles required for localization by orders of magnitude, enabling real-time online 6-DOF pose estimation. Each measurement is simply the contact position obtained by randomly repositioning the peg and moving towards the object until there is contact. To compute the likelihood of each measurement, we use a map a mesh model of the object that is based on the CAD model but also explicitly models the uncertainty in the map. The mesh uncertainty model makes our system robust to cases in which the actual measurement is different from the expected one. We demonstrate the advantages of our approach over previous methods using simulations as well as physical experiments with a robotic arm and a metal peg and object.