Rao-Blackwellized particle filtering for probing-based 6-DOF localization in robotic assembly

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 ...

• Type: Journal
• Year: 2010
• Where: ICRA
• Authors: Yuichi Taguchi, Tim K. Marks, Haruhisa Okuda
• Keywords: Extended Kalman filter, ICRA 2010, Mesh Uncertainty Model, Rao-Blackwellized Particle filter, Robotics