Probabilistic Articulated Real-Time Tracking for Robot Manipulation

Cristina Garcia Cifuentes1, Jan Issac1, Manuel Wüthrich2, Stefan Schaal3, Jeannette Bohg4

  • 1Max Planck Institute for Intelligent Systems
  • 2Max-Planck-Institute for Intelligent Systems
  • 3MPI Intelligent Systems & University of Southern California
  • 4Max-Planck Institute for Intelligent Systems

Details

Category

RA-L Papers

Sessions

09:55 - 11:10 | Tue 30 May | Room 4311/4312 | TUA3

Computer Vision 1

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Abstract

We propose a probabilistic filtering method which fuses joint measurements with depth images to yield a precise, real-time estimate of the end-effector pose in the camera frame. This avoids the need for frame transformations when using it in combination with visual object tracking methods. Precision is achieved by modeling and correcting biases in the joint measurements as well as inaccuracies in the robot model, such as poor extrinsic camera calibration. We make our method computationally efficient through a principled combination of Kalman filtering of the joint measurements and asynchronous depth-image updates based on the Coordinate Particle Filter. We quantitatively evaluate our approach on a dataset recorded from a real robotic platform, annotated with ground truth from a motion capture system. We show that our approach is robust and accurate even under challenging conditions such as fast motion, significant and long-term occlusions, and time-varying biases. We release the dataset along with open-source code of our approach to allow for quantitative comparison with alternative approaches.

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