Probabilistic Articulated Real-Time Tracking for Robot Manipulation

@article{Cifuentes2016ProbabilisticAR,
  title={Probabilistic Articulated Real-Time Tracking for Robot Manipulation},
  author={Cristina Garcia Cifuentes and Jan Issac and Manuel W{\"u}thrich and Stefan Schaal and Jeannette Bohg},
  journal={IEEE Robotics and Automation Letters},
  year={2016},
  volume={2},
  pages={577-584}
}
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… CONTINUE READING

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