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Probabilistic Object Tracking Using a Range Camera

2013

Conference Paper

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We address the problem of tracking the 6-DoF pose of an object while it is being manipulated by a human or a robot. We use a dynamic Bayesian network to perform inference and compute a posterior distribution over the current object pose. Depending on whether a robot or a human manipulates the object, we employ a process model with or without knowledge of control inputs. Observations are obtained from a range camera. As opposed to previous object tracking methods, we explicitly model self-occlusions and occlusions from the environment, e.g, the human or robotic hand. This leads to a strongly non-linear observation model and additional dependencies in the Bayesian network. We employ a Rao-Blackwellised particle filter to compute an estimate of the object pose at every time step. In a set of experiments, we demonstrate the ability of our method to accurately and robustly track the object pose in real-time while it is being manipulated by a human or a robot.

Author(s): Wüthrich, M. and Pastor, P. and Kalakrishnan, M. and Bohg, J. and Schaal, S.
Book Title: IEEE/RSJ International Conference on Intelligent Robots and Systems
Pages: 3195-3202
Year: 2013
Month: November
Publisher: IEEE

Department(s): Autonomous Motion
Research Project(s): Real-Time Perception meets Reactive Motion Generation
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

DOI: 10.1109/IROS.2013.6696810

Links: arXiv
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BibTex

@inproceedings{wuthrich-iros-2013,
  title = {Probabilistic Object Tracking Using a Range Camera},
  author = {W{\"u}thrich, M. and Pastor, P. and Kalakrishnan, M. and Bohg, J. and Schaal, S.},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
  pages = {3195-3202},
  publisher = {IEEE},
  month = nov,
  year = {2013},
  month_numeric = {11}
}