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Daphne Koller Publications

Detecting and modeling doors with mobile robots (2004)

by D. Anguelov, D. Koller, E. Parker, and S. Thrun


Abstract:

We describe a probabilistic framework for detection and modeling of doors from sensor data acquired in corridor environments with mobile robots. The framework captures shape, color, and motion properties of door and wall objects. The probabilistic model is optimized with a version of the expectation maximization algorithm, which segments the environment into door and wall objects and learns their properties. The framework allows the robot to generalize the properties of detected object instances to new object instances. We demonstrate the algorithm on real-world data acquired by a Pioneer robot equipped with a laser range nder and an omni-directional camera. Our results show that our algorithm reliably segments the environment into walls and doors, nding both doors that move and doors that do not move. We show that our approach achieves better results than models that only capture behavior, or only capture appearance.


Download Information

D. Anguelov, D. Koller, E. Parker, and S. Thrun (2004). "Detecting and modeling doors with mobile robots." Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). pdf

Bibtex citation

@inproceedings{Anguelov+al:ICRA04,
  author = {D. Anguelov and D. Koller and E. Parker and S. Thrun},
  title = {Detecting and modeling doors with mobile robots},
  booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
  year = 2004,
}

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