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Daphne Koller Publications

Learning hierarchical object maps of non-stationary environments with mobile robots (2002)

by D. Anguelov, R. Biswas, D. Koller, B. Limketkai, S. Sanner, and S. Thrun


Abstract: Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In this paper, we present an algorithm for learning object models of non-stationary objects found in office-type environments. Our algorithm exploits the fact that many objects found in office environments look alike (e.g., chairs, trash bins). It does so through a two-level hierarchical representation, which links individual objects with generic shape templates of object classes. We derive an approximate EM algorithm for learning shape parameters at both levels of the hierarchy, using local occupancy grid maps for representing shape. Additionally, we develop a Bayesian model selection algorithm that enables the robot to estimate the total number of objects and object templates in the environment. Experimental results using a real robot indicate that our approach performs well at learning object-based maps of simple office environments. The approach outperforms a previously developed non-hierarchical algorithm that models objects but lacks class templates.

Download Information

D. Anguelov, R. Biswas, D. Koller, B. Limketkai, S. Sanner, and S. Thrun (2002). "Learning hierarchical object maps of non-stationary environments with mobile robots." Proceedings of the 17th Annual Conference on Uncertainty in AI (UAI). pdf

Bibtex citation

@inproceedings{Anguelov+al:UAI02,
  author =       "D. Anguelov and R. Biswas and D. Koller and B.
                 Limketkai and S. Sanner and S. Thrun",
  booktitle =    "Proceedings of the 17th Annual Conference on
                 Uncertainty in AI (UAI)",
  title =        "Learning hierarchical object maps of non-stationary
                 environments with mobile robots",
  year =         "2002",
  address = "Edmonton, Canada",
}

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