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

  • Authors:

  • Dragomir Anguelov;Rahul Biswas;Daphne Koller;Benson Limketkai;Sebastian Thrun

  • Affiliations:

  • Computer Science Department, Stanford University, Stanford, CA;Computer Science Department, Stanford University, Stanford, CA;Computer Science Department, Stanford University, Stanford, CA;Computer Science Department, Stanford University, Stanford, CA;Computer Science Department, Stanford University, Stanford, CA

  • Venue:
  • UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
  • Year:
  • 2002

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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, recycling 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 pararneters 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 equipped with a laser range finder 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 jects but lacks class templates.