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 al gorithm for learning object models of non-stationary objects found in office-type environments. Our al gorithm 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 repre sentation, which links individual objects with generic shape templates of object classes. We derive an ap proximate EM algorithm for learning shape parame ters at both levels of the hierarchy, using local occu pancy grid maps for representing shape. Additionally, we develop a Bayesian model selection algorithm that enables the robot to estimate the total number of ob jects and object templates in the environment. Ex perimental 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 en vironments. The approach outperforms a previously developed non-hierarchical algorithm that models ob jects but lacks class templates.
For example, Anguelov et al. REF present an approach which aims to learn models of non-stationary objects from proximity data.
Learning Hierarchical Object Maps Of Non-Stationary Environments with mobile robots
UAI 2002
arXiv: Learning
Computer Science Mathematics