  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 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 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 but lacks class templates.
