  In order to perform complex actions in human environments, an autonomous
robot needs the ability to understand the environment, that is, to gather and
maintain spatial knowledge. Topological map is commonly used for representing
large scale, global maps such as floor plans. Although much work has been done
in topological map extraction, we have found little previous work on the
problem of learning the topological map using a probabilistic model. Learning a
topological map means learning the structure of the large-scale space and
dependency between places, for example, how the evidence of a group of places
influence the attributes of other places. This is an important step towards
planning complex actions in the environment. In this thesis, we consider the
problem of using probabilistic deep learning model to learn the topological
map, which is essentially a sparse undirected graph where nodes represent
places annotated with their semantic attributes (e.g. place category). We
propose to use a novel probabilistic deep model, Sum-Product Networks (SPNs),
due to their unique properties. We present two methods for learning topological
maps using SPNs: the place grid method and the template-based method. We
contribute an algorithm that builds SPNs for graphs using template models. Our
experiments evaluate the ability of our models to enable robots to infer
semantic attributes and detect maps with novel semantic attribute arrangements.
Our results demonstrate their understanding of the topological map structure
and spatial relations between places.
