  We discuss the process of building semantic maps, how to interactively label
entities in them, and how to use them to enable context-aware navigation
behaviors in human environments. We utilize planar surfaces, such as walls and
tables, and static objects, such as door signs, as features for our semantic
mapping approach. Users can interactively annotate these features by having the
robot follow him/her, entering the label through a mobile app, and performing a
pointing gesture toward the landmark of interest. Our gesture based approach
can reliably estimate which object is being pointed at and detect ambiguous
gestures with probabilistic modeling. Our person following method attempts to
maximize future utility by a search for future actions assuming constant
velocity model for the human. We describe a method to extract metric goals from
a semantic map landmark and to plan a human aware path that takes into account
the personal spaces of people. Finally, we demonstrate context-awareness for
person following in two scenarios: interactive labeling and door passing. We
believe that future navigation approaches and service robotics applications can
be made more effective by further exploiting the structure of human
environments.
