  Service robots are expected to reliably make sense of complex, fast-changing
environments. From a cognitive standpoint, they need the appropriate reasoning
capabilities and background knowledge required to exhibit human-like Visual
Intelligence. In particular, our prior work has shown that the ability to
reason about spatial relations between objects in the world is a key
requirement for the development of Visually Intelligent Agents. In this paper,
we present a framework for commonsense spatial reasoning which is tailored to
real-world robotic applications. Differently from prior approaches to
qualitative spatial reasoning, the proposed framework is robust to variations
in the robot's viewpoint and object orientation. The spatial relations in the
proposed framework are also mapped to the types of commonsense predicates used
to describe typical object configurations in English. In addition, we also show
how this formally-defined framework can be implemented in a concrete spatial
database.
