  Being able to explore an environment and understand the location and type of
all objects therein is important for indoor robotic platforms that must
interact closely with humans. However, it is difficult to evaluate progress in
this area due to a lack of standardized testing which is limited due to the
need for active robot agency and perfect object ground-truth. To help provide a
standard for testing scene understanding systems, we present a new robot vision
scene understanding challenge using simulation to enable repeatable experiments
with active robot agency. We provide two challenging task types, three
difficulty levels, five simulated environments and a new evaluation measure for
evaluating 3D cuboid object maps. Our aim is to drive state-of-the-art research
in scene understanding through enabling evaluation and comparison of active
robotic vision systems.
