  We introduce TopoNets, end-to-end probabilistic deep networks for modeling
semantic maps with structure reflecting the topology of large-scale
environments. TopoNets build a unified deep network spanning multiple levels of
abstraction and spatial scales, from pixels representing geometry of local
places to high-level descriptions of semantics of buildings. To this end,
TopoNets leverage complex spatial relations expressed in terms of arbitrary,
dynamic graphs. We demonstrate how TopoNets can be used to perform end-to-end
semantic mapping from partial sensory observations and noisy topological
relations discovered by a robot exploring large-scale office spaces. Thanks to
their probabilistic nature and generative properties, TopoNets extend the
problem of semantic mapping beyond classification. We show that TopoNets
successfully perform uncertain reasoning about yet unexplored space and detect
novel and incongruent environment configurations unknown to the robot. Our
implementation of TopoNets achieves real-time, tractable and exact inference,
which makes these new deep models a promising, practical solution to mobile
robot spatial understanding at scale.
