  We showcase a topological mapping framework for a challenging indoor
warehouse setting. At the most abstract level, the warehouse is represented as
a Topological Graph where the nodes of the graph represent a particular
warehouse topological construct (e.g. rackspace, corridor) and the edges denote
the existence of a path between two neighbouring nodes or topologies. At the
intermediate level, the map is represented as a Manhattan Graph where the nodes
and edges are characterized by Manhattan properties and as a Pose Graph at the
lower-most level of detail. The topological constructs are learned via a Deep
Convolutional Network while the relational properties between topological
instances are learnt via a Siamese-style Neural Network. In the paper, we show
that maintaining abstractions such as Topological Graph and Manhattan Graph
help in recovering an accurate Pose Graph starting from a highly erroneous and
unoptimized Pose Graph. We show how this is achieved by embedding topological
and Manhattan relations as well as Manhattan Graph aided loop closure relations
as constraints in the backend Pose Graph optimization framework. The recovery
of near ground-truth Pose Graph on real-world indoor warehouse scenes vindicate
the efficacy of the proposed framework.
