  This paper studies the problem of image-goal navigation which involves
navigating to the location indicated by a goal image in a novel previously
unseen environment. To tackle this problem, we design topological
representations for space that effectively leverage semantics and afford
approximate geometric reasoning. At the heart of our representations are nodes
with associated semantic features, that are interconnected using coarse
geometric information. We describe supervised learning-based algorithms that
can build, maintain and use such representations under noisy actuation.
Experimental study in visually and physically realistic simulation suggests
that our method builds effective representations that capture structural
regularities and efficiently solve long-horizon navigation problems. We observe
a relative improvement of more than 50% over existing methods that study this
task.
