  Being able to perceive the semantics and the spatial structure of the
environment is essential for visual navigation of a household robot. However,
most existing works only employ visual backbones pre-trained either with
independent images for classification or with self-supervised learning methods
to adapt to the indoor navigation domain, neglecting the spatial relationships
that are essential to the learning of navigation. Inspired by the behavior that
humans naturally build semantically and spatially meaningful cognitive maps in
their brains during navigation, in this paper, we propose a novel
navigational-specific visual representation learning method by contrasting the
agent's egocentric views and semantic maps (Ego$^2$-Map). We apply the visual
transformer as the backbone encoder and train the model with data collected
from the large-scale Habitat-Matterport3D environments. Ego$^2$-Map learning
transfers the compact and rich information from a map, such as objects,
structure and transition, to the agent's egocentric representations for
navigation. Experiments show that agents using our learned representations on
object-goal navigation outperform recent visual pre-training methods. Moreover,
our representations significantly improve vision-and-language navigation in
continuous environments for both high-level and low-level action spaces,
achieving new state-of-the-art results of 47% SR and 41% SPL on the test
server.
