  Can an autonomous agent navigate in a new environment without building an
explicit map?
  For the task of PointGoal navigation ('Go to $\Delta x$, $\Delta y$') under
idealized settings (no RGB-D and actuation noise, perfect GPS+Compass), the
answer is a clear 'yes' - map-less neural models composed of task-agnostic
components (CNNs and RNNs) trained with large-scale reinforcement learning
achieve 100% Success on a standard dataset (Gibson). However, for PointNav in a
realistic setting (RGB-D and actuation noise, no GPS+Compass), this is an open
question; one we tackle in this paper. The strongest published result for this
task is 71.7% Success.
  First, we identify the main (perhaps, only) cause of the drop in performance:
the absence of GPS+Compass. An agent with perfect GPS+Compass faced with RGB-D
sensing and actuation noise achieves 99.8% Success (Gibson-v2 val). This
suggests that (to paraphrase a meme) robust visual odometry is all we need for
realistic PointNav; if we can achieve that, we can ignore the sensing and
actuation noise.
  With that as our operating hypothesis, we scale the dataset and model size,
and develop human-annotation-free data-augmentation techniques to train models
for visual odometry. We advance the state of art on the Habitat Realistic
PointNav Challenge from 71% to 94% Success (+23, 31% relative) and 53% to 74%
SPL (+21, 40% relative). While our approach does not saturate or 'solve' this
dataset, this strong improvement combined with promising zero-shot sim2real
transfer (to a LoCoBot) provides evidence consistent with the hypothesis that
explicit mapping may not be necessary for navigation, even in a realistic
setting.
