  Navigation research is attracting renewed interest with the advent of
learning-based methods. However, this new line of work is largely disconnected
from well-established classic navigation approaches. In this paper, we take a
step towards coordinating these two directions of research. We set up classic
and learning-based navigation systems in common simulated environments and
thoroughly evaluate them in indoor spaces of varying complexity, with access to
different sensory modalities. Additionally, we measure human performance in the
same environments. We find that a classic pipeline, when properly tuned, can
perform very well in complex cluttered environments. On the other hand, learned
systems can operate more robustly with a limited sensor suite. Overall, both
approaches are still far from human-level performance.
