  Visual Indoor Navigation (VIN) task has drawn increasing attention from the
data-driven machine learning communities especially with the recently reported
success from learning-based methods. Due to the innate complexity of this task,
researchers have tried approaching the problem from a variety of different
angles, the full scope of which has not yet been captured within an overarching
report. This survey first summarizes the representative work of learning-based
approaches for the VIN task and then identifies and discusses lingering issues
impeding the VIN performance, as well as motivates future research in these key
areas worth exploring for the community.
