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Visual Indoor Navigation (VIN) task has drawn increasing attention from thedata-driven machine learning communities especially with the recently reportedsuccess from learning-based methods. Due to the innate complexity of this task,researchers have tried approaching the problem from a variety of differentangles, the full scope of which has not yet been captured within an overarchingreport. This survey first summarizes the representative work of learning-basedapproaches for the VIN task and then identifies and discusses lingering issuesimpeding the VIN performance, as well as motivates future research in these keyareas worth exploring for the community.

From Seeing to Moving: A Survey on Learning for Visual Indoor Navigation (VIN)