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.

翻译：视觉室内导航(VIN)任务已引起数据驱动机器学习界的日益关注,特别是最近所报告的学习方法的成功,由于这项任务的内在复杂性,研究人员试图从不同的角度来处理这个问题,其全部范围尚未在一份总体报告中加以阐述,这项调查首先总结了以学习为基础的方法执行网络任务的代表性工作,然后查明和讨论阻碍虚拟导航业绩的遗留问题,并激励今后在这些值得社区探索的关键领域开展研究。