  "Embodied visual navigation" problem requires an agent to navigate in a 3D
environment mainly rely on its first-person observation. This problem has
attracted rising attention in recent years due to its wide application in
autonomous driving, vacuum cleaner, and rescue robot. A navigation agent is
supposed to have various intelligent skills, such as visual perceiving,
mapping, planning, exploring and reasoning, etc. Building such an agent that
observes, thinks, and acts is a key to real intelligence. The remarkable
learning ability of deep learning methods empowered the agents to accomplish
embodied visual navigation tasks. Despite this, embodied visual navigation is
still in its infancy since a lot of advanced skills are required, including
perceiving partially observed visual input, exploring unseen areas, memorizing
and modeling seen scenarios, understanding cross-modal instructions, and
adapting to a new environment, etc. Recently, embodied visual navigation has
attracted rising attention of the community, and numerous works has been
proposed to learn these skills. This paper attempts to establish an outline of
the current works in the field of embodied visual navigation by providing a
comprehensive literature survey. We summarize the benchmarks and metrics,
review different methods, analysis the challenges, and highlight the
state-of-the-art methods. Finally, we discuss unresolved challenges in the
field of embodied visual navigation and give promising directions in pursuing
future research.
