  As an essential component for many autonomous driving and robotic activities
such as ego-motion estimation, obstacle avoidance and scene understanding,
monocular depth estimation (MDE) has attracted great attention from the
computer vision and robotics communities. Over the past decades, a large number
of methods have been developed. To the best of our knowledge, however, there is
not a comprehensive survey of MDE. This paper aims to bridge this gap by
reviewing 197 relevant articles published between 1970 and 2021. In particular,
we provide a comprehensive survey of MDE covering various methods, introduce
the popular performance evaluation metrics and summarize publically available
datasets. We also summarize available open-source implementations of some
representative methods and compare their performances. Furthermore, we review
the application of MDE in some important robotic tasks. Finally, we conclude
this paper by presenting some promising directions for future research. This
survey is expected to assist readers to navigate this research field.
