Towards Real-Time Monocular Depth Estimation for Robotics: A Survey
It addresses the lack of a comprehensive survey for researchers and practitioners in computer vision and robotics, but it is incremental as it synthesizes existing work without new experimental results.
This paper provides a comprehensive survey of monocular depth estimation (MDE) methods, reviewing 197 articles from 1970 to 2021, covering techniques, metrics, datasets, and applications in robotics to help readers navigate the field.
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.