Towards Real-Time Monocular Depth Estimation for Robotics: A Survey

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 10; pp. 16940 - 16961
• Main Authors: Dong, Xingshuai, Garratt, Matthew A., Anavatti, Sreenatha G., Abbass, Hussein A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cameras; Computer vision; depth prediction; Estimation; Feature extraction; Monocular depth estimation; Motion simulation; Obstacle avoidance; Performance evaluation; Robotics; Robots; Scene analysis; single image depth estimation; Structure from motion; survey; Task analysis; Three-dimensional displays
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2022.3160741

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.