Towards Real-Time Monocular Depth Estimation for Robotics: A Survey[-5pt]

被引：69
作者：
Dong, Xingshuai [1 ]
Garratt, Matthew A. [1 ]
Anavatti, Sreenatha G. [1 ]
Abbass, Hussein A. [1 ]
机构：
[1] Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2612, Australia
来源：
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS | 2022年 / 23卷 / 10期
关键词：
Estimation; Feature extraction; Robots; Cameras; Structure from motion; Three-dimensional displays; Task analysis; Monocular depth estimation; single image depth estimation; depth prediction; robotics; survey; OBSTACLE DETECTION; PREDICTION; NETWORK;
D O I：
10.1109/TITS.2022.3160741
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
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.
引用
收藏
页码：16940 / 16961
页数：22
相关论文
共 197 条
[1]   Adversarial Learning for Depth and Viewpoint Estimation From a Single Image [J].
Abdulwahab, Saddam ;
Rashwan, Hatem A. ;
Garcia, Miguel Angel ;
Jabreel, Mohammed ;
Chambon, Sylvie ;
Puig, Domenec .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2020, 30 (09) :2947-2958
[2]   Generative Adversarial Networks for Unsupervised Monocular Depth Prediction [J].
Aleotti, Filippo ;
Tosi, Fabio ;
Poggi, Matteo ;
Mattoccia, Stefano .
COMPUTER VISION - ECCV 2018 WORKSHOPS, PT I, 2019, 11129 :337-354
[3]  
Alhashim Ibraheem, 2018, High Quality Monocular Depth Estimation via Transfer Learning
[4]  
Almalioglu Y, 2019, IEEE INT CONF ROBOT, P5474, DOI [10.1109/icra.2019.8793512, 10.1109/ICRA.2019.8793512]
[5]   Collision Avoidance for Quadrotors with a Monocular Camera [J].
Alvarez, H. ;
Paz, L. M. ;
Sturm, J. ;
Cremers, D. .
EXPERIMENTAL ROBOTICS, 2016, 109 :195-209
[6]  
Amiri Ali Jahani, 2019, 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), P602, DOI 10.1109/ROBIO49542.2019.8961504
[7]  
[Anonymous], recognition (ICPR), DOI DOI 10.1109/CVPR.2016.470
[8]  
Armeni I., 2017, CoRR abs/1702.01105
[9]  
ATAPOURABARGHOU.A, 2018, PROC CVPR IEEE, P2800, DOI DOI 10.1109/CVPR.2018.00296
[10]  
Bahadur S., 2017, INT J RES GRANTHAALA, V5, P8
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