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

Dong, Xingshuai; Garratt, Matthew A.; Anavatti, Sreenatha G.; Abbass, Hussein A.

doi:10.48550/arxiv.2111.08600

Cited by 2 publications

(3 citation statements)

References 190 publications

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“…The results obtained for monocular depth prediction are encouraging and raise the question of the use of these methods for navigation applications as expressed in Dong et al survey [3]. For the moment, only a minority of papers address the issue of lightweight architecture, usable on embedded systems [14], [10].…”

Section: Related Work a Self-supervised Monocular Depthmentioning

confidence: 94%

Quadtree Segmentation Network for Obstacle Avoidance in Monocular Navigation

Braun¹,

Morel²,

Vasseur

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Monocular depth map prediction has become in recent years a major research topic in computer vision. Especially with the emergence of self-supervised methods that have demonstrated that based on geometric properties, it is possible to obtain good results without human generated labels. But these methods are, for the moment, mainly oriented towards high resolution dense reconstruction and therefore neglect applications for robotic navigation. In this paper, we propose addressing this problem by developing a solution for navigation and obstacle avoidance. The method takes advantage of dense depth prediction to segment the view into a limited number of classes of interest for navigation. Four classes have been thus retained: one to segment the road and three to cluster obstacles by their distance (close, middle and far). As a result, the system is able to directly identify threats and areas where it can navigate. Furthermore, efficient information compression can be considered using a quadtree data structure derived from Quadtree Generating Network. Experiments conducted on the Kitti dataset have shown our proposed method can efficiently predict a quadtree segmentation directly from monocular input images. In addition, the approach tends to significantly reduce the amount of information to be predicted without any loss of accuracy.

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Section: Related Work a Self-supervised Monocular Depthmentioning

confidence: 94%

Quadtree Segmentation Network for Obstacle Avoidance in Monocular Navigation

Braun¹,

Morel²,

Vasseur

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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“…RGB-D cameras have limited depth range and they suffer from specular reflections and absorbing objects. Therefore, many depth completion approaches have been proposed to mitigate the gap between sparse and dense depth maps [44].…”

Section: Depth Estimation (De)mentioning

confidence: 99%

“…Comprehensive to the related recent surveys in MDE in terms of six parameters; "TM": training manner, "ACC": accuracy, "CT": computational time, "RQ": resolution quality, "RTI": realtime inference, "TRAN": transferability, "IDS": input data shapes. Towards Real-Time Monocular Depth Estimation for Robotics [44] 2021…”

Section: Introductionmentioning

confidence: 99%

Monocular Depth Estimation Using Deep Learning: A Review

Masoumian

Rashwan

Cristiano

et al. 2022

Sensors

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In current decades, significant advancements in robotics engineering and autonomous vehicles have improved the requirement for precise depth measurements. Depth estimation (DE) is a traditional task in computer vision that can be appropriately predicted by applying numerous procedures. This task is vital in disparate applications such as augmented reality and target tracking. Conventional monocular DE (MDE) procedures are based on depth cues for depth prediction. Various deep learning techniques have demonstrated their potential applications in managing and supporting the traditional ill-posed problem. The principal purpose of this paper is to represent a state-of-the-art review of the current developments in MDE based on deep learning techniques. For this goal, this paper tries to highlight the critical points of the state-of-the-art works on MDE from disparate aspects. These aspects include input data shapes and training manners such as supervised, semi-supervised, and unsupervised learning approaches in combination with applying different datasets and evaluation indicators. At last, limitations regarding the accuracy of the DL-based MDE models, computational time requirements, real-time inference, transferability, input images shape and domain adaptation, and generalization are discussed to open new directions for future research.

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

Cited by 2 publications

References 190 publications

Quadtree Segmentation Network for Obstacle Avoidance in Monocular Navigation

Quadtree Segmentation Network for Obstacle Avoidance in Monocular Navigation

Monocular Depth Estimation Using Deep Learning: A Review

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