Sparse Depth Calculation Using Real-Time Key-Point Detection and Structure from Motion for Advanced Driver Assist Systems

Prakash, Charan D.; Li, Jinjin; Akhbari, Farshad; Karam, Lina J.

doi:10.1007/978-3-319-14249-4_71

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…where s(t, τ ) is the scale, and T Z (t, τ ) is the camera translation in depth. Prakash et al [9] present a SfM-based sparse depth estimation method. The proposed approach takes a sequence of 5 to 8 images captured by a monocular camera to estimate a depth map.…”

Section: A Methodologiesmentioning

confidence: 99%

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

Dong¹,

Garratt²,

Anavatti³

et al. 2021

Preprint

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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.

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Section: A Methodologiesmentioning

confidence: 99%

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

Dong¹,

Garratt²,

Anavatti³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Other researchers detected road lanes instead of using additional devices or calibration boards. Xu et al [24] and Prakash et al [25] detected road lanes and used them for estimating the orientation and location of the front camera. The estimated parameters are used for IPM.…”

Section: Vehicle-mounted Camera Calibrationmentioning

confidence: 99%

“…Other approaches detect road lanes or the host vehicle instead of utilizing addi devices [24][25][26][27][28]. These approaches also focus on the calibration of only one camera et al [29] calibrated four AVM cameras to align adjacent images using detected lanes.…”

Section: Introductionmentioning

confidence: 99%

A Novel AVM Calibration Method Using Unaligned Square Calibration Boards

Lee

2021

Sensors

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An around view monitoring (AVM) system acquires the front, rear, left, and right-side information of a vehicle using four cameras and transforms the four images into one image coordinate system to monitor around the vehicle with one image. Conventional AVM calibration utilizes the maximum likelihood estimation (MLE) to determine the parameters that can transform the captured four images into one AVM image. The MLE requires reference data of the image coordinate system and the world coordinate system to estimate these parameters. In conventional AVM calibration, many aligned calibration boards are placed around the vehicle and are measured to extract the reference sample data. However, accurately placing and measuring the calibration boards around a vehicle is an exhaustive procedure. To remediate this problem, we propose a novel AVM calibration method that requires only four randomly placed calibration boards by estimating the location of each calibration board. First, we define the AVM errors and determine the parameters that minimize the error in estimating the location. We then evaluate the accuracy of the proposed method through experiments using a real-sized vehicle and an electric vehicle for children to show that the proposed method can generate an AVM image similar to the conventional AVM calibration method regardless of a vehicle’s size.

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“…Prakash [25] presented a sparse depth estimation approach based on SfM and a multi-scale keypoint detector. A monocular video sequence is fed into the framework to facilitate training, which performs feature matching between consecutive frames and leverages two-view geometry to determine sparse depth values, resulting in fast and robust performance.…”

Section: Motion-based Monocular Depth Estimationmentioning

confidence: 99%

Learning-based monocular vision obstacle detection and avoidance for UAV navigation in urban airspace

Zhang

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Huat, for his invaluable guidance, support, and encouragement throughout my academic journey. His expertise, vision, and patience have been instrumental in shaping my research work and developing my skills as a researcher. His insightful comments, constructive feedback, and constant motivation have inspired me to strive for excellence and to push the boundaries of knowledge.I would also like to extend my deepest appreciation to my co-supervisor, Prof. Lv Chen, for his valuable input, encouragement, and support. His expertise, dedication, and commitment have been critical in shaping my research work and in guiding me through the challenges of academic research. His guidance and mentorship have been invaluable in my academic growth and personal development.I am deeply grateful to my friends in the lab, especially Dai Wei, Deng Chao, Pang Bizhao, Huxinting, Zhang Mingcheng, and Yu Qing, for their invaluable support, encouragement, and camaraderie. Their friendship, laughter, and shared "Everything negative -pressure, challenges -is all an opportunity for me to rise."

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Sparse Depth Calculation Using Real-Time Key-Point Detection and Structure from Motion for Advanced Driver Assist Systems

Cited by 4 publications

References 18 publications

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

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

A Novel AVM Calibration Method Using Unaligned Square Calibration Boards

Learning-based monocular vision obstacle detection and avoidance for UAV navigation in urban airspace

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