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基于语义分割和双目视觉的水下机器人测距技术研究

Research on underwater robot ranging technology based on semantic segmentation and binocular vision.

作者信息

Hu Qing, Wang Kekuan, Ren Fushen, Wang Zhongyang

机构信息

Sanya Offshore Oil and Gas Research Institute, Northeast Petroleum University, Sanya, 572025, China.

CNPC Engineering Technology Research Company Limited, Tianjin, 300451, China.

出版信息

Sci Rep. 2024 May 29;14(1):12309. doi: 10.1038/s41598-024-63017-8.

DOI:10.1038/s41598-024-63017-8
PMID:38811640
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11637160/
Abstract

Based on the principle of light refraction and binocular ranging, the underwater imaging model is obtained. It provides a theoretical basis for underwater camera calibration. In order to meet the requirement of underwater vehicle to identify and distance underwater target, a new underwater vehicle distance measurement system based on semantic segmentation and binocular vision is proposed. The system uses Deeplabv3 + to identify the underwater target captured by the binocular camera and generate the target map, which is then used for binocular ranging. Compared with the binocular ranging using the original drawing, the measurement accuracy of the proposed method has not changed, the measurement speed is increased by 30%, and the error rate is controlled within 5%, which meets the needs of underwater robot operations.

摘要

基于光折射原理和双目测距原理，得到水下成像模型。它为水下相机标定提供了理论依据。为满足水下航行器识别和测量水下目标距离的需求，提出了一种基于语义分割和双目视觉的新型水下航行器测距系统。该系统使用Deeplabv3+识别双目相机捕获的水下目标并生成目标地图，然后用于双目测距。与使用原始图像进行双目测距相比，该方法的测量精度不变，测量速度提高了30%，错误率控制在5%以内，满足了水下机器人作业的需求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1653/11637160/9f7337653374/41598_2024_63017_Fig14_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1653/11637160/909207f4c264/41598_2024_63017_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1653/11637160/dd97e3e377d4/41598_2024_63017_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1653/11637160/68af36740fc8/41598_2024_63017_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1653/11637160/42f9a132f31c/41598_2024_63017_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1653/11637160/5b8221ca2d96/41598_2024_63017_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1653/11637160/04549e4d615c/41598_2024_63017_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1653/11637160/6398e2f915e1/41598_2024_63017_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1653/11637160/078aaae2f083/41598_2024_63017_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1653/11637160/cc8a630fde5f/41598_2024_63017_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1653/11637160/3f8646c9a9f0/41598_2024_63017_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1653/11637160/00d93ff36754/41598_2024_63017_Fig11_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1653/11637160/9f91ba798e9f/41598_2024_63017_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1653/11637160/9f7337653374/41598_2024_63017_Fig14_HTML.jpg

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2
Artificial Intelligence in Meta-optics.人工智能在超表面光学中的应用。
Chem Rev. 2022 Oct 12;122(19):15356-15413. doi: 10.1021/acs.chemrev.2c00012. Epub 2022 Jun 24.
3
Image Segmentation Using Deep Learning: A Survey.基于深度学习的图像分割技术综述。
IEEE Trans Pattern Anal Mach Intell. 2022 Jul;44(7):3523-3542. doi: 10.1109/TPAMI.2021.3059968. Epub 2022 Jun 3.
4
Camera Calibration Using Gray Code.基于格雷码的相机标定。
Sensors (Basel). 2019 Jan 10;19(2):246. doi: 10.3390/s19020246.
5
Fully Convolutional Networks for Semantic Segmentation.全卷积网络用于语义分割。
IEEE Trans Pattern Anal Mach Intell. 2017 Apr;39(4):640-651. doi: 10.1109/TPAMI.2016.2572683. Epub 2016 May 24.
6
Influence of camera calibration conditions on the accuracy of 3D reconstruction.相机校准条件对三维重建精度的影响。
Opt Express. 2016 Feb 8;24(3):2678-86. doi: 10.1364/OE.24.002678.
7
Extrinsic Calibration of Camera Networks Using a Sphere.使用球体进行相机网络的外部校准。
Sensors (Basel). 2015 Aug 4;15(8):18985-9005. doi: 10.3390/s150818985.