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一种用于 USV 自主导航的新型水边线检测方法。

A Novel Water-Shore-Line Detection Method for USV Autonomous Navigation.

机构信息

School of Navigation, Wuhan University of Technology, Wuhan 430063, China.

Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China.

出版信息

Sensors (Basel). 2020 Mar 18;20(6):1682. doi: 10.3390/s20061682.

DOI:10.3390/s20061682
PMID:32197317
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7147473/
Abstract

For the navigation of an unmanned surface vehicle (USV), detection and recognition of the water-shore-line (WSL) is an important part of its intellectualization. Current research on this issue mainly focuses on the straight WSL obtained by straight line fitting. However, the WSL in the image acquired by boat-borne vision is not always in a straight line, especially in an inland river waterway. In this paper, a novel three-step approach for WSL detection is therefore proposed to solve this problem through the information of an image sequence. Firstly, the initial line segment pool is built by the line segment detector (LSD) algorithm. Then, the coarse-to-fine strategy is used to obtain the onshore line segment pool, including the rough selection of water area instability and the fine selection of the epipolar constraint between image frames, both of which are demonstrated in detail in the text. Finally, the complete shore area is generated by an onshore line segment pool of multi-frame images, and the lower boundary of the area is the desired WSL. In order to verify the accuracy and robustness of the proposed method, field experiments were carried out in the inland river scene. Compared with other detection algorithms based on image processing, the results demonstrate that this method is more adaptable, and can detect not only the straight WSL, but also the curved WSL.

摘要

对于无人水面艇（USV）的导航，水边线（WSL）的检测和识别是其智能化的重要组成部分。目前关于该问题的研究主要集中在通过直线拟合获得的直线 WSL 上。然而，船载视觉获取的图像中的 WSL 并不总是直线，尤其是在内河航道中。因此，本文提出了一种新颖的三步 WSL 检测方法，通过图像序列的信息来解决这个问题。首先，通过线段检测器（LSD）算法构建初始线段池。然后，采用由粗到精的策略来获取岸线线段池，包括水域不稳定的粗选和帧间极线约束的精选，文本中对此进行了详细的说明。最后，通过多帧图像的岸线线段池生成完整的岸区，该区域的下边界即为所需的 WSL。为了验证所提出方法的准确性和鲁棒性，在内河场景中进行了现场实验。与其他基于图像处理的检测算法相比，实验结果表明，该方法更具适应性，不仅可以检测直线 WSL，还可以检测曲线 WSL。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f47f/7147473/1fcdcea5dfc3/sensors-20-01682-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f47f/7147473/76f4d59c326f/sensors-20-01682-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f47f/7147473/b1f19912a2ca/sensors-20-01682-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f47f/7147473/3115f8dd54fd/sensors-20-01682-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f47f/7147473/093e4cea6844/sensors-20-01682-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f47f/7147473/fbadd1727fd7/sensors-20-01682-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f47f/7147473/d45dc934761a/sensors-20-01682-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f47f/7147473/6710c044a3bd/sensors-20-01682-g015a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f47f/7147473/e6ca27a64a39/sensors-20-01682-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f47f/7147473/3346f06122b3/sensors-20-01682-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f47f/7147473/c244cc16f222/sensors-20-01682-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f47f/7147473/74d2330a624a/sensors-20-01682-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f47f/7147473/1fcdcea5dfc3/sensors-20-01682-g020.jpg

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