一种基于双目视觉的水下导引光源检测和测距方法

朱志鹏; 朱志宇

doi:10.11993/j.issn.2096-3920.2021.01.010

一种基于双目视觉的水下导引光源检测和测距方法

doi: 10.11993/j.issn.2096-3920.2021.01.010

江苏科技大学电子信息学院, 江苏镇江, 212000

基金项目: 国防基础预研项目(JCKY2017414C002).

详细信息

作者简介:
朱志鹏(1996-), 男, 在读硕士, 主要研究方向为AUV回收对接末端双目视觉定位.

中图分类号: TP242.62 P715.5
计量
- 文章访问数: 203
- HTML全文浏览量: 1
- PDF下载量: 231
- 被引次数: 0
出版历程
- 收稿日期: 2020-06-18
- 修回日期: 2020-07-21
- 刊出日期: 2021-03-01

Method for Detecting and Ranging an Underwater Guided Light Source Based on Binocular Vision

Jiangsu University of Science and Technology, School of Electronic Information, Zhenjiang 212000, China

摘要

摘要: 为满足自主水下航行器(AUV)水下对接过程中高精确性、实时性和鲁棒性等要求, 提出一种基于双目视觉的水下导引光源检测和测距方法, 其中包括水下相机标定、原始图像去噪、光源检测、位置信息解算等几个重要步骤。在原始图像去噪阶段, 引入Laplace算子改进均值去噪算法并增强图像突显光源; 接着使用基于二分法的自适应阈值二值化图像, 将光源与背景有效分开, 进一步检测并匹配左右成像平面光源; 最后, 根据双目定位原理, 利用检测出的光源信息解算相对位置。文中针对水下环境改进传统均值去噪, 更加突显导引光源信息, 以连通域为循环判断条件, 自适应获取最佳图像二值化阈值, 利用加权质心检测确定光源中心, 并通过试验验证该方法满足水下对接精度要求, 且实时性得到优化。
- 自主水下航行器 /
- 双目视觉 /
- 水下对接 /
- 图像处理 /
- 定位
Abstract: To meet the requirements of high accuracy, real-time performance, and robustness in autonomous underwater vehicle(AUV) underwater docking, this study proposes a binocular vision-based underwater guidance light source detection and ranging method. The main processes of this method includes underwater camera calibration, denoising of original images, detection of light sources, and location calculation. In the original image denoising stage, a Laplace operator is introduced to improve the mean denoising algorithm and enhance the image to highlight the light source. Then, an adaptive threshold binary image based on the dichotomy is used to effectively separate the light source from the background. Light sources on the left and right imaging planes are then detected and matched. Finally, according to the principle of binocular location, the relative position is solved by using the detected light source information. In this method, traditional mean denoising is improved for an underwater environment, and information of the guided light source is highlighted. The best image binary threshold is obtained by using the connected domain as the cyclic judgment condition, and the light source center is determined by weighted centroid detection method. The method is verified in specific experiments that meet the requirements of underwater docking accuracy. In addition, real-time performance is optimized.
- autonomous undersea vehicle(AUV) /
- binocular vision /
- underwater docking /
- image processing /
- location

HTML全文

参考文献(18)

[1]	Liam P, Sajad S, Mae S, et al. AUV Navigation and Localization: A Review[J]. IEEE Journal of Oceanic Engineering, 2014, 39(1): 131-149.
[2]	Inzartev A V, Matvienko Y V, Pavin A M, et al. Investigation of Autonomous Docking System Elements for Long Term AUV[C]//Proceedings of MTS/IEEE Oceans. Washington, USA: IEEE, 2005: 388-393.
[3]	孙叶义, 武皓微, 李晔, 等. 智能无人水下航行器水下回收对接技术综述[J]. 哈尔滨工程大学学报, 2019, 40(1): 1-11. Sun ye-yi, Wu Hao-wei, Li Ye, et al. Review of Underwater Docking Technology for Intelligent Unmanned Underwater Vehicle[J]. Journal of Harbin Engineering University, 2019, 40(1): 1-11
[4]	Yao P, Qi S B. Obstacle-avoiding Path Planning for Multiple Autonomous Underwater Vehicles with Simultaneous Arrival[J]. Science China(Technological Sciences), 2019, 62(1): 121-132.
[5]	赵霞, 袁家政, 刘宏哲. 基于视觉的目标定位技术的研究进展[J]. 计算机科学, 2016, 43(6): 10-16, 43. Zhao Xia, Yuan Jia-zheng, Liu Hong-zhe. Advances in Vision-based Target Location Technology[J]. Computer Science, 2016, 43(6): 10-16, 43.
[6]	姜言清. AUV回收控制的关键技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2016.
[7]	王晓娟. 基于视觉的AUV水下回收导引定位技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2011.
[8]	Li Y, Jiang Y, Cao J, et al. AUV Docking Experiments Based on Vision Positioning Using Two Cameras[J]. Ocean Engineering, 2015, 110: 163-173.
[9]	王国权, 周小红, 蔚立磊. 基于分水岭算法的图像分割方法研究[J]. 计算机仿真, 2009, 26(5): 255-258. Wang Guo-quan, Zhou Xiao-hong, Yu Li-lei. Image Segmentation Based on Watershed Algorithm[J]. Computer Simulation, 2009, 26(5): 255-258.
[10]	周莉莉, 姜枫. 图像分割方法综述研究[J]. 计算机应用研究, 2017, 34(7): 1921-1928. Zhou Li-li, Jiang Feng. Survey on Image Segmentation Methods[J]. Application Research of Computers, 2017, 34(7): 1921-1928.
[11]	寻言言, 薛河儒, 姜新华. 基于MATLAB的相机标定方法[J]. 内蒙古农业大学学报(自然科学版), 2014, 35(2): 164-168. Xun Yan-yan, Xue He-ru, Jiang Xin-hua. The Camera Calibration Method Based on MATLAB[J]. Journal of Inner Mongolia Agricultural University(Natural Science Edition), 2014, 35(2): 164-168.
[12]	Bouchra B, Sebastien K, Nabil E A, et al. A Flexible Technique Based on Fundamental Matrix for Camera Self-calibration with Variable Intrinsic Parameters from Two Views[J]. Journal of Visual Communication and Image Representation, 2016, 39: 40-45.
[13]	Otsu N. A Threshold Selection Method from Gray-Level Histograms[J]. IEEE Transactions on Systems Man & Cybernetics, 2007, 9(1): 62-66.
[14]	马逸东, 周顺勇. 基于连通性检测的图像椒盐噪声滤波算法[J]. 液晶与显示, 2020, 35(2): 167-172. Ma Yi-dong, Zhou Shun-yong. Salt and Pepper Noise Filtering Algorithm Based on Connectivity Detection[J]. Chinese Journal of Liquid Crystals and Displays, 2020, 35(2): 167-172.
[15]	刘子铭. 加权方向自适应全变分去噪算法[J]. 电子技术与软件工程, 2019(23): 69-70.
[16]	权稳稳. 基于视觉的水下目标识别与定位技术研究[D]. 青岛: 山东大学, 2018.
[17]	于永军, 徐锦法, 张梁, 等. 惯导/双目视觉位姿估计算法研究[J]. 仪器仪表学报, 2014, 35(10): 2170-2176. Yu Yong-jun, Xu Jin-fa, Zhang Liang, et al. Research on SINS/Binocular Vision Integrated Position and Attitude Estimation Algorithm[J]. Chinese Journal of Scientific Instrument, 2014, 35(10): 2170-2176.
[18]	Scharstein D, Szeliski R. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms[J]. Kluwer Academic Publishers, 2002, 47: 1-3.