Autonomous underwater vehicles (AUVs) have been deployed for underwater exploration. However, its potential is confined by its limited on-board battery energy and data storage capacity. This problem has been addressed using docking systems by underwater recharging and data transfer for AUVs. In this work, we propose a vision based framework for underwater docking following these systems. The proposed framework comprises two modules; (i) a detection module which provides location information on underwater docking stations in 2D images captured by an on-board camera, and (ii) a pose estimation module which recovers the relative 3D position and orientation between docking stations and AUVs from the 2D images. For robust and credible detection of docking stations, we propose a convolutional neural network called Docking Neural Network (DoNN). For accurate pose estimation, a perspective-n-point algorithm is integrated into our framework. In order to examine our framework in underwater docking tasks, we collected a dataset of 2D images, named Underwater Docking Images Dataset (UDID), in an experimental water pool. To the best of our knowledge, UDID is the first publicly available underwater docking dataset. In the experiments, we first evaluate performance of the proposed detection module on UDID and its deformed variations. Next, we assess the accuracy of the pose estimation module by ground experiments, since it is not feasible to obtain true relative position and orientation between docking stations and AUVs under water. Then, we examine the pose estimation module by underwater experiments in our experimental water pool. Experimental results show that the proposed framework can be used to detect docking stations and estimate their relative pose efficiently and successfully, compared to the state-of-the-art baseline systems.
翻译:水下自主潜水器(AUVs)已部署用于水下勘探,但其潜力因机载电池能量和数据储存能力有限而受到限制,这一问题已经通过水下补给系统和AUV数据传输数据对接系统加以解决。在这项工作中,我们提议了一个水下对接框架。拟议框架由两个模块组成:(一) 检测模块,提供由机载相机摄取的2D图像的水下对接站定位信息;(二) 构成估算模块,从2D图像中恢复对接站和AUV之间的相对3D位置和方向。为了对停靠站进行强有力和可信的检测,我们建议建立一个名为Docking Neural网络(DoNNN)的脉动神经网络。为了准确的表面估计,一个透视点算法将纳入我们的框架。为了检查我们在水下对接任务中的框架,我们收集了一套2D图像的数据集,名为Interwater Docking 图像集(UDD) 数据集(ADID) 和实验水下测算框架(UD) 能够通过实验水下对水下测算结果进行测试。我们第一次了解的最佳位置,UDID是用来进行相对对水底底底实验的实验的实验,我们测测算的实验模型的模型的模型显示的模型,我们提出的对底基模模模模模模数。我们用来评估。