Visual-based deep sea docking simulation of underwater vehicle using dual-eyes cameras with lighting adaptation
Visual-based deep sea docking simulation of underwater vehicle using dual-eyes cameras with lighting adaptation
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DOI：
10.1109/oceansap.2016.7485423
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发表时间：
2016-04
期刊：
影响因子：
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通讯作者：
Myo Myint;Kenta Yonemori;A. Yanou;K. Lwin;M. Minami;Shintaro Ishiyama
中科院分区：
文献类型：
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作者：
Myo Myint;Kenta Yonemori;A. Yanou;K. Lwin;M. Minami;Shintaro Ishiyama
本文提出了一种基于视觉的水下航行器对接/归航系统，该系统是在深海试验环境下进行的。代替使用其他非接触式传感器测量车辆的绝对位置，提出了使用双目相机和3D目标对象的相对位置和姿态（姿态）的估计。对于相对位姿估计，基于3D模型的识别方法由于其实时有效的性能而被应用。根据遗传算法的有效性、简单性和实时性的可重复评估能力，本文采用遗传算法作为“多步遗传算法”的改进形式，对视频输入的动态图像中代表相对位姿的候选基因进行评估，直到得到最可信位姿的最佳基因。P控制器用于使用来自双眼相机的实时图像来控制车辆以获得期望的姿态。由于水下环境的复杂性，本文还研究了水下机器人姿态波动导致的可变光照环境下的深海对接作业，并相应改变了两个LED照明方向，这对视觉伺服系统提出了巨大的挑战。作为本文的主要贡献，因此，我们已经开发了视觉伺服使用自适应系统的未知照明环境。以遥控潜水器为试验平台，在模拟室内水池中进行了试验。实验结果表明，视觉伺服性能在不同的照明条件下，对接性能，使用该系统。
This paper proposes visual-based underwater vehicle docking/homing system under the environment especially simulated for deep sea trial. Instead of measuring absolute position of vehicle using other non-contact sensors, estimation of the robot's relative position and posture (pose) using dual-eyes cameras and 3D target object is proposed. For relative pose estimation, 3D model-based recognition approach is applied because of its real-time effective performance. According to effectiveness, simplicity and repeatable evaluation ability for real-time performance, Genetic Algorithm (GA) is utilized here as a modified form of " Multi-step GA" to evaluate the gene candidates that represent relative poses until getting the best gene with the most trustful pose in dynamic images input by videorate. P controller is used to control the vehicle for the desired pose using real-time images from dual-eyes cameras. Since the underwater environment is complex, this work also addresses to the experiment for deep sea docking operation under changeable lighting environment derived from pose fluctuation of underwater vehicle with two LED-lighting direction altered accordingly that offers huge challenges for visual servoing. As the main contribution for this paper, therefore, we have developed visual servoing using adaptive system for unknown lighting environment. Remotely Operated Vehicle (ROV) is used as a test bed and the experiments are conducted in simulated indoor pool. Experimental results show visual servoing performance under varying lighting conditions and docking performance using proposed system.