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Test Method for Measuring Station-Keeping With Unmanned Marine Vehicles Using Sonar or Optical Sensors

机译:使用声纳或光学传感器使用无人船用车辆测量站的测试方法

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This paper proposes a test method for measuring the ability of a USV or ROV to fixate on an underwater object, i.e., station-keeping. Station-keeping is needed to permit an operator or domain expert to stay focused on an area in an image long enough to identify objects, such as submerged cars and debris, or a condition, such as scour eroding the underwater footing of a bridge. This problem is different from traditional robot control, as the point is not to measure the positions of the robot and sensor payload but rather how well the system maintains the position of the object in the image. The test method uses the Lucas-Kanade optical flow algorithm in OpenCV to track an inexpensive raised plywood and wire fiducial. The rotational, translational, and root mean square (RMS) error is measured over a 3 minute period as well as number of image frames in which the fiducial was not visible. The method was demonstrated using a DIDSON acoustic camera, but is generalizable to other types of sonars and underwater video cameras.
机译:本文提出了一种用于测量USV或ROV以固定在水下物体上的能力的测试方法,即站保持。需要站保持允许操作员或领域专家在足够长的图像中留在图像中的一个区域,以识别淹没的汽车和碎屑,或者诸如冲刷的水下基础。此问题与传统的机器人控制不同,因为该点不是测量机器人和传感器有效载荷的位置,而是系统在图像中保持对象位置的位置。测试方法使用OpenCV中的Lucas-Kanade光学流量算法跟踪廉价的胶合板和电线基准。在3分钟的时间段内测量旋转,平移和均方根平方(RMS)误差,以及无需基准的图像帧的数量。使用DIDSON声学相机证明该方法,但是可以概括到其他类型的声纳和水下摄像机。

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