Many complex vehicular systems, such as large marine vessels, contain confined spaces like water tanks, which are critical for the safe functioning of the vehicles. It is particularly hazardous for humans to inspect such spaces due to limited accessibility, poor visibility, and unstructured configuration. While robots provide a viable alternative, they encounter the same set of challenges in realizing robust autonomy. In this work, we specifically address the problem of detecting foreign object debris (FODs) left inside the confined spaces using a visual mapping-based system that relies on Mahalanobis distance-driven comparisons between the nominal and online maps for local outlier identification. Simulation trials show extremely high recall but low precision for the outlier identification method. The assistance of remote humans is, therefore, taken to deal with the precision problem by going over the close-up robot camera images of the outlier regions. An online survey is conducted to show the usefulness of this assistance process. Physical experiments are also reported on a GPU-enabled mobile robot platform inside a scaled-down, prototype tank to demonstrate the feasibility of the FOD detection system.
翻译:许多复杂的车辆系统,如大型海洋船只,都包含水箱等封闭空间,这对于车辆的安全运行至关重要,由于无障碍程度有限、可见度低和结构化配置不完善,人类视察这些空间尤其危险;机器人提供了可行的替代办法,但在实现强有力的自主方面,它们也遇到同样的挑战;在这项工作中,我们具体地解决了利用基于视觉绘图的系统探测封闭空间内留下的外国物体碎片的问题,该系统依靠马哈拉诺比斯远程驱动的图像系统,对标称地图和在线地图进行局部外部识别的比较;模拟试验显示,外观识别方法的回顾率极高,但精确度很低;因此,远程人类的协助是用来应对精确问题的,办法是翻越外部区域的近距离机器人相机图像;进行在线调查,以显示这一援助进程的效用;还报告了在缩小规模、原型坦克内一个GPU驱动的移动机器人平台上进行的物理实验,以展示FOD探测系统的可行性。