论文标题
人类辅助机器人检测外来物体碎片在海洋血管内部的狭窄空间内使用概率映射
Human-Assisted Robotic Detection of Foreign Object Debris Inside Confined Spaces of Marine Vessels Using Probabilistic Mapping
论文作者
论文摘要
许多复杂的车辆系统，例如大型船舶，都包含诸如水箱之类的狭窄空间，这对于车辆的安全运行至关重要。由于有限的可及性，可见性和非结构化配置，因此人类检查此类空间尤为危险。尽管机器人提供了可行的替代方案，但它们在实现强大的自主权方面遇到了相同的挑战。在这项工作中，我们专门解决了使用基于视觉映射的系统依赖于Mahalanobis距离距离驱动的比较，在名称和在线地图之间以局部异常识别进行比较的问题，该问题依靠基于视觉映射的系统来检测限制空间内部留下的异物碎片（FOD）的问题。模拟试验显示出极高的召回率，但对于异常识别方法而言，精度较低。因此，远程人类的帮助是通过浏览离群区域的特写机器人摄像头图像来解决精度问题。进行了在线调查，以显示此援助过程的有用性。还报告了启用GPU的移动机器人平台内的物理实验，以证明FOD检测系统的可行性。
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.
