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SLAM-GDM Implementation on Robot Operating System for Gas Source Localization

机译:血液源定位机器人操作系统上的血液GDM实现

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Gas Distribution Mapping (GDM) can be defined as visualization for the spatial distribution of gas concentration in an unknown indoor environment. Recently, incidents of gas leakage have increased gradually worldwide. This will significantly increase the casualty rate at the workplace. This paper focusses on the implementation of Kernel DM based SLAM-GDM on Robot Operating System (ROS), a novel approach that will be useful in the effort of localizing the gas source during gas leakage inspection. This approach has been conducted successfully by using ROS-architecture implemented system, tested in simulation and on a mobile robot. The result shows that this implementation achieved average accuracy of 0.71m and 0.32m and therefore deemed suitable for gas source location prediction.
机译:气体分配映射(GDM)可以定义为在未知室内环境中的气体浓度的空间分布的可视化。 最近,天然气泄漏事件逐渐增加了全球。 这将大大提高工作场所的伤亡率。 本文重点是在机器人操作系统(ROS)上基于Kernel DM基于血液GDM的实施,这是一种新的方法,即在气体泄漏检查期间本地化气体源的努力。 通过使用ROS-Architecture实现的系统,在模拟和移动机器人上进行了成功进行了这种方法。 结果表明,该实现实现了0.71米和0.32米的平均精度,因此认为适用于气源位置预测。

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