ABSTRACT Mobile robot carrying gas sensors have been widely used in mobile olfaction applications. One of the challenging tasks in this research field is Gas Distribution Mapping (GDM). GDM is a representation of how volatile organic compound is spatially dispersed within an environment. This paper addresses the effect of obstacles towards GDM for indoor environment. This work proposes a solution by improvising the Kernel DM + V technique using propagated distance transform (DT) as the weighing function. Since DT computations are CPU heavy, parallel computing, using Compute Unified Device Architecture (CUDA) available in Graphics Processing Unit (GPU), is used to accelerate the DT computation. The proposed solution is compared with the Kernel DM + V algorithm, presenting that the proposed method drastically improves the quality of GDM under various kernel sizes. The study is also further extended towards the effect of obstacles on gas source localization task. The outcome of this work proves that the proposed method shows better accuracy for GDM estimation and gas source localization if obstacle information is considered. GRAPHICAL ABSTRACT

中文翻译：

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通过传播距离变换改进基于移动机器人的气体分布映射，用于结构化室内环境

摘要 携带气体传感器的移动机器人已广泛应用于移动嗅觉应用。该研究领域的一项具有挑战性的任务是气体分布图 (GDM)。GDM 表示挥发性有机化合物在环境中的空间分布情况。本文讨论了室内环境中障碍对 GDM 的影响。这项工作通过使用传播距离变换 (DT) 作为加权函数改进 Kernel DM + V 技术提出了一种解决方案。由于 DT 计算占用大量 CPU，因此使用图形处理单元 (GPU) 中可用的计算统一设备架构 (CUDA) 进行并行计算来加速 DT 计算。将提出的解决方案与 Kernel DM + V 算法进行比较，表明所提出的方法在各种内核大小下极大地提高了 GDM 的质量。该研究还进一步扩展到障碍对气源定位任务的影响。这项工作的结果证明，如果考虑障碍物信息，所提出的方法在 GDM 估计和气源定位方面表现出更好的准确性。图形概要