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首页> 外文期刊>Journal of Field Robotics >What is a Hole? Discovering Access Holes in Disaster Rubble with Functional and Photometric Attributes
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What is a Hole? Discovering Access Holes in Disaster Rubble with Functional and Photometric Attributes

机译:什么是孔?通过功能和光度学属性发现灾难瓦砾中的检修孔

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摘要

The collapse of buildings and other structures in heavily populated areas often results in human victims becoming trapped within the resulting rubble. This rubble is often unstable, difficult to traverse, and dangerous for emergency first responders tasked with finding, stabilizing, and extricating entombed or hidden victims through access holes in the rubble. Recent work in scene mapping and reconstruction using photometric color and metric depth (RGB-D) data collected by unmanned aerial vehicles (UAVs) suggests the possibility of automatically identifying potential access holes into the interior of rubble. This capability would greatly improve search operations by directing the limited human search capacity to areas where access holes might exist. This paper presents a novel approach to automatically identifying access holes in rubble. The investigation begins by defining an access hole in terms that allow for their algorithmic identification as a potential means of accessing the interior of rubble. This definition captures the functional and photometric attributes of holes. From this definition, a set of hole-related features for detection is presented. Experiments were conducted using RGB-D data collected over a real-world disaster training facility using a UAV. Empirical evaluation suggests the efficacy of the proposed approach for successfully identifying potential access holes in disaster rubble.
机译:人口稠密地区的建筑物和其他建筑物倒塌通常导致受害人被困在由此产生的瓦砾中。对于那些负责通过瓦砾中的出入孔寻找,稳定和解救被埋葬或隐藏的受害者的紧急情况的第一响应者来说,该瓦砾通常是不稳定的,难以穿越的,并且是危险的。使用无人飞行器(UAV)收集的光度颜色和度量深度(RGB-D)数据进行的场景映射和重建的最新工作表明,可以自动识别瓦砾内部的潜在检修孔。通过将有限的人工搜索能力指向可能存在访问漏洞的区域,此功能将极大地改善搜索操作。本文提出了一种新颖的方法来自动识别瓦砾中的检修孔。研究从定义访问孔开始,这些访问孔允许对其进行算法识别,作为访问瓦砾内部的潜在手段。此定义捕获了孔的功能和光度学属性。根据这个定义,提出了一组用于检测的与孔有关的特征。实验是通过使用无人机在现实世界的灾难训练设施中收集的RGB-D数据进行的。实证评估表明,所提出的方法能够成功地识别灾难性瓦砾中的潜在通孔。

著录项

  • 来源
    《Journal of Field Robotics》 |2016年第6期|825-836|共12页
  • 作者单位

    Department of Computer Science, Ryerson University, Toronto, Ontario, Canada;

    Department of Computer Science, Ryerson University, Toronto, Ontario, Canada;

    School of Information and Communications Technology, Seneca College, Toronto, Ontario, Canada;

    Department of Computer Science, Ryerson University, Toronto, Ontario, Canada;

    Department of Computer Science, Ryerson University, Toronto, Ontario, Canada;

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  • 正文语种 eng
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