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A comparison of sensors for underground void mapping by unmanned aerial vehicles

机译:无人机航空车辆地下空隙映射传感器的比较

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Unmanned aerial vehicles (UAVs) are being employed in a rapidly increasing number of applications in mining, including the emerging area of mapping underground void spaces such as stopes, which are otherwise inaccessible to humans, automated ground vehicles and survey technologies. Void mapping can provide both visual rock surface and 3D structural information about stopes, supporting more effective planning of ongoing blast designs. Underground stope mapping by UAVs, however, involves overcoming a number of engineering challenges to allow flights beyond operator line-of-sight where there is no global positioning system (GPS), natural or artificial light, or existing communications infrastructure. This paper describes the construction of a UAV sensor suite that uses sound navigation and ranging (SONAR) data to create a rough 3D model of the underground UAV operational environment in real time to provide operators with high situational awareness for beyond line-of-sight operations. The system also provides a backup when dust obscures visual sensors to provide situation awareness and a coarser, but still informative, 3D model of the underground space. Typically, light detection and ranging (LIDAR) systems have superseded SONAR sensors for similar applications. LIDAR is much more accurate than SONAR, but has several disadvantages. SONAR sensor data is sparse, and therefore much easier to process in real time on-board the UAV than LIDAR. The SONAR sensor hardware is also lighter than current LIDAR systems, which is of importance regarding the constrained payload capacity of UAVs. However, the most important factor that makes SONAR stand out in this application is its ability to operate in dusty or smoke-filled environments. The UAV system was tested both above and below-ground using a predefined path with check point locations for the UAV to follow. Due to the lack of GPS, survey points in combination with photogrammetry allowed the UAVs location to be estimated. This allowed the system to be tested to determine how accurate the SONAR data is in comparison with 3D modelling via photogrammetry of images from a separate digital single-lens reflex camera. Comparing the shape of void surfaces determined by photogrammetry with that determined by SONAR provides quantifiable accuracy when the photogrammetry models are used as ground truth data. Above-ground and underground pilot studies have determined that SONAR sensors provide acceptable accuracy compared with modelling via photogrammetry, sufficient to provide effective situational awareness for human operation of the UAV beyond line-of-sight.
机译:无人驾驶飞行器(无人机)正在采用迅速越来越多的采矿次数,包括绘制地下空隙空间的新兴区域,例如停止,否则对人类,自动化地面车辆和调查技术等不可进入。 void映射可以提供有关停止的视觉岩石表面和3D结构信息,支持更有效的持续爆炸设计规划。然而,无人机的地下占据映射涉及克服许多工程挑战,以允许运营商超出运营商的航班,其中没有全球定位系统(GPS),天然或人造光或现有的通信基础设施。本文介绍了UAV传感器套件的构建,它使用声音导航和测距(SONAR)数据实时创建了地下无人机运营环境的粗略3D模型,为运营商提供了高景点操作的高情景意识。当灰尘遮挡视觉传感器时,该系统还提供了备份,以提供情况意识和较粗糙,但仍然是地下空间的3D模型。通常,光检测和测距(LIDAR)系统具有用于类似应用的超声声卡传感器。利达比声纳更准确,但有几个缺点。声纳传感器数据稀疏,因此实时地处理比LIDAR更容易处理。声纳传感器硬件也比当前的LIDAR系统轻,这对UAV的受限有效载荷容量具有重要性。然而,使声纳在这个应用程序中脱颖而出的最重要因素是其在尘土飞扬或含烟的环境中运行的能力。 UAV系统使用预定义路径在接地上以上和下面测试,具有用于UAV的检查点位置。由于缺乏GPS,调查点与摄影测量相结合允许估计无人机地点。这允许测试系统以确定声纳数据与来自单独的数字单镜头反射相机的图像摄影测量相比的SONAR数据的准确程度。比较由摄影测量测定的空隙表面的形状与Sonar确定的,在摄影测量模型用作地面真理数据时提供可量化的精度。地上和地下试点研究确定了声纳传感器提供可接受的准确性,与通过摄影测量建模相比,足以为无人机的人类运营提供有效的态势意识,超出视线。

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