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Merging of range images for inspection or safety applications

机译:合并范围图像以进行检查或安全应用

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Range imagers provide useful information for part inspection, robot control or human safety applications in industrial environments. However, some applications may require more information than range data from a single viewpoint. Therefore, multiple range images must be combined to create a three-dimensional representation of the scene. Although simple in its principle, this operation is not straightforward to implement in industrial systems, since each range image is affected by noise. In this paper, we present two specific applications where merging of range images must be performed. We use the same processing pipeline for both applications : conversion from range image to point clouds, elimination of degrees of freedom between different clouds, validation of the merged results. Nevertheless, each step in this pipeline requires dedicated algorithms for our example applications. The first application is high resolution inspection of large parts, where many range images are acquired sequentially and merged in a post-processing step, allowing to create a virtual model of the part observed, typically larger than the instrument's field of view. The key requirement in this application is high accuracy for the merging of multiple point clouds. The second application discussed is human safety in a human/robot environment: range images are used to ensure that no human is present in the robot's zone of operation, and can trigger the robot's emergency shutdown when needed. In this case, range image merging is required to avoid uncertainties due to occlusions. The key requirement here is real-time operation, namely the merging operation should not introduce a significant latency in the data processing pipeline. For both application cases, the improvements brought by merging multiple range images are clearly illustrated.
机译:范围成像器可为工业环境中的零件检查,机器人控制或人身安全应用提供有用的信息。但是,从单个角度来看,某些应用程序可能需要比范围数据更多的信息。因此,必须组合多个范围的图像以创建场景的三维表示。尽管其原理很简单,但是由于每个距离图像都受噪声影响,因此在工业系统中实现该操作并不简单。在本文中,我们介绍了必须执行距离图像合并的两个特定应用程序。我们对两种应用程序使用相同的处理管道:从范围图像到点云的转换,消除不同云之间的自由度,验证合并结果。但是,此流程中的每个步骤都需要针对示例应用程序的专用算法。第一个应用是大型零件的高分辨率检查,其中许多距离图像被顺序采集并在后处理步骤中合并,从而允许创建观察到的零件的虚拟模型,通常大于仪器的视野。此应用程序中的关键要求是多点云合并的高精度。讨论的第二个应用是在人/机器人环境中的人身安全:范围图像用于确保机器人的操作区域中没有人,并且可以在需要时触发机器人的紧急停机。在这种情况下,需要进行距离图像合并,以避免由于遮挡而产生的不确定性。这里的关键要求是实时操作,即合并操作不应在数据处理管道中引入明显的延迟。对于这两种应用案例,都清楚地说明了合并多个范围图像带来的改进。

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