基于无人机图像的大规模点云体积分析加速

D. Stojcsics, Zsolt Domozi, A. Molnár
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引用次数: 1

摘要

小型无人机,无论是经典的固定翼设计,还是如此迅速普及的旋翼设计,现在正在彻底改变航空调查的整个工作过程。在本文中,我们将讨论在我们之前的工作中已经介绍过的大规模点云处理的质量特征的可能性,主要是减少我们之前工作中提出的大型点云处理的执行时间。根据我们开发的工作流程,我们使用无人机调查工作区域,通常是采石场,然后我们从图片中创建一个高分辨率的3D点云。然后,使用ICP算法对时间序列照片进行拟合,并进行体积分析。在本例中,ICP算法和体积计算是非常耗时的操作,因为每个3D模型由~ 2000万个点组成。市场上两家领先的图形处理单元生产商已经为通用计算提供了硬件资源。通过这种方式,我们可以看到将这些耗时的计算实现到目标硬件中的良好机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Large-Scale Point Cloud Based Volume Analysis Acceleration Based on UAV Images
Small-sized drones, either having classical fixed-wing design or the so rapidly widespread rotorcraft design, are now revolutionizing the complete work process of aerial surveys. In the paper, we will discuss the possibilities of the quality characteristics of large-scale point cloud processing, already introduced in our previous work, and primarily the reduction of the execution time of the large-point cloud processing presented in our previous work. Based on the work process developed by us, we survey the work area, typically a quarry, using UAVs, and then we create a high resolution 3D point cloud out of the pictures. Then, the time series photos are fitted to each other using the ICP algorithm and we conduct the volume analysis. In the present case, the ICP algorithm and volume calculation were very time-consuming operations, as every 3D model was made up of ~20 million points. The two leading graphics processing unit producers of the market have made the resources of the hardware available for general-purpose calculations. In this way, we can see good opportunity in the implementation of these time-consuming calculations into the target hardware.
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