Fast and accurate pile volume estimation is a very important basic problem in mining, waste sorting, and waste disposing industry. Nevertheless, for rapid changing or badly conditioned piles like stockpiles or landfills, conventional approaches involving massive measurements may not be applicable. To solve these problems, in this work, by utilizing unmanned aerial vehicles and unmanned ground vehicles equipped with a camera, we propose a collaborative framework to estimate volumes of free-formed piles accurately in short time. Compensating aerial- and ground views enable the reconstruction of piles with steep sides that is hard to be observed by single unmanned aerial vehicle. With the help of red-green-blue image sequences captured by unmanned aerial vehicles, we are able to distinguish piles from the ground in reconstructed point clouds and automatically eliminate concave on the ground while estimating pile volume. In the experiments, we compared our method to state-of-the-art dense reconstruction photogrammetry approaches. The results show that our approach for pile volume estimation has proved its feasibility for industrial use and its availability to free-formed piles on a different scale, providing high-accuracy estimation results in short time.

中文翻译：

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空地协同 3D 重建，用于未开发环境下的快速桩体积估计

快速准确的堆体积估算是采矿、垃圾分类和垃圾处理行业中一个非常重要的基础问题。然而，对于快速变化或状况不佳的堆，如库存或垃圾填埋场，涉及大规模测量的传统方法可能不适用。为了解决这些问题，在这项工作中，通过利用配备摄像头的无人机和无人地面车辆，我们提出了一种协作框架，可以在短时间内准确估计自由成型桩的体积。补偿空中和地面视图可以重建单架无人机难以观察到的陡峭边坡。借助无人机拍摄的红绿蓝图像序列，我们能够在重建的点云中将桩与地面区分开来，并在估计桩体积时自动消除地面上的凹面。在实验中，我们将我们的方法与最先进的密集重建摄影测量方法进行了比较。结果表明，我们的桩体积估算方法已证明其工业用途的可行性及其对不同规模的自由成型桩的可用性，在短时间内提供高精度的估算结果。