The recovery of freshly fallen meteorites from tracked and triangulated meteors is critical to determining their source asteroid families. Even though our ability to locate meteorite falls continues to improve, the recovery of meteorites remains a challenge due to large search areas with terrain and vegetation obscuration. To improve the efficiency of meteorite recovery, we have tested the hypothesis that meteorites can be located using machine learning techniques and an autonomous drone. To locate meteorites autonomously, a quadcopter drone first conducts a grid survey acquiring top-down images of the strewn field from a low altitude. The drone-acquired images are then analyzed using a machine learning classifier to identify meteorite candidates for follow-up examination. Here, we describe a proof-of-concept meteorite classifier that deploys off-line a combination of different convolution neural networks to recognize meteorites from images taken by drones in the field. The system was implemented in a conceptual drone setup and tested in the suspected strewn field of a recent meteorite fall near Walker Lake, Nevada.

中文翻译：

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使用自主无人机和机器学习回收陨石

从跟踪和三角测量的流星中回收新落下的陨石对于确定它们的来源小行星家族至关重要。尽管我们定位陨石坠落的能力不断提高，但由于地形和植被遮蔽的大型搜索区域，陨石的恢复仍然是一个挑战。为了提高陨石回收的效率，我们测试了可以使用机器学习技术和自主无人机定位陨石的假设。为了自主定位陨石，四旋翼无人机首先进行网格调查，从低空获取散落场地的自上而下图像。然后使用机器学习分类器分析无人机获取的图像，以确定后续检查的陨石候选物。这里，我们描述了一个概念验证陨石分类器，它离线部署了不同卷积神经网络的组合，以从现场无人机拍摄的图像中识别陨石。该系统是在概念无人机设置中实施的，并在内华达州沃克湖附近最近一次陨石坠落的疑似散落场地中进行了测试。