A remaining challenge in multirotor drone flight is the autonomous identification of viable landing sites in unstructured environments. One approach to solve this problem is to create lightweight, appearance-based terrain classifiers that can segment a drone's RGB images into safe and unsafe regions. However, such classifiers require data sets of images and masks that can be prohibitively expensive to create. We propose a pipeline to automatically generate synthetic data sets to train these classifiers, leveraging modern drones' ability to survey terrain automatically and the ability to automatically calculate landing safety masks from terrain models derived from such surveys. We then train a U-Net on the synthetic data set, test it on real-world data for validation, and demonstrate it on our drone platform in real-time.

翻译：多旋翼无人机飞行中一个持续存在的挑战是在非结构化环境中自主识别可行的着陆点。解决此问题的一种方法是创建轻量级的、基于外观的地形分类器，能够将无人机的RGB图像分割为安全与不安全区域。然而，此类分类器需要图像和掩码数据集，而创建这些数据集的成本可能极高。我们提出一种自动生成合成数据集的流程来训练这些分类器，该方法利用现代无人机自动勘测地形的能力，以及从此类勘测所得地形模型自动计算着陆安全掩码的能力。随后，我们在合成数据集上训练了一个U-Net模型，在真实世界数据上进行了验证测试，并在我们的无人机平台上实现了实时演示。