面向非结构化环境中多旋翼无人机基于外观的自主着陆点识别
Toward Appearance-based Autonomous Landing Site Identification for Multirotor Drones in Unstructured Environments
AI总结：
针对多旋翼无人机在非结构化环境中自主着陆点识别难题，提出自动生成合成数据训练轻量级外观地形分类器，并利用U-Net在真实数据上验证，实现实时着陆安全分割。
摘要：
多旋翼无人机飞行中的一个剩余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，将无人机的RGB图像分割为安全和不安全区域。然而，此类分类器需要图像和掩码数据集，而创建这些数据集可能成本过高。我们提出了一种自动生成合成数据集以训练这些分类器的流程，利用现代无人机自动勘测地形的能力以及从这些勘测中导出的地形模型自动计算着陆安全掩码的能力。然后，我们在合成数据集上训练U-Net，在真实世界数据上进行验证，并在我们的无人机平台上进行实时演示。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
