用合成数据训练无人机自动识别安全着陆点
Toward Appearance-based Autonomous Landing Site Identification for Multirotor Drones in Unstructured Environments
- 通过地形模型自动生成带安全标签的合成图像
- 在真实数据上验证，实现实时安全区域分割
- 适合无人值守环境下的无人机自主降落
多旋翼无人机在非结构化环境中自主识别可行着陆点仍是挑战。一种解决方案是构建轻量级、基于外观的地形分类器，将无人机的RGB图像分割为安全与危险区域。但这类分类器需要大量带标注的图像数据集，而人工标注成本高昂。本文提出一套自动化流程，利用现代无人机自动测绘地形的能力，结合从测绘数据生成的地形模型，自动生成带有着陆安全掩码的合成图像数据集。随后在该合成数据集上训练U-Net模型，并在真实世界数据上进行验证，最终在实际无人机平台上实现实时运行。
原文摘要 · Abstract (English)
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.
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