Toward Appearance-based Autonomous Landing Site Identification for Multirotor Drones in Unstructured Environments
This addresses the challenge of costly data collection for terrain classifiers in drone autonomy, though it is incremental as it builds on existing methods like U-Net and synthetic data generation.
The paper tackled the problem of autonomous landing site identification for drones in unstructured environments by proposing a pipeline to automatically generate synthetic training data, which enabled training a U-Net classifier that achieved real-time performance on a drone platform.
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.