Toward Appearance-based Autonomous Landing Site Identification for Multirotor Drones in Unstructured Environments

Autor:	Springer, Joshua, Guðmundsson, Gylfi Þór, Kyas, Marcel
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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. Comment: 14 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.15486 Zobrazit plný text záznamu View this record from Arxiv