부산대학교 도서관

Obstacle maps are essential for autonomous UAVs to achieve navigation. Different kinds of sensors are used for 3D map reconstruction, such as: LIDAR, radar and cameras. However, 3D obstacle map reconstruction from UAVs has been still a challenging task due to the limitation of on-board computational resources. With the development of wireless streaming technologies, a cloud-based solution for visual map reconstruction would solve the issue. A collaborative aerial-ground system for visual map reconstruction using wireless streaming includes several drones and a fixed or mobile ground station. Images are taken from equipped cameras on the UAV while being streamed to a ground station through a high-speed wireless link, such as: WiFi or the mobile network (4G, 5G). The map is then quickly reconstructed from the images by means of a very strong computer, and the output obstacles map is then sent back to the UAV for navigation tasks. In this paper, we propose and implement a cloud-based visual map reconstruction for UAV navigation with a WiFi link. The system worked well for both indoor and outdoor experiments with achieved transfer times per image of less than 0.29 s and a total 3D reconstruction time of less than 205 s for 87 images with a resolution of 1920x1080 px.