Achieving the autonomous deployment of aerial robots in unknown outdoor environments using only onboard computation is a challenging task. In this work we have developed a solution to demonstrate the feasibility of autonomously deploying drones in unknown outdoor environments, with the main capability of providing an obstacle map of the area of interest in a short period of time. We focus on use cases where no obstacle maps are available beforehand, for instance in search and rescue scenarios, and on increasing the autonomy of drones in such situations. The presented vision-based mapping approach consists of two separate steps. First, the drone performs an overview flight at a safe altitude acquiring overlapping nadir images, while creating a high quality sparse map of the environment by using a state-of-the-art photogrammetry method. Second, this map is georeferenced, densified by fitting a mesh model and converted into an Octomap obstacle map, which can be continuously updated while performing a task of interest near the ground or in the vicinity of objects. The generation of the overview obstacle map is performed in almost real-time on the onboard computer of the drone, a map of size 100mx75m is created in 2.75min, therefore with enough time remaining for the drone to execute other tasks inside the area of interest during the same flight. We evaluate quantitatively the accuracy of the acquired map and the characteristics of the planned trajectories. We further demonstrate experimentally the safe navigation of the drone in an area mapped with our proposed approach.
ASJC Scopus subject areas
- !!Control and Systems Engineering
- !!Computer Science Applications