2 research outputs found
Overview obstacle maps for obstacle aware navigation of autonomous drones
Achieving the autonomous deployment of aerial robots in unknown outdoor environments using only onboard computation is a challenging task. In this study, 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. Our visionâbased 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 highâquality sparse map of
the environment by using a stateâofâtheâart 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
100 m 75 Ă m is created in â2.75 min, 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 approac