Towards autonomous flight of micro aerial vehicles using ORB-SLAM

In the last couple of years a novel visual simultaneous localisation and mapping (SLAM) system, based on visual features, has emerged as one of the best, if not the best, systems for estimating the 6D camera pose whilst building a 3D map of the observed scene. This method is called ORB-SLAM and one of its key ideas is to use the same visual descriptor, a binary descriptor called ORB, for all the visual tasks, this is, for feature matching, relocalisation and loop closure. On the top of this, ORB-SLAM combines local and graph-based global bundle adjustment, which enables a scalable map generation whilst keeping real-time performance. Therefore, motivated by its performance in terms of processing speed, robustness against erratic motion and scalability, in this paper we present an implementation of autonomous flight for a low-cost micro aerial vehicle (MAV), where ORB-SLAM is used as a visual positioning system that feeds a PD controller that controls pitch, roll and yaw. Our results indicate that our implementation has potential and could soon be implemented on a bigger aerial platform with more complex trajectories to be flown autonomously.