Visual steering of UAV in unknown environments

In this paper, we propose a novel approach for the visual navigation of unmanned aerial vehicles (UAV). In contrast to most available methods, a single perspective camera is used to estimate the complete set of 3D motion parameters undergone by the UAV. We establish robust point correspondences between consecutive image frames captured by the flying vehicle. Based on the estimated motion parameters as well as the reconstructed relative scene depth, a visual steering algorithm has been realized so that the UAV is capable of avoiding obstacles during navigation. The advantage of our approach lies in the fact that decision for collision avoidance is made immediately, by using purely visual information extracted from the live video sequence. Furthermore, it eliminates the time-consuming steps of explicit obstacle recognition and global reconstruction of the environment. Experimental evaluation has been carried out based on computer simulation as well as using a commercially available flying drone. It has been shown that the UAV is capable of autonomous navigation in unknown environments with arbitrary configuration of obstacles.