Vision based navigation for an unmanned aerial vehicle

Abstract

We are developing a system for autonomous navigation of unmanned aerial vehicles (UAVs) based on computer vision. A UAV is equipped with on-board cameras and each UAV is provided with noisy estimates of its own state, coming from GPS/INS. The mission of the UAV is low altitude navigation from an initial position to a final position in a partially known 3-D environment while avoiding obstacles and minimizing path length. We use a hierarchical approach to path planning. We distinguish between a global offline computation, based on a coarse known model of the environment and a local online computation, based on the information coming from the vision system. A UAV builds and updates a virtual 3-D model of the surrounding environment by processing image sequences and fusing them with sensor data. Based on such a model the UAV will plan a path from its current position to the terminal point. It will then follow such path, getting more data from the on-board cameras, and refining map and local path in real time.