Velocity estimation of an UAV using visual and IMU data in a GPS-denied environment

This paper proposes two methods for UAV translational velocity estimation based on onboard sensing only. Spherical image measurements provided by a single onboard camera along with IMU data consist the main information feeding the estimators. The first algorithm consists of a nonlinear observer, designed using Lyapunov synthesis, while the second is based on the Unscented Kalman filtering technique. Differently with respect to existing approaches, the velocity is directly estimated from the onboard image without the need to fully estimate the vehicle 3D pose. The low computational requirement makes the proposed techniques suitable for applications where the execution time is of prominent importance even if no powerful hardware is available, as it is the case with UAV systems. Experimental results validate the algorithms, and this with the use of only four image features.