The exceptional flight capabilities of insects have long amazed and inspired researchers and roboticists striving to make Micro Aerial Vehicles (MAVs) smaller and more agile. It is well known that optical flow plays a prominent role in insect flight control and navigation, and hence it is being increasingly investigated for applications in flying robots as well. However, optical flow based strategies for estimation and stabilization of orientation remain obscure in literature. In this report, we introduce a novel state estimation algorithm based on optical flow measurements and the knowledge of efference copies. The proposed technique estimates the following states of a flying robot (constrained to move with three degrees of freedom): roll angle, rate of change of roll angle, horizontal and vertical components of velocity and height. The estimator only utilizes the knowledge of control inputs and optical flow measurements obtained from a downward looking monocular camera. Through non-linear observability analysis, we theoretically prove the feasibility of estimating the attitude of a MAV using ventral flow and divergence measurements. Based on the findings of the observability analysis, an extended Kalman filter state estimator is designed and its performance is verified in simulations and through flight data recorded on a real flying robot. To the best of our knowledge, the introduced strategy is the first attitude estimation technique that utilizes monocular optical flow as the only sensory information.

Besides the investigation on optical flow based attitude estimation technique, this thesis presents a comprehensive literature survey on the main topics relevant to the work.