Vision-Based State Estimation for Autonomous Micro Air Vehicles

plane. This paper explores both of these robustness issues using results from a micro air vehicle simulation model developed at the NASA Langley Research Center. In particular, a hierarchy of dynamic models, ranging from a random walk model to a high-fidelity nonlinear micro air vehicle model, is employed in the Kalman filter for a simulated micro air vehicle trajectory with varying levels of measurement noise. It is demonstrated that the visionbased measurement updates in the filter are capable of compensating for significant modeling errors and filter initialization errors. As would be expected, superior overall results are achieved using higher-fidelity dynamic modelsintheKalman filter.Theworkpresentedinthispaperrepresentsthe firststeptowardtheultimateobjectiveof incorporating vision-based state estimation into the design of autonomous flight control systems for micro air vehicles operating in urban environments.