Abstract
Usually, standard inertial navigation unit (INU) with global positioning system (GPS) provides relatively poor accuracy in altitude estimation, while autonomous landing of unmanned aerial vehicles (UAVs) requires accurate position estimation. In this paper, a UAV navigation system with aid from an external camera for landing is investigated. This paper presents: (i) a sensor fusion algorithm for passive monocular vision and INU based on the extended Kalman filter (EKF) considering measurement delay to improve the accuracy of position estimates, and (ii) a robust object-detection vision algorithm using optical flow. Pilot controlled landing experiments on a NASA UAV platform and the filter simulations validate the feasibility and performance of the proposed approach.
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