This chapter provides an overview of UAV localization with a focus on aided inertial localization, that is, algorithms for fusing data from, for example, satellite positioning systems, barometric sensors, and magnetometers with inertial sensors to provide real-time position and orientation. The presentation is based in the use of the Extended Kalman Filter (EKF) as the core tool for data fusion, which is commonly used for its simplicity, computational efficiency, and optimality (with respect to sensor noise characteristics). This chapter presents an example implementation of aided inertial localization on a UAV as a tutorial in the key concepts in airborne localization and as a basic guide toward more complicated implementation.
UAV Localization Using Inertial Sensors and Satellite Positioning Systems
Handbook of Unmanned Aerial Vehicles ; Kapitel : 3 ; 433-460
09.08.2014
28 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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