Abstract
Abstract This paper presents a navigation system based on Kalman complementary filtering for position and attitude estimation, with an application for Unmanned Air Vehicles (UAVs), in denied Global Positioning System (GPS) areas. Using inertial measurements, vector observations and landmarks positioning, the proposed complementary filters provide attitude estimates resorting to Euler angles representation and position estimates relative to a fixed inertial frame, while compensating for rate gyro and velocity biases using a gyroscope noise and velocity bias models. Stability and performance properties for the operating conditions are derived and the procedure on how to tune the parameters of the filters in the frequency domain is emphasized. Requirements on low computational burden were a priority in both the vision algorithm and navigation system, making it suitable for off-the-shelf hardware. Experimental results obtained in real time with an implementation of the proposed algorithm running on a laptop communicating with an AR.Drone 2.0 via Wi-Fi are presented and discussed.
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