"State Estimation of an Autonomous Helicopter Using Kalman Filtering "
This paper presents a technique to accurately esti mate the state of a robot helicopter using a combina tion of gyroscopes, accelerometers, inclinometers and GPS. Simulation results of state estimation of the he licopter are presented using Kalman filtering based on sensor modeling. The number of estimated states of helicopter is nine : three attitudes(`; OE; /) from the gyroscopes, three accelerations(¨x; ¨ y; ¨ z) and three posi tions (x; y; z) from the accelerometers. Two Kalman filters were used, one for the gyroscope data and the other for the accelerometer data. Our approach is unique because it explicitly avoids dynamic modeling of the system and allows for an elegant combination of sensor data available at different frequencies. We also describe the larger context in which this work is embedded, namely the design and implementation of an autonomous robot helicopter.
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