State estimation of an autonomous helicopter using Kalman filtering

Presents a technique to accurately estimate the state of a robot helicopter using a combination of gyroscopes, accelerometers, inclinometers and GPS. Simulation results of state estimation of the helicopter are presented using Kalman filtering based on sensor modeling. The number of estimated states of helicopter is nine : three attitudes(/spl theta/,/spl phi/,/spl psi/) from the gyroscopes, three accelerations(x/spl I.oarr/,y/spl I.oarr/,z/spl I.oarr/) and three positions (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 can 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.