Attitude estimation based on extended Kalman filter for a two-wheeled robot

Abstract

Aiming at the error from inertial sensors of a two-wheeled self-balanced robot,a compensating algorithm based on the extended kalman filter(EKF) was proposed.According to the inertial sensor error characteristic obatined from experiments,the error mathematical models were established by Levenberg-Marquardt nonlinear least-square iterative fit method.The error of gyro and accelerometer was calibrated by computer simulation.Using an extended kalman filter to fuse the data from the gyro and accelerometer and compensate for the sensor error,an optimal estimation for attitude was achieved.Results of simulation and field experiment demonstrated that the attitude error was suppressed validly,then an accurate and low-cost estimation of attitude was achieved,which proved that the attitude estimation method was effective and feasible.