Self-balancing robot pose estimated based on the adaptive Kalman filter

This paper proposed a new method by which the error of two self-balancing robot sensors was reduced and avoiding conventional Kalman filter can not meet real-time modulation. In the paper, Correction algorithm can come out real-time robot posture in the right way according to the characteristics of navigation sensor error from the iteration of nonlinear least-squares error model based on the method Adaptive Kalman Filter. By computer simulation, an error through the gyro and accelerometer has been corrected. Kalman filter fused the data of gyroscope and accelerometer adaptation, and errors of the sensors pose estimation was corrected. The test results and simulation proved that this method of reducing posture estimation error is feasible and effective, and can achieve better accurate estimates inexpensively.