Calibration of Low Cost IMU's Inertial Sensors for Improved Attitude Estimation

To better solve the attitude of robots with high precision using low cost inertial measurement unit (IMU), we design novel calibration methods, namely twelve positions calibration method, method based on ellipsoid fitting and eight positions calibration method, to effectively calibrate accelerometer, magnetometer and gyroscope, respectively. An attitude estimator using data from the multi-sensors calibrated by our proposed methods was designed based on Kalman Filter, with its estimated value of the attitude prediction deviation fed back to the predicted value as the prediction deviation at the end of each filtering period. Finally, relevant experiments are designed to verify the validity of the proposed attitude solution method as well as the proposed calibration methods, with results showing that the roll and pitch angle measured by the attitude measurement unit have an effective resolution of 0.1 degrees and the yaw angle has an effective resolution of 1 degrees. The innovation of this paper is that the proposed calibration methods can be carried out with simple tools, eliminating the need for expensive and complicated multi-axis turntables, and the designed attitude measurement unit based on calibrated low cost IMU's inertial sensors has smaller size, higher resolution in roll and pitch, and much lower cost compared with the commercial MTi-G-710. The accuracy of the attitude solution for such IMU is high enough for the application on robots.