Inertial measurement units (IMU) have been wildly used to provide accurate location and movement measurement solutions, along with the advances of modern manufacturing technologies. The scale factors of accelerometers and gyroscopes are linear when the range of the sensors are reasonably small, but the factor becomes non-linear when the range gets much bigger. Based on this observation, this paper presents a calibration method for low-cost IMU, by effectively deriving the nonlinear scale

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... of the sensors. Two motion patterns of the sensor on a rigid object are moved to collect data for calibration, one motion pattern is to upcast and rotation the rigid object, another pattern is to place the rigid object on a stable base in different attitudes. The rotation motion produces centripetal and Coriolis force, which increases the measurement range of accelerometers. Four cost functions with different weight factors and two sets of data are utilized to optimize the IMU parameters. The weight factor comes from derived formula with input values which are the variance of the noise of the sampled data. The proposed approach was validated and evaluated on both synthetic and real-world data sets, and the experimental results demonstrated the superiority of the proposed approach in improving the accuracy of IMU for long range use. In particular, the errors of acceleration and angular velocity led by our algorithm are significantly smaller than those resulted from the existing approaches using the same testing datasets, demonstrating a remarkable improvement of 64.12% and 47.90%, respectively.