Design and Implementation of an IMU Calibration Platform - Chen, Ge (My University, 2021)
Inertial Measurements Units (IMU) are widely used in robotics, such as navigation and mapping tasks. Nowadays, many commercial off-the-shelf devices like smartphones and drones are mostly equipped with low-cost embedded IMU sensors. Nevertheless, systematic errors affect low-cost IMUs due to imprecise scaling factors and axes misalignments that decrease the accuracy in position and attitude estimation. Therefore, a procedure to calibrate these IMUs at reasonable costs is essential in many engineering applications. Traditionally the calibration of such IMUs has been done by using special mechanical platforms such as a robotic manipulator. However, such mechanical platforms used for calibration are usually costly. In this report, we propose a method to calibrate IMUs with the help of a low-cost platform. The procedure is based on a multi-position scheme, providing scale and misalignments factors for both the accelerometers and gyroscopes triads, as well as estimating sensor biases. The method only requires a sensor to be attached to the calibration platform. We use an Arduino Due board to control the motor on the platform and set different attitudes for the rotatable shaft. We design a data collection and calibration protocol that exploits an effective parameterless static filter to reliably detect the static intervals in the sensor measurements, where local stability of the gravity's magnitude can be assumed. In the protocol, the accelerometers triad is first calibrated from measurement samples in the static intervals. Next, these results are exploited to calibrate the gyroscopes through a robust numerical integration. The performances of the proposed calibration technique have been evaluated via actual experiments with a commercial high-precision IMU sensor.
Keywords: IMU; calibration algorithm; sensor error model; allan variance; cost function; Runge-Kutta integration; motion detector; data similarity measures; aligning time series data