A novel MEMS-RIMU self-calibration method based on gravity vector observation

Abstract

Abstract The microelectromechanical system (MEMS) redundant inertial measurement unit (RIMU) is a low-cost navigation system, which can improve reliability effectively. In existing MEMS-RIMU calibration schemes, extra equipment with higher accuracy is required. However, on some occasions, a rotation platform or other reference equipment cannot be provided, and calibration of the MEMS-RIMU is limited. To solve the problem, a novel self-calibration method for the MEMS-RIMU based on gravity vector observation is proposed, which can estimate the bias, scale errors and alignment errors of accelerometers and gyroscopes without extra equipment or rotation platforms. In this paper, a novel calibration model for accelerometers and gyroscopes is built by analyzing the relationship between gravity vector observation and the measurement value of the MEMS-RIMU. There is no attitude information in the calibration model, so the RIMU can be calibrated without attitude references via extra equipment. Then, a derivative unscented Kalman filter is proposed to estimate the bias, scale errors and alignment errors of accelerometers and gyroscopes, which can reduce the computation load. Finally, the simulation and experimental results demonstrate the efficiency and accuracy of the proposed calibration scheme.