Improvement of Low-Cost Inertial Sensors Based on Online Estimation of Temperature Drift Error

Improving the performance of low-cost inertial sensors is meaningful for popularization of automated driving technologies. Researchers usually utilize thermal chamber to calibrate the temperature drift errors (TDEs) of expensive inertial sensors and compensate them before using them. However, it is unacceptable for low-cost inertial sensors considering the massive calibration efforts. This paper proposes a novel method to model TDE as a state so that it can be estimated online with other states together, which can adaptively compensate different TDEs without preparation. A multi-sensor fusion system for estimating yaw angle with this idea is studied. The observability of this system is analyzed and the result shows that TDE is independent from others states when there is change of temperature. Experiments are carried out and the results reveal that the performance of inertial sensor assisted with the proposed method is better than that in normal system.