MEMS Gyro Bias Estimation in Accelerated Motions Using Sensor Fusion of Camera and Angular-Rate Gyroscope

Abstract

Although the accuracy of MEMS gyroscopes has been extremely improved, in some aspects, such as stability of bias, they still suffer from some big error sources, like run-to-run bias, which determines the sensor price but is not negligible even inexpensive sensors. Due to the fact that run-to-run bias is a kind of stochastic parameter, it has to be measured by utilizing online methods. Utilizing a novel, fast and efficient vision-based rotation estimation algorithm for ground vehicles, we have developed a visual gyroscope that is used in our sensor fusion system, in order to estimate run-to-run bias of the MEMS gyroscope, accurately. Comparing with similar approaches that use GPS, odometer, accelerometer or magnetometer, the most important advantage of the proposed vision-based sensor-fusion framework is its accuracy in accelerated motions. Moreover, it can be used at environments that have a magnetic field, such as urban environments, without depending on external signals. We have evaluated the efficiency of the proposed system using real datasets, recorded from a car moving in urban areas. According to our experimental results, the proposed algorithm is capable of estimating bias of gyroscope after a convergence time of about 6 seconds and improving the accuracy of the MEMS gyroscope, which provides the possibility of using cheaper sensors for high-accuracy demands.