A Heading Gyro Bias Online Calibration Method for Autonomous Navigation System

In the waist-worn indoor Autonomous Navigation System based on Dead-Reckoning principle using low-cost MEMS (Micro-Electro Mechanical Systems) inertial sensors, heading gyro bias error with poor repeatability is one key factor, which significantly reduces positioning accuracy. For this problem, the paper designs a closed-loop walking track and the corresponding indoor corridors azimuth introduced online is used as the observed information by Kalman Filter for heading gyro bias online calibration. Thus, the positioning error caused by the large gyro bias repeatability error can be reduced. The effectiveness of this method is demonstrated by multi-groups of positioning experiments. In these experiments, their average total distance was 1270. 7m. For the experimental results, the uncalibrated positioning error rates ranged from 0.59% to 1.63% and the calibrated were from 0.25% to 0.65%. Experimental results indicate that the proposed method is effective to calibrate heading gyro bias for restraining heading drift and improving positioning accuracy.