Robust Equipment-Free Calibration of Low-Cost Inertial Measurement Units

Abstract

Low-cost Inertial Measurement Units (IMUs) are ubiquitously used in the attitude estimation of cell phones and robots. Accurate and robust IMU calibration is required to ensure attitude estimation accuracy. This paper proposes an accurate and robust equipment-free IMU calibration method. We do not assume gyroscope biases are invariant during calibration compared with conventional methods because low-cost IMUs like MPU6000 have large gyroscope biases instability. We propose introducing biases removal and outlier-aware optimization to alleviate the impact of variant gyroscope biases. Furthermore, we introduce a multi-resolution analysis based static detector to detect subtle IMU motion in real data collection. Our detector can detect 84% subtle motions (1-degree rotations) present in the simulated calibration data while the conventional variance thresholding detector can only detect 31% of them. In addition, we derive a proper data collection method to guide the user to collect data effectively. We benchmark our method with another existing equipment-free method with synthetic and real datasets. The results in synthetic datasets show that our method is 25% more accurate and robust than the existing method. The results in real datasets vouch that our method achieves an estimation of IMU intrinsic parameters comparable to the ground truth. Furthermore, the roll and pitch estimation of MPU6000 using our calibration method are close to (< 0.15° in ±30°) that of an expensive factory-calibrated IMU in real testing.