Inertial measurement units (IMUs) have been widely used to provide accurate location and movement measurement solutions, along with the advances of modern manufacturing technologies. The scale factors of accelerometers and gyroscopes are linear when the range of the sensors are reasonably small, but the factor becomes nonlinear when the range gets much bigger. Based on this observation, this article presents a calibration method for low-cost IMU by effectively deriving the nonlinear scale factors of the sensors. Two motion patterns of the sensor on a rigid object are moved to collect data for calibration: One motion pattern is to upcast and rotate the rigid object, and another pattern is to place the rigid object on a stable base in different attitudes. The rotation motion produces centripetal and Coriolis force, which increases the measurement range of accelerometers. Four cost functions with different weight factors and two sets of data are utilized to optimize the IMU parameters. The weight factor comes from derived formula with input values which are the variance of the noise of the sampled data. The proposed approach was validated and evaluated on both synthetic and real-world data sets, and the experimental results demonstrated the superiority of the proposed approach in improving the accuracy of IMU for long-range use. In particular, the errors of acceleration and angular velocity led by our algorithm are significantly smaller than those resulted from the existing approaches using the same testing data sets, demonstrating a remarkable improvement of 64.12% and 47.90%, respectively.

中文翻译：

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使用多芯光纤布拉格光栅和 Si-APD 中的双光子吸收过程进行多点弯曲测量


随着现代制造技术的进步，惯性测量单元（IMU）已被广泛用于提供精确的位置和运动测量解决方案。当传感器的范围相当小时，加速度计和陀螺仪的比例因子是线性的，但当范围变得更大时，该因子变得非线性。基于这一观察，本文通过有效推导传感器的非线性比例因子，提出了一种低成本 IMU 的校准方法。移动传感器在刚性物体上的两种运动模式来收集校准数据：一种运动模式是将刚性物体向上翻转和旋转，另一种模式是将刚性物体以不同的姿态放置在稳定的底座上。旋转运动产生向心力和科里奥利力，从而增加了加速度计的测量范围。利用四个具有不同权重因子的成本函数和两组数据来优化 IMU 参数。权重因子来自派生公式，输入值为采样数据噪声的方差。所提出的方法在合成数据集和真实数据集上进行了验证和评估，实验结果证明了所提出的方法在提高 IMU 远程使用精度方面的优越性。特别是，我们的算法导致的加速度和角速度的误差明显小于使用相同测试数据集的现有方法所产生的误差，分别显着提高了 64.12% 和 47.90%。