Despite their widespread use in determining system attitude, Micro-Electro-Mechanical Systems (MEMS) Attitude and Heading Reference Systems (AHRS) are limited by sensor measurement biases. This paper introduces a method called MAgnetometer and GYroscope Calibration (MAGYC), leveraging three-axis angular rate measurements from an angular rate gyroscope to estimate both the hard- and soft-iron biases of magnetometers as well as the bias of gyroscopes. We present two implementation methods of this approach based on batch and online incremental factor graphs. Our method imposes fewer restrictions on instrument movements required for calibration, eliminates the need for knowledge of the local magnetic field magnitude or instrument's attitude, and facilitates integration into factor graph algorithms for Smoothing and Mapping frameworks. We validate the proposed methods through numerical simulations and in-field experimental evaluations with a sensor onboard an underwater vehicle. By implementing the proposed method in field data of a seafloor mapping dive, the dead reckoning-based position estimation error of the underwater vehicle was reduced from 10% to 0.5% of the distance traveled.

翻译：尽管微机电系统（MEMS）姿态与航向参考系统（AHRS）在确定系统姿态方面应用广泛，但其性能受限于传感器测量偏置。本文提出一种名为磁力计与陀螺仪校准（MAGYC）的方法，该方法利用角速率陀螺仪的三轴角速率测量值，同时估计磁力计的硬铁偏置与软铁偏置以及陀螺仪的偏置。我们提出了基于批处理和在线增量因子图的两种实现方法。该方法对校准所需的仪器运动限制更少，无需已知当地磁场强度或仪器姿态，并便于集成至用于平滑与建图框架的因子图算法中。我们通过数值仿真以及搭载于水下航行器的传感器实地实验评估验证了所提方法。通过在海床测绘潜航的实地数据中实施所提方法，水下航行器基于航位推算的位置估计误差从行进距离的10%降低至0.5%。