Triaxial MEMS accelerometers are widely used for inertial sensing, navigation, and sensor fusion, but existing calibration methods often rely on costly reference setups or nonlinear iterative optimization, limiting their efficiency and applicability to low-cost or self-calibrating systems. We present attitude-aided linear accelerometer calibration (ALAC), a method that operates on any platform providing orientation information, such as turntables, robotic arms, or inertial measurement units. ALAC constructs a combined error matrix (CEM) to represent sensor errors in a unified calibration model and enables linear least-squares estimation. The bias and gravity vector are jointly estimated, implicitly accounting for platform misalignment, and matrix decomposition of the CEM recovers scale, non-orthogonality, and alignment rotation parameters. Under static gravity, calibration is formulated as a constrained homogeneous least-squares (CHLS) problem and solved in closed form using standard linear algebra. Only five arbitrarily oriented measurements are required, and a recursive extension supports online or in-field calibration. Experiments on a stationary robot-mounted accelerometer and a quasi-static public IMU trajectory show that ALAC, in both offline and online modes, outperforms reference-based and online baselines in accuracy and robustness to sensor noise. On the same dataset, it matches iterative self-calibration under filtered conditions and surpasses all evaluated baselines on raw measurements. These results demonstrate a robust and practical calibration scheme for MEMS-based inertial platforms, especially low-cost IMUs and online calibration scenarios.

翻译：摘要：三轴MEMS加速度计广泛应用于惯性感知、导航与传感器融合领域，但现有标定方法通常依赖昂贵的基准设备或非线性迭代优化，限制了其在低成本或自标定系统中的效率与适用性。本文提出姿态辅助线性加速度计标定方法（ALAC），该方法可在任意提供姿态信息的平台（如转台、机械臂或惯性测量单元）上运行。ALAC通过构建组合误差矩阵（CEM）建立统一的标定模型，实现线性最小二乘估计。该方法联合估计偏置与重力矢量，隐式补偿平台对准误差，并通过CEM的矩阵分解恢复比例因子、非正交性与对准旋转参数。在静态重力条件下，标定问题被表述为约束齐次最小二乘（CHLS）问题，并通过标准线性代数闭合求解。该方法仅需五次任意取向的测量，其递归扩展形式支持在线或现场标定。在固定机器人搭载加速度计与准静态公开IMU轨迹上的实验表明，ALAC离线与在线模式在精度及对传感器噪声的鲁棒性方面均优于基于基准的方法与在线基线方法。在相同数据集上，该方法的滤波条件结果与迭代自标定相当，且在原始测量结果上超越所有评估基线。研究结果证实了面向MEMS惯性平台（尤其低成本IMU与在线标定场景）的鲁棒实用标定方案。