不依赖精密转台的 MEMS-IMU 误差标定补偿方法

微机电惯性测量单元(MEMS-IMU)具有尺寸小、重量轻、成本低、可靠性高等优点，在机器人、虚拟现实以及智能穿戴等诸多领域广泛应用。低成本的微机电惯性测量单元在使用过程中受噪声和零偏误差等影响，需要通过测试和误差补偿手段来提高其实际使用精度。本文提出了一种全面测试和补偿惯性测量单元误差的方法，通过建立MEMS-IMU的误差模型，使用优化方法标定误差模型中的系统误差参数；使用Allan方差分析方法确定随机误差参数；基于上述结果，采用与视觉融合的非线性优化方法在线实时估计并补偿零偏，最终达到提高定位精度的目的。通过实验分析，上述组合方法不需要使用专门测试标定设备，能够有效补偿低成本微机电惯性测量单元的误差，提高定位精度。

MEMS-IMU error calibration compensation method independent of precision turntable

The MEMS IMU has the advantages of small size, lightweight, low cost, and high reliability. It is widely used in many fields such as robotics, virtual reality and smart wear. Low-cost MEMS inertial measurement units are affected by noise and zero-bias errors in practical deployment, hence testing and error compensation methods are required to improve their actual use accuracy. This article proposes a method that comprehensively tests and compensates for the error in the inertial measurement unit. Firstly, the error model of MEMS-IMU is established, and the deterministic parameters in the error model are calibrated by the optimization method. Secondly, the Allan variance analysis method is utilized to calibrate the random error parameters. Finally, the nonlinear optimization method fused with vision is used to estimate and compensate the zero bias online and in real-time, thereby achieving the goal of improving the navigation and positioning accuracy of the MEMS-IMU. Through experimental analysis, the above combined method does not need to use the specific test and calibration equipment, and can effectively compensate for the error of the low-cost MEMS inertial measurement unit and improve the positioning accuracy.