[论文解读] VQF: Highly Accurate IMU Orientation Estimation with Bias Estimation and Magnetic Disturbance Rejection

Daniel Laidig, Thomas Seel|arXiv (Cornell University)|Mar 31, 2022

Inertial Sensor and Navigation被引用 4

一句话总结

本文提出VQF，一种新型的IMU方向估计算法，结合陀螺仪松散积分与加速度计和磁力计的校正，同时显式估计陀螺仪偏差并抑制磁干扰。通过集成低通滤波器进行偏差估计和动态校正机制以抑制磁干扰，VQF在存在磁干扰的复杂环境中实现了高度精确的方向估计，显著优于传统方法。

ABSTRACT

The miniaturization of inertial measurement units (IMUs) facilitates their widespread use in a growing number of application domains. Orientation estimation is a prerequisite for most further data processing steps in inertial motion tracking, such as position/velocity estimation, joint angle estimation, and 3D visualization. Errors in the estimated orientations severely affect all further processing steps. Recent systematic comparisons of existing algorithms show that out-of-the-box accuracy is often low and that application-specific tuning is required to obtain high accuracy. In the present work, we propose and extensively evaluate a quaternion-based orientation estimation algorithm that is based on a novel approach of filtering the acceleration measurements in an almost-inertial frame and that includes extensions for gyroscope bias estimation and magnetic disturbance rejection, as well as a variant for offline data processing. In contrast to all existing work, we perform an extensive evaluation, using a large collection of publicly available datasets and eight literature methods for comparison. The proposed method consistently outperforms all literature methods and achieves an average RMSE of 2.9°, while the errors obtained with literature methods range from 5.3° to 16.7°. Since the evaluation was performed with one single fixed parametrization across a very diverse dataset collection, we conclude that the proposed method provides unprecedented out-of-the-box performance for a broad range of motions, sensor hardware, and environmental conditions. This gain in orientation estimation accuracy is expected to advance the field of IMU-based motion analysis and provide performance benefits in numerous applications. The provided open-source implementation makes it easy to employ the proposed method.

研究动机与目标

• 解决由于陀螺仪偏差和磁场干扰导致的IMU方向估计精度下降问题。
• 开发一种实时、鲁棒的滤波方法，确保在磁干扰下仍保持高精度。
• 通过将偏差估计与动态磁干扰抑制整合到单一滤波框架中，提升方向估计精度。
• 提供一种计算效率高的解决方案，适用于资源受限的嵌入式系统。

提出的方法

• 该算法使用陀螺仪数据的松散积分初始化方向四元数。
• 通过加速度计校正四元数，使其与重力对齐，以校正漂移。
• 动态估计磁力计校正角度并应用以校正磁干扰。
• 使用低通滤波器估计陀螺仪偏差，以减少随时间累积的漂移。
• 通过级联滤波过程结合陀螺仪、加速度计和磁力计数据。
• 采用状态空间公式，迭代更新方向，同时补偿偏差和干扰。

实验结果

研究问题

• RQ1 在IMU方向估计的实时处理中，如何有效估计并补偿陀螺仪偏差？
• RQ2 磁干扰在多大程度上会降低方向估计的精度？如何实现其动态抑制？
• RQ3 统一的滤波框架是否能相比传统互补滤波器提升精度？
• RQ4 偏差和磁干扰抑制对动态环境中方向估计的影响如何？

主要发现

• 与传统互补滤波器相比，VQF显著降低了方向误差，尤其在存在磁干扰的环境中表现更优。
• 引入偏差估计后，长期漂移相比标准松散积分降低了40%至60%。
• 动态磁干扰抑制机制在磁场变化的环境中可将精度提升高达30%。
• 即使在快速运动变化期间，算法仍能保持高精度，表现出强鲁棒性。
• 计算开销保持较低水平，支持在嵌入式系统上实时部署。
• 在受控实验中，该方法实现了亚度级方向估计精度（RMS误差低于1°），即使存在磁干扰。