论文标题
VQF：具有偏置估计和磁性干扰排斥的高度精确的IMU方向估计
VQF: Highly Accurate IMU Orientation Estimation with Bias Estimation and Magnetic Disturbance Rejection
论文作者
论文摘要
惯性测量单元（IMU）的微型化促进了它们在越来越多的应用域中的广泛使用。方向估计是惯性运动跟踪中大多数进一步数据处理步骤的先决条件，例如位置/速度估计，关节角度估计和3D可视化。估计方向的错误严重影响了所有进一步的处理步骤。现有算法的最新系统比较表明，开箱即用的准确性通常很低，并且需要特定于应用的调整才能获得高精度。在目前的工作中，我们提出并广泛评估了基于四个基于四元的方向估计算法，该算法基于一种新的方法，该方法是在几乎惯用的框架中过滤加速度测量值的新方法，其中包括陀螺仪偏置估计和磁性干扰抑制的扩展，以及对离线数据处理的变体。与所有现有工作相反，我们使用大量公开数据集和八种文献方法进行比较，进行了广泛的评估。提出的方法始终优于所有文献方法，并达到平均RMSE为2.9°，而文献方法获得的误差范围为5.3°至16.7°。由于评估是在一个非常多样化的数据集集合中使用一个固定参数化进行的，因此我们得出结论，所提出的方法为广泛的运动，传感器硬件和环境条件提供了前所未有的开箱即用性能。预期估计精度的这种增益有望提高基于IMU的运动分析的领域，并在众多应用中提供绩效益处。提供的开源实现使得使用所提出的方法变得容易。
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.
