Head Orientation Prediction: Delta Quaternions Versus Quaternions
Head Orientation Prediction: Delta Quaternions Versus Quaternions
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DOI：
10.1109/tsmcb.2009.2016571
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发表时间：
2009-12-01
影响因子：
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通讯作者：
Motai, Yuichi
中科院分区：
文献类型：
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作者：
Himberg, Henry;Motai, Yuichi
头盔显示器在模拟环境中的显示滞后会导致沉浸感的丧失，从而降低虚拟/增强现实训练模拟器的价值。模拟器使用预测跟踪来补偿显示滞后，根据预期的头部运动准备显示更新。提出了一种基于增量四元数（DQ）的扩展卡尔曼滤波器（EKF）的头部方向预测新方法，并与四元数EKF进行了性能比较。所提出的框架对连续数据帧（DQ）之间的四元数的变化进行操作，这避免了四元数运动方程的沉重计算负担。通过EKF从DQ估计头部速度，然后用于预测未来的头部取向。我们已经测试了新的框架与捕获的头部运动数据，并将其与计算昂贵的四元数滤波器。实验结果表明，所提出的DQ方法提供了四元数方法的准确性，而没有沉重的计算负担。
Display lag in simulation environments with helmet-mounted displays causes a loss of immersion that degrades the value of virtual/augmented reality training simulators. Simulators use predictive tracking to compensate for display lag, preparing display updates based on the anticipated head motion. This paper proposes a new method for predicting head orientation using a delta quaternion (DQ)-based extended Kalman filter (EKF) and compares the performance to a quaternion EKF. The proposed framework operates on the change in quaternion between consecutive data frames (the DQ), which avoids the heavy computational burden of the quaternion motion equation. Head velocity is estimated from the DQ by an EKF and then used to predict future head orientation. We have tested the new framework with captured head motion data and compared it with the computationally expensive quaternion filter. Experimental results indicate that the proposed DQ method provides the accuracy of the quaternion method without the heavy computational burden.