用方向统计方法提升姿态跟踪精度，减少误差。
Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter
- 融合方向统计的集成卡尔曼滤波器，处理姿态不确定性。
- 在仿真与数字孪生头追踪中，误差显著低于仅用测量值。
- 适合需要高精度姿态跟踪的应用场景。
本文提出一种基于集合的卡尔曼滤波方法——集成方向卡尔曼滤波器（EnDKF），用于姿态跟踪，通过方向统计思想联合估计物体的位置与姿态。EnDKF采用单位四元数表示姿态，突破传统卡尔曼滤波对均值和协方差的假设，更准确刻画方向性不确定性。在恒定速度-恒定角速度的仿真系统以及基于FoundationPose算法的数字孪生头追踪场景中，实验表明该方法相比仅使用原始测量值显著降低了误差。
原文摘要 · Abstract (English)
This paper introduces the ensemble directional Kalman filter (EnDKF), an ensemble-based Kalman filtering approach for pose tracking that jointly estimates an object's position and attitude using ideas from directional statistics. The EnDKF integrates a unit-quaternion attitude representation to move beyond canonical Kalman filter mean and covariance assumptions that poorly capture directional uncertainty. Experiments on a synthetic constant-velocity constant-angular-velocity system and a digital-twin head-tracking scenario using the FoundationPose algorithm demonstrate a significant reduction in error as opposed to merely using measurements.
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