Unscented Dual Quaternion Particle Filter for SE(3) Estimation
Unscented Dual Quaternion Particle Filter for SE(3) Estimation
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DOI：
10.1109/lcsys.2020.3005066
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发表时间：
2021-04-01
影响因子：
3
通讯作者：
Hanebeck, Uwe D.
中科院分区：
文献类型：
--
作者：
Li, Kailai;Pfaff, Florian;Hanebeck, Uwe D.
提出了一种新的对偶四元数滤波器，用于刚体运动的递推估计。基于顺序蒙特卡罗格式，将粒子部署在单位对偶四元数流形上。这允许对SE(3)组基础上的任意分布进行非参数建模。通过一种新型的双四元数无气味卡尔曼滤波器（DQ-UKF）估计了重要性采样的建议分布。它适应于流形几何结构，并将先验粒子驱动到流形上的高似然区域。由此产生的无气味对偶四元数粒子滤波器（U-DQPF）结合了最近观察到的证据，大大提高了非线性姿态估计任务的粒子效率。与普通粒子滤波和其他基于参数模型的对偶四元数滤波方案相比，所提出的U-DQPF在非线性SE(3)估计中表现出优越的性能。
We present a novel dual quaternion filter for recursive estimation of rigid body motions. Based on the sequential Monte Carlo scheme, particles are deployed on the manifold of unit dual quaternions. This allows non-parametric modeling of arbitrary distributions underlying on the SE(3) group. The proposal distribution for importance sampling is estimated particle-wise by a novel dual quaternion unscented Kalman filter (DQ-UKF). It is adapted to the manifold geometric structure and drives the prior particles towards high-likelihood regions on the manifold. The resultant unscented dual quaternion particle filter (U-DQPF) incorporates the most recently observed evidence, raising the particle efficiency considerably for nonlinear pose estimation tasks. Compared with ordinary particle filters and other parametric model-based dual quaternion filtering schemes, the proposed U-DQPF shows superior performance in nonlinear SE(3) estimation.