Time-varying, smooth trajectory estimation is of great interest to the vision community for accurate and well behaving 3D systems. In this paper, we propose a novel principal component local regression filter acting directly on the Riemannian manifold of unit dual quaternions $\mathbbD \mathbbH_1$. We use a numerically stable Lie algebra of the dual quaternions together with $\exp$ and $łog$ operators to locally linearize the 6D pose space. Unlike state of the art path smoothing methods which either operate on $SOłeft(3i̊ght)$ of rotation matrices or the hypersphere $\mathbbH_1$ of quaternions, we treat the orientation and translation jointly on the dual quaternion quadric in the 7-dimensional real projective space $\mathbbR\mathbbP^7$. We provide an outlier-robust IRLS algorithm for generic pose filtering exploiting this manifold structure. Besides our theoretical analysis, our experiments on synthetic and real data show the practical advantages of the manifold aware filtering on pose tracking and smoothing.     «

Time-varying, smooth trajectory estimation is of great interest to the vision community for accurate and well behaving 3D systems. In this paper, we propose a novel principal component local regression filter acting directly on the Riemannian manifold of unit dual quaternions $\mathbbD \mathbbH_1$. We use a numerically stable Lie algebra of the dual quaternions together with $\exp$ and $łog$ operators to locally linearize the 6D pose space. Unlike state of the art path smoothing methods which ei...     »