Tracking 6D poses of objects from videos provides rich information to a robot
in performing different tasks such as manipulation and navigation. In this
work, we formulate the 6D object pose tracking problem in the Rao-Blackwellized
particle filtering framework, where the 3D rotation and the 3D translation of
an object are decoupled. This factorization allows our approach, called
PoseRBPF, to efficiently estimate the 3D translation of an object along with
the full distribution over the 3D rotation. This is achieved by discretizing
the rotation space in a fine-grained manner, and training an auto-encoder
network to construct a codebook of feature embeddings for the discretized
rotations. As a result, PoseRBPF can track objects with arbitrary symmetries
while still maintaining adequate posterior distributions. Our approach achieves
state-of-the-art results on two 6D pose estimation benchmarks. A video showing
the experiments can be found at this https URL

该文提出了一种基于 Rao-Blackwellized 粒子滤波方法的 6D 目标姿态跟踪算法 PoseRBPF，该算法通过旋转和平移的分解实现了对对象 3D Translation 的有效估计，并且在对旋转空间精细离散方式下进行了特征嵌入的自编码神经网络的训练以获得特征嵌入代码本，以实现对任意对称对象的跟踪并仍保持足够的后验分布。该方法在两个 6D 姿态估计基准测试上实现了最先进的结果。