PoseRBPF: A Rao-Blackwellized Particle Filter for6D Object Pose Estimation

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-Blackwellizedparticle filtering framework, where the 3D rotation and the 3D translation of the object are decoupled in the estimation process.This factorization allows our approach, called PoseRBPF, to efficiently estimate an object's 3D translation 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-artresults on two 6D pose estimation benchmarks