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Tracking 6D poses of objects from videos provides rich information to a robotin performing different tasks such as manipulation and navigation. In thiswork, we formulate the 6D object pose tracking problem in the Rao-Blackwellizedparticle filtering framework, where the 3D rotation and the 3D translation ofan object are decoupled. This factorization allows our approach, calledPoseRBPF, to efficiently estimate the 3D translation of an object along withthe full distribution over the 3D rotation. This is achieved by discretizingthe rotation space in a fine-grained manner, and training an auto-encodernetwork to construct a codebook of feature embeddings for the discretizedrotations. As a result, PoseRBPF can track objects with arbitrary symmetrieswhile still maintaining adequate posterior distributions. Our approach achievesstate-of-the-art results on two 6D pose estimation benchmarks. A video showingthe experiments can be found at https://youtu.be/lE5gjzRKWuA

PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking