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

Xinke Deng, Arsalan Mousavian, Yu Xiang, Fei Xia, Timothy Bretl, Dieter Fox

Research output: Contribution to journalArticlepeer-review


Tracking 6-D poses of objects from videos provides rich information to a robot in performing different tasks such as manipulation and navigation. In this article, we formulate the 6-D object pose tracking problem in the Rao–Blackwellized particle filtering framework, where the 3-D rotation and the 3-D translation of an object are decoupled. This factorization allows our approach, called PoseRBPF, to efficiently estimate the 3-D translation of an object along with the full distribution over the 3-D rotation. This is achieved by discretizing the rotation space in a fine-grained manner and training an autoencoder 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 6-D pose estimation benchmarks. We open-source our implementation at https://github.com/NVlabs/PoseRBPF.

Original languageEnglish (US)
JournalIEEE Transactions on Robotics
StateAccepted/In press - 2021


  • 6-D object pose tracking
  • Computer vision
  • Pose estimation
  • Solid modeling
  • Target tracking
  • Task analysis
  • Tracking
  • Training
  • Uncertainty
  • state estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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