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PoseRBPF: A Rao–Blackwellized Particle Filter for 6-D Object Pose Tracking.
- Source :
-
IEEE Transactions on Robotics . Oct2021, Vol. 37 Issue 5, p1328-1342. 15p. - Publication Year :
- 2021
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *AIR filters
*ROBOTICS
*ROTATIONAL motion
*COMPUTER vision
Subjects
Details
- Language :
- English
- ISSN :
- 15523098
- Volume :
- 37
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Robotics
- Publication Type :
- Academic Journal
- Accession number :
- 153763284
- Full Text :
- https://doi.org/10.1109/TRO.2021.3056043