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

Abstract

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 https://youtu.be/lE5gjzRKWuA

arXiv commentAccepted to RSS 2019

References in corpus (3)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects
• DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

Cited by in corpus (13)

• DeepIM: Deep Iterative Matching for 6D Pose Estimation
• Tac2Pose: Tactile Object Pose Estimation from the First Touch
• Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands
• I Like to Move It: 6D Pose Estimation as an Action Decision Process
• Self-supervised 6D Object Pose Estimation for Robot Manipulation
• 6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints
• KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects
• se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
• Robotic Task Success Evaluation Under Multi-modal Non-Parametric Object Pose Uncertainty
• In-Hand Object Pose Tracking via Contact Feedback and GPU-Accelerated Robotic Simulation
• Fast Uncertainty Quantification for Deep Object Pose Estimation
• Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization
• VideoPose: Estimating 6D object pose from videos