Robotic Depowdering for Additive Manufacturing Via Pose Tracking-Reference-Cited by-同舟云学术

Robotic Depowdering for Additive Manufacturing Via Pose Tracking

Published:2022-10 Issue:4 Volume:7 Page:10770-10777
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Liu Zhenwei¹^ORCID,Geng Junyi²^ORCID,Dai Xikai¹^ORCID,Swierzewski Tomasz¹,Shimada Kenji¹

Affiliation:

1. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

2. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

Funder

Carnegie Mellon University Manufacturing Futures Initiative

NASA University Leadership Initiative

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/9831196/09847016.pdf?arnumber=9847016

Reference30 articles.

1. ICP registration using invariant features

2. CosyPose: Consistent Multi-view Multi-object 6D Pose Estimation

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

4. PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

5. DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

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