Robotic object pose estimation with deep neural networks
Name
1078151125-MIT.pdf
Description
Full printable version
Size
2.73 MB
Format
Adobe PDF
Checksum (MD5)
31e499d7591a2cc8cef2d5dda80abe2d
Author(s)
Wu, Jimmy, M. Eng. Massachusetts Institute of Technology
Advisor(s)
Antonio Torralba.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
In this work, we introduce pose interpreter networks for 6-DoF object pose estimation. In contrast to other CNN-based approaches to pose estimation that require expensively-annotated object pose data, our pose interpreter network is trained entirely on synthetic data. We use object masks as an intermediate representation to bridge real and synthetic. We show that when combined with a segmentation model trained on RGB images, our synthetically-trained pose interpreter network is able to generalize to real data. Our end-to-end system for object pose estimation runs in real-time (20 Hz) on live RGB data, without using depth information or ICP refinement.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 39-45).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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