SegICP: Integrated deep semantic segmentation and pose estimation
Name
1703.01661.pdf
Description
Accepted version
Size
2.49 MB
Format
Adobe PDF
Checksum (MD5)
a131a4977e15197ed7428f0542a82481
Author(s)
Wong, Jay M.
Kee, Vincent
Le, Tiffany
Wagner, Syler
Mariottini, Gian-Luca
Schneider, Abraham
Hamilton, Lei
Chipalkatty, Rahul
Hebert, Mitchell
Johnson, David M.S.
Date Issued
September 2017
Publisher
IEEE
Citation
Wong, Jay M., Kee, Vincent, Le, Tiffany, Wagner, Syler, Mariottini, Gian-Luca et al. 2017. "SegICP: Integrated deep semantic segmentation and pose estimation."
Version
Author's final manuscript
Abstract
© 2017 IEEE. Recent robotic manipulation competitions have highlighted that sophisticated robots still struggle to achieve fast and reliable perception of task-relevant objects in complex, realistic scenarios. To improve these systems' perceptive speed and robustness, we present SegICP, a novel integrated solution to object recognition and pose estimation. SegICP couples convolutional neural networks and multi-hypothesis point cloud registration to achieve both robust pixel-wise semantic segmentation as well as accurate and real-time 6-DOF pose estimation for relevant objects. Our architecture achieves 1 cm position error and < 5° angle error in real time without an initial seed. We evaluate and benchmark SegICP against an annotated dataset generated by motion capture.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros.2017.8206470
