Title:
RPN-based architecture for object detection and pose estimation using RGB-D data
RPN-based architecture for object detection and pose estimation using RGB-D data
| dc.contributor.advisor | Collins, Thomas R. | |
| dc.contributor.author | Gourdon, Remi | |
| dc.contributor.committeeMember | Kira, Zsolt | |
| dc.contributor.committeeMember | Vela, Patricio A. | |
| dc.contributor.department | Electrical and Computer Engineering | |
| dc.date.accessioned | 2019-01-16T17:22:38Z | |
| dc.date.available | 2019-01-16T17:22:38Z | |
| dc.date.created | 2018-12 | |
| dc.date.issued | 2018-12-07 | |
| dc.date.submitted | December 2018 | |
| dc.date.updated | 2019-01-16T17:22:38Z | |
| dc.description.abstract | Pose estimation is a topic of important research in the fields of robotic and computer vision, particularly for applications in autonomous transportation and robotic manipulators. This thesis presents the implementation of a pose estimation network capable of leveraging color and depth information from commercial off-the-shelf sensors, and proposes its integration as an extension to well-known architectures based on Region Proposal Networks. This work also presents an automated image and pose data collection method using an industrial robotic arm and multiple cameras, and describes its use for the acquisition of a chicken dataset as part of a research effort in poultry processing automation. The estimation results obtained on this application-specific dataset are presented. | |
| dc.description.degree | M.S. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/60747 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Robotics | |
| dc.subject | Deep learning | |
| dc.subject | Computer vision | |
| dc.title | RPN-based architecture for object detection and pose estimation using RGB-D data | |
| dc.type | Text | |
| dc.type.genre | Thesis | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | School of Electrical and Computer Engineering | |
| local.contributor.corporatename | College of Engineering | |
| relation.isOrgUnitOfPublication | 5b7adef2-447c-4270-b9fc-846bd76f80f2 | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 | |
| thesis.degree.level | Masters |