Title:
Factor Graphs for Flexible Inference in Robotics and Vision
Factor Graphs for Flexible Inference in Robotics and Vision
| dc.contributor.author | Dellaert, Frank | |
| dc.contributor.corporatename | Georgia Institute of Technology. Institute for Robotics and Intelligent Machines | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Interactive Computing | en_US |
| dc.date.accessioned | 2019-01-15T19:01:58Z | |
| dc.date.available | 2019-01-15T19:01:58Z | |
| dc.date.issued | 2019-01-09 | |
| dc.description | Presented on January 9, 2019 from 12:15 p.m.-1:15 p.m. in the Marcus Nanotechnology Building, Rooms 1116-1118, Georgia Tech. | en_US |
| dc.description | Frank Dellaert is a professor in the School of Interactive Computing at the Georgia Institute of Technology. While on leave from Tech in 2016-2018, he served as a technical project lead at Facebook Reality Labs. Before that, he completed a stint as chief scientist at Skydio, a startup founded by MIT grads to create intuitive interfaces for micro-aerial vehicles. Dellaert’s research interests lie in the overlap of robotics and computer vision, and he is particularly interested in graphical model techniques to solve large-scale problems in mapping and 3D reconstruction. The GTSAM toolbox embodies many of the ideas his research group has worked on in the past few years and is available for download at https://bitbucket.org/gtborg/gtsam. | en_US |
| dc.description | Runtime: 59:54 minutes | en_US |
| dc.description.abstract | In robotics and computer vision, Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SFM) are important and closely related problems in robotics and vision. I will review how SLAM, SFM and other problems in robotics and vision can be posed in terms of factor graphs, which provide a graphical language in which to develop and collaborate on such problems. The theme of the talk will be to emphasize the advantages and intuition that come with that. I will show how using these insights we have developed both batch and incremental algorithms defined on graphs in the SLAM/SFM domain, as well as more sophisticated approaches to trajectory optimization. Many of these ideas are embodied in the Skydio R1, a commercially available, fully autonomous drone I helped develop at Skydio, a San Francisco Bay area startup. | en_US |
| dc.format.extent | 59:54 minutes | |
| dc.identifier.uri | http://hdl.handle.net/1853/60646 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.relation.ispartofseries | IRIM Seminar Series | |
| dc.subject | AI | en_US |
| dc.subject | Drones | en_US |
| dc.subject | Robotics | en_US |
| dc.title | Factor Graphs for Flexible Inference in Robotics and Vision | en_US |
| dc.type | Moving Image | |
| dc.type.genre | Lecture | |
| dspace.entity.type | Publication | |
| local.contributor.author | Dellaert, Frank | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| local.relation.ispartofseries | IRIM Seminar Series | |
| relation.isAuthorOfPublication | dac80074-d9d8-4358-b6eb-397d95bdc868 | |
| relation.isOrgUnitOfPublication | 66259949-abfd-45c2-9dcc-5a6f2c013bcf | |
| relation.isSeriesOfPublication | 9bcc24f0-cb07-4df8-9acb-94b7b80c1e46 |
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