Title:
Factor Graphs and GTSAM: A Hands-on Introduction
Factor Graphs and GTSAM: A Hands-on Introduction
| dc.contributor.author | Dellaert, Frank | |
| dc.contributor.corporatename | Georgia Institute of Technology. Center for Robotics and Intelligent Machines | |
| dc.date.accessioned | 2012-11-01T15:35:07Z | |
| dc.date.available | 2012-11-01T15:35:07Z | |
| dc.date.issued | 2012-09 | |
| dc.description.abstract | In this document I provide a hands-on introduction to both factor graphs and GTSAM. Factor graphs are graphical models (Koller and Friedman, 2009) that are well suited to modeling complex estimation problems, such as Simultaneous Localization and Mapping (SLAM) or Structure from Motion (SFM). You might be familiar with another often used graphical model, Bayes networks, which are directed acyclic graphs. A factor graph, however, is a bipartite graph consisting of factors connected to variables. The variables represent the unknown random variables in the estimation problem, whereas the factors represent probabilistic information on those variables, derived from measurements or prior knowledge. In the following sections I will show many examples from both robotics and vision. The GTSAM toolbox (GTSAM stands for “Georgia Tech Smoothing and Mapping”) toolbox is a BSD-licensed C++ library based on factor graphs, developed at the Georgia Institute of Technology by myself, many of my students, and collaborators. It provides state of the art solutions to the SLAM and SFM problems, but can also be used to model and solve both simpler and more complex estimation problems. It also provides a MATLAB interface which allows for rapid prototype development, visualization, and user interaction. GTSAM exploits sparsity to be computationally efficient. Typically measurements only provide information on the relationship between a handful of variables, and hence the resulting factor graph will be sparsely connected. This is exploited by the algorithms implemented in GTSAM to reduce computational complexity. Even when graphs are too dense to be handled efficiently by direct methods, GTSAM provides iterative methods that are quite efficient regardless. You can download the latest version of GTSAM at http://tinyurl.com/gtsam. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/45226 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.relation.ispartofseries | CP&R Technical Report ; GT-RIM-CP&R-2012-002 | en_US |
| dc.subject | Factor graph | en_US |
| dc.subject | Georgia Tech smoothing and mapping | en_US |
| dc.subject | Simultaneous localization and mapping | en_US |
| dc.subject | Structure from motion | en_US |
| dc.subject | Visual odometry | en_US |
| dc.title | Factor Graphs and GTSAM: A Hands-on Introduction | en_US |
| dc.type | Text | |
| dc.type.genre | Technical Report | |
| dspace.entity.type | Publication | |
| local.contributor.author | Dellaert, Frank | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| relation.isAuthorOfPublication | dac80074-d9d8-4358-b6eb-397d95bdc868 | |
| relation.isOrgUnitOfPublication | 66259949-abfd-45c2-9dcc-5a6f2c013bcf |
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