Computational Perception & Robotics Technical Reports
http://hdl.handle.net/1853/45224
2021-06-21T16:47:42ZSupplementary Material to: IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation
http://hdl.handle.net/1853/53653
Supplementary Material to: IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation
Forster, Christian; Carlone, Luca; Dellaert, Frank; Scaramuzza, Davide
This report provides additional derivations and implementation details to support the paper [4]. Therefore,
it should not be considered a self-contained document, but rather regarded as an appendix of [4], and cited as: “C. Forster, L. Carlone, F. Dellaert, and D. Scaramuzza, IMU preintegration on manifold for efficient
visual-inertial maximum-a-posteriori estimation, (supplementary material, GT-IRIM-CP&R-2015-001),
In Robotics: Science and Systems (RSS), 2015.”
2015-05-30T00:00:00ZForster, ChristianCarlone, LucaDellaert, FrankScaramuzza, DavideAnalytic Inverse Kinematics for the Universal Robots UR-5/UR-10 Arms
http://hdl.handle.net/1853/50782
Analytic Inverse Kinematics for the Universal Robots UR-5/UR-10 Arms
Hawkins, Kelsey P.
2013-12-07T00:00:00ZHawkins, Kelsey P.Rapid Loop Updates
http://hdl.handle.net/1853/45227
Rapid Loop Updates
Indelman, Vadim; Dellaert, Frank
2012-09-11T00:00:00ZIndelman, VadimDellaert, FrankFactor Graphs and GTSAM: A Hands-on Introduction
http://hdl.handle.net/1853/45226
Factor Graphs and GTSAM: A Hands-on Introduction
Dellaert, Frank
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.
2012-09-01T00:00:00ZDellaert, FrankIn 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.