Fixed some top-level files
dellaert
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README.md
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README.md
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@ -40,10 +40,12 @@ Optional prerequisites - used automatically if findable by CMake:
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Additional Information
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----------------------
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Read about important [`GTSAM-Concepts`] here.
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Read about important [`GTSAM-Concepts`](GTSAM-Concepts.md) here.
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See the [`INSTALL`] file for more detailed installation instructions.
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See the [`INSTALL`](INSTALL) file for more detailed installation instructions.
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GTSAM is open source under the BSD license, see the [`LICENSE`](https://bitbucket.org/gtborg/gtsam/src/develop/LICENSE) and [`LICENSE.BSD`](https://bitbucket.org/gtborg/gtsam/src/develop/LICENSE.BSD) files.
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GTSAM is open source under the BSD license, see the [`LICENSE`](LICENSE) and [`LICENSE.BSD`](LICENSE.BSD) files.
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Please see the [`examples/`](examples) directory and the [`USAGE`] file for examples on how to use GTSAM.
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Please see the [`examples/`](examples) directory and the [`USAGE`](USAGE.md) file for examples on how to use GTSAM.
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GTSAM was developed in the lab of [Frank Dellaert](http://www.cc.gatech.edu/~dellaert) at the [Georgia Institute of Technology](http://www.gatech.edu), with the help of many contributors over the years, see [THANKS](THANKS).
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21
THANKS
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THANKS
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@ -1,20 +1,39 @@
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GTSAM was made possible by the efforts of many collaborators at Georgia Tech
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Sungtae An
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Doru Balcan
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Chris Beall
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Luca Carlone
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Alex Cunningham
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Jing Dong
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Alireza Fathi
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Eohan George
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Alex Hagiopol
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Viorela Ila
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Yong-Dian Jian
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Michael Kaess
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Zhaoyang Lv
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Andrew Melim
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Kai Ni
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Carlos Nieto
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Duy-Nguyen
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Duy-Nguyen Ta
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Manohar Paluri
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Christian Potthast
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Richard Roberts
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Grant Schindler
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Natesh Srinivasan
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Thomas Schneider
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Alex Trevor
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at ETH, Zurich
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Paul Furgale
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Mike Bosse
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Hannes Sommer
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at Uni Zurich:
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Christian Forster
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Many thanks for your hard work!!!!
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Frank Dellaert
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@ -1,6 +1,5 @@
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USAGE - Georgia Tech Smoothing and Mapping library
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---------------------------------------------------
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===================================
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What is this file?
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This file explains how to make use of the library for common SLAM tasks,
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@ -34,18 +33,12 @@ The GTSAM library has three primary components necessary for the construction
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of factor graph representation and optimization which users will need to
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adapt to their particular problem.
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FactorGraph:
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A factor graph contains a set of variables to solve for (i.e., robot poses,
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landmark poses, etc.) and a set of constraints between these variables, which
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make up factors.
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Values:
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Values is a single object containing labeled values for all of the
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variables. Currently, all variables are labeled with strings, but the type
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or organization of the variables can change
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Factors:
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A nonlinear factor expresses a constraint between variables, which in the
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SLAM example, is a measurement such as a visual reading on a landmark or
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odometry.
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* FactorGraph:
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A factor graph contains a set of variables to solve for (i.e., robot poses, landmark poses, etc.) and a set of constraints between these variables, which make up factors.
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* Values:
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Values is a single object containing labeled values for all of the variables. Currently, all variables are labeled with strings, but the type or organization of the variables can change
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* Factors:
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A nonlinear factor expresses a constraint between variables, which in the SLAM example, is a measurement such as a visual reading on a landmark or odometry.
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The library is organized according to the following directory structure:
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@ -59,23 +52,3 @@ The library is organized according to the following directory structure:
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VSLAM Example
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---------------------------------------------------
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The visual slam example shows a full implementation of a slam system. The example contains
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derived versions of NonlinearFactor, NonlinearFactorGraph, in classes visualSLAM::ProjectionFactor,
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visualSLAM::Graph, respectively. The values for the system are stored in the generic
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Values structure. For definitions and interface, see gtsam/slam/visualSLAM.h.
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The clearest example of the use of the graph to find a solution is in
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testVSLAM. The basic process for using graphs is as follows (and can be seen in
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the test):
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- Create a NonlinearFactorGraph object (visualSLAM::Graph)
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- Add factors to the graph (note the use of Boost.shared_ptr here) (visualSLAM::ProjectionFactor)
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- Create an initial configuration (Values)
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- Create an elimination ordering of variables (this must include all variables)
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- Create and initialize a NonlinearOptimizer object (Note that this is a generic
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algorithm that does not need to be derived for a particular problem)
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- Call optimization functions with the optimizer to optimize the graph
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- Extract an updated values from the optimizer
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