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g2o: A General Framework for Graph Optimization
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# g2o - General Graph Optimization
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<p align="center">
<picture>
<source media="(prefers-color-scheme: dark)" width="250" srcset="doc/pics/g2o-logo-dark.svg">
<source media="(prefers-color-scheme: light)" width="250" srcset="doc/pics/g2o-logo.svg">
<img alt="g2o logo" width="250" src="doc/pics/g2o-logo.svg">
</picture>
</p>
g2o is an open-source C++ framework for optimizing graph-based nonlinear error
functions. g2o has been designed to be easily extensible to a wide range of
problems and a new problem typically can be specified in a few lines of code.
The current implementation provides solutions to several variants of SLAM and
BA.
A wide range of problems in robotics as well as in computer-vision involve the
minimization of a non-linear error function that can be represented as a graph.
Typical instances are simultaneous localization and mapping (SLAM) or bundle
adjustment (BA). The overall goal in these problems is to find the
configuration of parameters or state variables that maximally explain a set of
measurements affected by Gaussian noise. g2o is an open-source C++ framework
for such nonlinear least squares problems. g2o has been designed to be easily
extensible to a wide range of problems and a new problem typically can be
specified in a few lines of code. The current implementation provides solutions
to several variants of SLAM and BA. g2o offers a performance comparable to
implementations of state-of-the-art approaches for the specific problems
(02/2011).
## Python and updated memory management
The branch [pymem](https://github.com/RainerKuemmerle/g2o/tree/pymem) contains a python wrapper and switches to smart pointer instead of RAW pointers.
It is currently experimental but PRs and improvements are welcome - as always.
See [g2o-python](https://github.com/miquelmassot/g2o-python) for the pypi release of g2o's python bindings.
## Papers Describing the Approach
Rainer Kuemmerle, Giorgio Grisetti, Hauke Strasdat,
Kurt Konolige, and Wolfram Burgard
[g2o: A General Framework for Graph Optimization](http://ais.informatik.uni-freiburg.de/publications/papers/kuemmerle11icra.pdf)
IEEE International Conference on Robotics and Automation (ICRA), 2011
## Documentation
A detailed description of how the library is structured and how to use and extend it can be found in /doc/g2o.pdf
The API documentation can be generated as described in doc/doxygen/readme.txt
## License
g2o is licensed under the BSD License. However, some libraries are available
under different license terms. See below.
The following parts are licensed under LGPL v2.1+:
- csparse_extension
The following parts are licensed under GPL3+:
- g2o_viewer
- g2o_incremental
- slam2d_g2o (example for 2D SLAM with a QGLviewer GUI)
Please note that some features of CHOLMOD (which may be used by g2o, see
libsuitesparse below) are licensed under the GPL. To avoid the GPL, you may
have to re-compile CHOLMOD without including its GPL features. The CHOLMOD
library distributed with, for example, Ubuntu or Debian includes the GPL
features. For example, the supernodal factorization that is licensed under GPL
is considered by g2o if it is available.
Within sub-folders we include software not written by us to guarantee easy compilation and iExcerpt of 8,043 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:45f4496544954c8e, topic:slam, readme:robotics, readme:slam
