# GTSAM: Georgia Tech Smoothing and Mapping Library
**Important Note**
**As of January 2023, the `develop` branch is officially in "Pre 4.3" mode. We envision several API-breaking changes as we switch to C++17 and away from boost.**
In addition, features deprecated in 4.2 will be removed. Please use the stable [4.2 release](https://github.com/borglab/gtsam/releases/tag/4.2) if you need those features. However, most are easily converted and can be tracked down (in 4.2) by disabling the cmake flag `GTSAM_ALLOW_DEPRECATED_SINCE_V42`.
## What is GTSAM?
GTSAM is a C++ library that implements smoothing and
mapping (SAM) in robotics and vision, using Factor Graphs and Bayes
Networks as the underlying computing paradigm rather than sparse
matrices.
<!-- Main CI Badges (develop branch) -->
| CI Status | Platform | Compiler |
|:----------|:---------|:---------|
| [](https://github.com/borglab/gtsam/actions/workflows/build-python.yml?query=branch%3Adevelop) | Ubuntu 22.04, MacOS 13-14, Windows | gcc/clang,MSVC |
| [](https://github.com/borglab/gtsam/actions/workflows/vcpkg.yml?query=branch%3Adevelop) | Latest Windows/Ubuntu/Mac | - |
| [](https://github.com/borglab/gtsam/actions/workflows/build-cibw.yml?query=branch%3Adevelop) | See [pypi files](https://pypi.org/project/gtsam-develop/#files); no Windows| - |
On top of the C++ library, GTSAM includes [wrappers for MATLAB & Python](#wrappers).
## Quickstart
In the root library folder execute:
```sh
#!bash
mkdir build
cd build
cmake ..
make check # optional, runs all unit tests
make install
```
Prerequisites:
- A modern compiler:
- Mac: at least xcode-14.2
- Linux: at least clang-11 or gcc-9
- Windows: at least msvc-14.2
- [CMake](http://www.cmake.org/cmake/resources/software.html) >= 3.9
- Ubuntu: `sudo apt-get install cmake`
Optional Boost prerequisite:
Boost is now *optional*. Two cmake flags govern its behavior:
- `GTSAM_USE_BOOST_FEATURES` = `ON|OFF`: some of our timers and concept checking in the tests still depend on boost.
- `GTSAM_ENABLE_BOOST_SERIALIZATION` = `ON|OFF`: serialization of factor graphs, factors, etc still is done using boost
If one or both of these flags are `ON`, you need to install [Boost](http://www.boost.org/users/download/) >= 1.70
- Mac: `brew install boost`
- Ubuntu: `sudo apt-get install libboost-all-dev`
- Windows: We highly recommend using the [vcpkg](https://github.com/microsoft/vcpkg) package manager. For other installation methods or troubleshooting, please see the guidance in the [cmake/HandleBoost.cmake](cmake/HandleBoost.cmake) script.
Optional prerequisites - used automatically if findable by CMake:
- [Intel Threaded Building Blocks (TBB)](http://www.threadingbuildingblocks.org/) (Ubuntu: `sudo apt-get install libtbb-dev`)
- [Intel Math Kernel Library (MKL)](http://software.intel.com/en-us/intel-mkl) (Ubuntu: [installing using APT](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo))
- See [INSTALL.md](INSTALL.md) for more installation information
- Note that MKL may not provide a speedup in all cases. Make sure to benchmark your problem with and without MKL.
## GTSAM 4 Compatibility
GTSAM 4 introduces several new features, most notably Expressions and a Python toolbox. It also introduces traits, a C++ technique that allows optimizing with non-GTSAM types. That opens the door to retiring geometric types such as Point2 and Point3 to pure Eigen types, which we also do. A significant change which will not trigger a compile error is that zero-initializing of Point2 and Point3 is deprecated, so please be aware that this might render functions using their default constructor incorrect.
There is a flag `GTSAM_ALLOW_DEPRECATED_SINCE_V43` for newly deprecated methods since the 4.3 release, which is on by default, allowing anyone to just pull version 4.3 and compile.
## Wrappers
We provide support for [MATLAB](matlab/README.md) and [Python](python/README.md) wrappers for GTSAM. Please refer to the linked documents for more details.
## Citation
If you are using GTSAM for academic work, please use the following citation:
```bibtex
@software{gtsam,
author = {Frank Dellaert and GTSAM Contributors},
title = {borglab/gtsam},
month = May,
year = 2022,
publisher = {Georgia Tech Borg Lab},
version = {4.2a8},
doi = {10.5281/zenodo.5794541},
url = {https://github.com/borglab/gtsam)}}
}
```
To cite the `Factor Graphs for Robot Perception` book, please use:
```bibtex
@book{factor_graphs_for_robot_perception,
author={Frank Dellaert and Michael Kaess},
year={2017},
title={Factor Graphs for Robot Perception},
publisher={Foundations and Trends in Robotics, Vol. 6},
url={http://www.cs.cmu.edu/~kaess/pub/Dellaert17fnt.pdf}
}
```
If you are using the IMU preintegration scheme, please cite:
```bibtex
@book{imu_preintegration,
author={Christian Forster and Luca Carlone and Frank Dellaert and Davide Scaramuzza},
title={IMU preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation},
year={2015}
}
```
## The Preintegrated IMU Factor
GTSAM includes a state of the art IMU handling scheme based on
- Todd Lupton and Salah Sukkarieh, _"Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions"_, TRO, 28(1):61-76, 2012. [[link]](https://ieeexplore.ieee.org/document/6092505)
Our implementation improves on this using integration on the manifold, as detailed in
- Luca Carlone, Zsolt Kira, Chris Beall, Vadim Indelman, and Frank Dellaert, _"Eliminating conditionally independent sets in factor graphs: a unifying perspective based on smart factors"_, Int. Conf. on Robotics and Automation (ICRA), 2014. [[link]](https://ieeexplore.ieee.org/abstract/document/6907483)
- Christian Forster, Luca Carlone, Frank Dellaert, and Davide Scaramuzza, _"IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation"_, Robotics: Science and Systems (RSS), 2015. [[link]](http://www.roboticsproceedings.org/rss11/p06.pdf)
If you are using the factor in academic work, please cite the publications above.
In GTSAM 4 a new and more efficient implementation, based on integrating on the NavState tangent space and detailed in [this document](doc/ImuFactor.pdf), is enabled by default. To switch to the RSS 2015 version, set the flag `GTSAM_TANGENT_PREINTEGRATION` to OFF.
## Additional Information
There is a [GTSAM users Google group](https://groups.google.com/forum/#!forum/gtsam-users) for general discussion.
Read about important [GTSAM-Concepts](doc/GTSAM-Concepts.md) here. A primer on GTSAM Expressions,
which support (superfast) automatic differentiation,
can be found on the [GTSAM wiki on BitBucket](https://bitbucket.org/gtborg/gtsam/wiki/Home).
See the [`INSTALL`](INSTALL.md) file for more detailed installation instructions.
GTSAM is open source under the BSD license, see the [`LICENSE`](LICENSE) and [`LICENSE.BSD`](LICENSE.BSD) files.
Please see the [`examples/`](examples) directory and the [`USAGE`](USAGE.md) file for examples on how to use GTSAM.
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.md).
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"description": "# GTSAM: Georgia Tech Smoothing and Mapping Library\n\n**Important Note**\n\n**As of January 2023, the `develop` branch is officially in \"Pre 4.3\" mode. We envision several API-breaking changes as we switch to C++17 and away from boost.**\n\nIn addition, features deprecated in 4.2 will be removed. Please use the stable [4.2 release](https://github.com/borglab/gtsam/releases/tag/4.2) if you need those features. However, most are easily converted and can be tracked down (in 4.2) by disabling the cmake flag `GTSAM_ALLOW_DEPRECATED_SINCE_V42`.\n\n## What is GTSAM?\n\nGTSAM is a C++ library that implements smoothing and\nmapping (SAM) in robotics and vision, using Factor Graphs and Bayes\nNetworks as the underlying computing paradigm rather than sparse\nmatrices.\n\n\n<!-- Main CI Badges (develop branch) -->\n| CI Status | Platform | Compiler |\n|:----------|:---------|:---------|\n| [](https://github.com/borglab/gtsam/actions/workflows/build-python.yml?query=branch%3Adevelop) | Ubuntu 22.04, MacOS 13-14, Windows | gcc/clang,MSVC |\n| [](https://github.com/borglab/gtsam/actions/workflows/vcpkg.yml?query=branch%3Adevelop) | Latest Windows/Ubuntu/Mac | - |\n| [](https://github.com/borglab/gtsam/actions/workflows/build-cibw.yml?query=branch%3Adevelop) | See [pypi files](https://pypi.org/project/gtsam-develop/#files); no Windows| - |\n\nOn top of the C++ library, GTSAM includes [wrappers for MATLAB & Python](#wrappers).\n\n\n## Quickstart\n\nIn the root library folder execute:\n\n```sh\n#!bash\nmkdir build\ncd build\ncmake ..\nmake check # optional, runs all unit tests\nmake install\n```\n\nPrerequisites:\n\n- A modern compiler:\n - Mac: at least xcode-14.2\n - Linux: at least clang-11 or gcc-9\n - Windows: at least msvc-14.2\n- [CMake](http://www.cmake.org/cmake/resources/software.html) >= 3.9\n - Ubuntu: `sudo apt-get install cmake`\n\nOptional Boost prerequisite:\n\nBoost is now *optional*. Two cmake flags govern its behavior:\n - `GTSAM_USE_BOOST_FEATURES` = `ON|OFF`: some of our timers and concept checking in the tests still depend on boost.\n - `GTSAM_ENABLE_BOOST_SERIALIZATION` = `ON|OFF`: serialization of factor graphs, factors, etc still is done using boost\n\nIf one or both of these flags are `ON`, you need to install [Boost](http://www.boost.org/users/download/) >= 1.70\n - Mac: `brew install boost`\n - Ubuntu: `sudo apt-get install libboost-all-dev`\n - Windows: We highly recommend using the [vcpkg](https://github.com/microsoft/vcpkg) package manager. For other installation methods or troubleshooting, please see the guidance in the [cmake/HandleBoost.cmake](cmake/HandleBoost.cmake) script.\n\nOptional prerequisites - used automatically if findable by CMake:\n\n- [Intel Threaded Building Blocks (TBB)](http://www.threadingbuildingblocks.org/) (Ubuntu: `sudo apt-get install libtbb-dev`)\n- [Intel Math Kernel Library (MKL)](http://software.intel.com/en-us/intel-mkl) (Ubuntu: [installing using APT](https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo))\n - See [INSTALL.md](INSTALL.md) for more installation information\n - Note that MKL may not provide a speedup in all cases. Make sure to benchmark your problem with and without MKL.\n\n## GTSAM 4 Compatibility\n\nGTSAM 4 introduces several new features, most notably Expressions and a Python toolbox. It also introduces traits, a C++ technique that allows optimizing with non-GTSAM types. That opens the door to retiring geometric types such as Point2 and Point3 to pure Eigen types, which we also do. A significant change which will not trigger a compile error is that zero-initializing of Point2 and Point3 is deprecated, so please be aware that this might render functions using their default constructor incorrect.\n\n There is a flag `GTSAM_ALLOW_DEPRECATED_SINCE_V43` for newly deprecated methods since the 4.3 release, which is on by default, allowing anyone to just pull version 4.3 and compile.\n\n\n## Wrappers\n\nWe provide support for [MATLAB](matlab/README.md) and [Python](python/README.md) wrappers for GTSAM. Please refer to the linked documents for more details.\n\n## Citation\n\nIf you are using GTSAM for academic work, please use the following citation:\n\n```bibtex\n@software{gtsam,\n author = {Frank Dellaert and GTSAM Contributors},\n title = {borglab/gtsam},\n month = May,\n year = 2022,\n publisher = {Georgia Tech Borg Lab},\n version = {4.2a8},\n doi = {10.5281/zenodo.5794541},\n url = {https://github.com/borglab/gtsam)}}\n}\n```\n\nTo cite the `Factor Graphs for Robot Perception` book, please use:\n```bibtex\n@book{factor_graphs_for_robot_perception,\n author={Frank Dellaert and Michael Kaess},\n year={2017},\n title={Factor Graphs for Robot Perception},\n publisher={Foundations and Trends in Robotics, Vol. 6},\n url={http://www.cs.cmu.edu/~kaess/pub/Dellaert17fnt.pdf}\n}\n```\n\nIf you are using the IMU preintegration scheme, please cite:\n```bibtex\n@book{imu_preintegration,\n author={Christian Forster and Luca Carlone and Frank Dellaert and Davide Scaramuzza},\n title={IMU preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation},\n year={2015}\n}\n```\n\n\n## The Preintegrated IMU Factor\n\nGTSAM includes a state of the art IMU handling scheme based on\n\n- Todd Lupton and Salah Sukkarieh, _\"Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions\"_, TRO, 28(1):61-76, 2012. [[link]](https://ieeexplore.ieee.org/document/6092505)\n\nOur implementation improves on this using integration on the manifold, as detailed in\n\n- Luca Carlone, Zsolt Kira, Chris Beall, Vadim Indelman, and Frank Dellaert, _\"Eliminating conditionally independent sets in factor graphs: a unifying perspective based on smart factors\"_, Int. Conf. on Robotics and Automation (ICRA), 2014. [[link]](https://ieeexplore.ieee.org/abstract/document/6907483)\n- Christian Forster, Luca Carlone, Frank Dellaert, and Davide Scaramuzza, _\"IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation\"_, Robotics: Science and Systems (RSS), 2015. [[link]](http://www.roboticsproceedings.org/rss11/p06.pdf)\n\nIf you are using the factor in academic work, please cite the publications above.\n\nIn GTSAM 4 a new and more efficient implementation, based on integrating on the NavState tangent space and detailed in [this document](doc/ImuFactor.pdf), is enabled by default. To switch to the RSS 2015 version, set the flag `GTSAM_TANGENT_PREINTEGRATION` to OFF.\n\n\n## Additional Information\n\nThere is a [GTSAM users Google group](https://groups.google.com/forum/#!forum/gtsam-users) for general discussion.\n\nRead about important [GTSAM-Concepts](doc/GTSAM-Concepts.md) here. A primer on GTSAM Expressions,\nwhich support (superfast) automatic differentiation,\ncan be found on the [GTSAM wiki on BitBucket](https://bitbucket.org/gtborg/gtsam/wiki/Home).\n\nSee the [`INSTALL`](INSTALL.md) file for more detailed installation instructions.\n\nGTSAM is open source under the BSD license, see the [`LICENSE`](LICENSE) and [`LICENSE.BSD`](LICENSE.BSD) files.\n\nPlease see the [`examples/`](examples) directory and the [`USAGE`](USAGE.md) file for examples on how to use GTSAM.\n\nGTSAM 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.md).\n",
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