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GTSAM: Georgia Tech Smoothing and Mapping Library

C++ API Docs

GTSAM manifold optimization workflow: build a factor graph, linearize and solve in tangent spaces, retract to manifolds, and iterate to convergence.

Development branch

The develop branch contains changes intended for the next GTSAM release and may include API changes. For production use, choose the latest stable version from the GTSAM releases. Current development builds require C++17; Boost support is optional and controlled by CMake options.

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.

CI StatusPlatformCompiler
Python CIUbuntu 22.04, macOS 15, Windows 2022GCC/Clang/MSVC
vcpkgLatest Windows/Ubuntu/Mac-
Build Wheels for DevelopSee pypi files; no Windows-

On top of the C++ library, GTSAM includes wrappers for MATLAB & Python.

Documentation

Quickstart

In the root library folder execute:

cmake -S . -B build
cmake --build build --target check  # optional, runs all unit tests
cmake --build build --target install

Prerequisites:

Older C++17-capable toolchains may work but are not continuously tested.

Optional Boost prerequisite:

Boost is optional. Two CMake flags govern its use:

Both options default to ON for ordinary CMake builds and OFF inside ROS 2 builds (colcon, or an install prefix under /opt/ros/). If either option is ON, install Boost 1.70 or newer:

Optional prerequisites:

GTSAM 4 Compatibility

GTSAM 4 introduced Expressions, a Python toolbox, and traits that allow optimization with non-GTSAM types. Point2 and Point3 are Eigen vector aliases; their default constructors do not initialize their coefficients, so initialize them explicitly before use.

GTSAM_ALLOW_DEPRECATED_SINCE_V43 controls APIs deprecated for the GTSAM 4.3 release and defaults to ON. Disable it while migrating code to identify APIs scheduled for removal after 4.3.

Wrappers

We provide support for MATLAB and Python 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:

@software{Dellaert26zenodo_GTSAM_4_3,
  author       = {Dellaert, Frank and GTSAM Contributors},
  title        = {GTSAM 4.3.0},
  month        = sep,
  year         = 2026,
  publisher    = {Zenodo},
  version      = {4.3.0},
  doi          = {10.5281/zenodo.22866773},
  url          = {https://doi.org/10.5281/zenodo.22866773},
}

To cite the Factor Graphs for Robot Perception book, please use:

@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:

@inproceedings{Forster-RSS-15,
    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},
    booktitle = {Proceedings of Robotics: Science and Systems},
    year      = {2015},
    address   = {Rome, Italy},
    month     = {July},
    doi       = {10.15607/RSS.2015.XI.006}
}

The Preintegrated IMU Factor

GTSAM includes a state of the art IMU handling scheme based on

Our implementation improves on this using integration on the manifold, as detailed in

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, 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 for general discussion.

Read about important GTSAM concepts. A primer on GTSAM Expressions, which support efficient automatic differentiation, is available in Expressions.

See the INSTALL file for more detailed installation instructions. Our CI/CD process is detailed in workflows.md.

GTSAM is open source under the BSD license, see the LICENSE and LICENSE.BSD files.

Please see the examples/ directory and the USAGE file for examples on how to use GTSAM.

GTSAM was developed in the lab of Frank Dellaert at the Georgia Institute of Technology, with the help of many contributors over the years, see THANKS.

GTSAM Docs
Installation Guide