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GTSAM: Georgia Tech Smoothing and Mapping Library

C++ API Docs

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 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.

CI Status Platform Compiler
Python CI Ubuntu 22.04, MacOS 13-14, Windows gcc/clang,MSVC
vcpkg Latest Windows/Ubuntu/Mac -
Build Wheels for Develop See pypi files; no Windows -

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

Documentation

Quickstart

In the root library folder execute:

#!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 >= 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 >= 1.70 - Mac: brew install boost - Ubuntu: sudo apt-get install libboost-all-dev - Windows: We highly recommend using the vcpkg package manager. For other installation methods or troubleshooting, please see the guidance in the cmake/HandleBoost.cmake script.

Optional prerequisites - used automatically if findable by CMake:

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 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{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:

@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:

@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]

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]
  • 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]

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 here. A primer on GTSAM Expressions, which support (superfast) automatic differentiation, can be found on the GTSAM wiki on BitBucket.

See the INSTALL file for more detailed installation instructions.

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.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Stable release available (4.2.2)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for dellaert from gravatar.com dellaert Avatar for yambati3 from gravatar.com yambati3

Credits

Author: Frank Dellaert et. al.

License

BSD License (Simplified BSD license)

Tags

slam sam robotics localization mapping optimization

Classifiers

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