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Important Note

Thedevelop 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 stable4.2 release if you need those features. However, most are easily converted and can be tracked down (in 4.2) by disabling the cmake flagGTSAM_ALLOW_DEPRECATED_SINCE_V42.

GTSAM is a C++ library that implements smoothing andmapping (SAM) in robotics and vision, using Factor Graphs and BayesNetworks as the underlying computing paradigm rather than sparsematrices.

CI Status	Platform	Compiler
	Ubuntu 22.04, MacOS 13-14, Windows	gcc/clang,MSVC
	Latest Windows/Ubuntu/Mac	-
	Seepypi files; no Windows	-

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

• C++ API Docs:https://gtsam.org/doxygen/
• Python API Docs:https://borglab.github.io/gtsam/

In the root library folder execute:

#!bashmkdir buildcd buildcmake ..make check# optional, runs all unit testsmake 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 nowoptional. 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 areON, you need to installBoost >= 1.70- Mac:brew install boost- Ubuntu:sudo apt-get install libboost-all-dev- Windows: We highly recommend using thevcpkg package manager. For other installation methods or troubleshooting, please see the guidance in thecmake/HandleBoost.cmake script.

Optional prerequisites - used automatically if findable by CMake:

• Intel Threaded Building Blocks (TBB) (Ubuntu:sudo apt-get install libtbb-dev)
• Intel Math Kernel Library (MKL) (Ubuntu:installing using APT)
  • SeeINSTALL.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 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 flagGTSAM_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.

We provide support forMATLAB andPython wrappers for GTSAM. Please refer to the linked documents for more details.

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 theFactor 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}}

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 inthis document, is enabled by default. To switch to the RSS 2015 version, set the flagGTSAM_TANGENT_PREINTEGRATION to OFF.

There is aGTSAM users Google group for general discussion.

Read about importantGTSAM-Concepts here. A primer on GTSAM Expressions,which support (superfast) automatic differentiation,can be found on theGTSAM wiki on BitBucket.

See theINSTALL file for more detailed installation instructions. Our CI/CD process is detailed inworkflows.md.

GTSAM is open source under the BSD license, see theLICENSE andLICENSE.BSD files.

Please see theexamples/ directory and theUSAGE file for examples on how to use GTSAM.

GTSAM was developed in the lab ofFrank Dellaert at theGeorgia Institute of Technology, with the help of many contributors over the years, seeTHANKS.