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Georgia Tech Smoothing And Mapping library

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# README - Georgia Tech Smoothing and Mapping Library

Important Note

As of August 1 2020, the ‘develop‘ branch is officially in “Pre 4.1” mode, and features deprecated in 4.0 have been removed. Please use the last [4.0.3 release]( if you need those features.

However, most are easily converted and can be tracked down (in 4.0.3) by disabling the cmake flag ‘GTSAM_ALLOW_DEPRECATED_SINCE_V4‘.

## 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.

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 unit tests) $ make install ‘‘‘


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.

GTSAM 4 also deprecated some legacy functionality and wrongly named methods. If you are on a 4.0.X release, you can define the flag GTSAM_ALLOW_DEPRECATED_SINCE_V4 to use the deprecated methods.

GTSAM 4.1 added a new pybind wrapper, and removed the deprecated functionality. There is a flag GTSAM_ALLOW_DEPRECATED_SINCE_V41 for newly deprecated methods since the 4.1 release, which is on by default, allowing anyone to just pull version 4.1 and compile.

## Wrappers

We provide support for [MATLAB](matlab/ and [Python](cython/ wrappers for GTSAM. Please refer to the linked documents for more details.

## 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](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‘](!forum/gtsam-users) 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‘](LICENSE) and [‘LICENSE.BSD‘](LICENSE.BSD) files.

Please see the [‘examples/‘](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](

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