GTSAM is a library of C++ classes that implement 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 a MATLAB interface (enable GTSAM_INSTALL_MATLAB_TOOLBOX in CMake to build it). A Python interface is under development.
In the root library folder execute:
#!bash $ mkdir build $ cd build $ cmake .. $ make check (optional, runs unit tests) $ make install
sudo apt-get install libboost-all-dev)
sudo apt-get install cmake)
Optional prerequisites - used automatically if findable by CMake:
sudo apt-get install libtbb-dev)
GTSAM 4 will introduce several new features, most notably Expressions and a python toolbox. We will also deprecate some legacy functionality and wrongly named methods, but by default the flag GTSAM_ALLOW_DEPRECATED_SINCE_V4 is enabled, allowing anyone to just pull V4 and compile. To build the python toolbox, however, you will have to explicitly disable that flag.
Also, GTSAM 4 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 will also do. A significant change which will not trigger a compile error is that zero-initializing of Point2 and Point3 will be deprecated, so please be aware that this might render functions using their default constructor incorrect.
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 docs/ImuFactor.pdf, is enabled by default. To switch to the RSS 2015 version, set the flag GTSAM_TANGENT_PREINTEGRATION to OFF.
Read about important
INSTALL file for more detailed installation instructions.