README - Georgia Tech Smoothing and Mapping library
What is GTSAM?
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
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:
$ mkdir build $ cd build $ cmake .. $ make check (optional, runs unit tests) $ make install
- Boost >= 1.43 (Ubuntu:
sudo apt-get install libboost-all-dev)
- CMake >= 3.0 (Ubuntu:
sudo apt-get install cmake)
- A modern compiler, i.e., at least gcc 4.7.3 on Linux.
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)
GTSAM 4 Compatibility
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.
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.
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.
- 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.
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.
There is a
GTSAM users Google group for general discussion.
INSTALL file for more detailed installation instructions.