Added ImuFactor things in readme
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README.md
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README.md
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README - Georgia Tech Smoothing and Mapping library
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===================================================
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What is GTSAM?
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--------------
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GTSAM is a library of C++ classes that implement smoothing and
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mapping (SAM) in robotics and vision, using factor graphs and Bayes
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networks as the underlying computing paradigm rather than sparse
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matrices.
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On top of the C++ library, GTSAM includes a MATLAB interface (enable
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GTSAM_INSTALL_MATLAB_TOOLBOX in CMake to build it). A Python interface
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is under development.
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Quickstart
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----------
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In the root library folder execute:
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```
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#!bash
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$ mkdir build
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$ cd build
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$ cmake ..
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$ make check (optional, runs unit tests)
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$ make install
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```
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Prerequisites:
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- [Boost](http://www.boost.org/users/download/) >= 1.43 (Ubuntu: `sudo apt-get install libboost-all-dev`)
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- [CMake](http://www.cmake.org/cmake/resources/software.html) >= 2.6 (Ubuntu: `sudo apt-get install cmake`)
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- A modern compiler, i.e., at least gcc 4.7.3 on Linux.
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Optional prerequisites - used automatically if findable by CMake:
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- [Intel Threaded Building Blocks (TBB)](http://www.threadingbuildingblocks.org/) (Ubuntu: `sudo apt-get install libtbb-dev`)
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- [Intel Math Kernel Library (MKL)](http://software.intel.com/en-us/intel-mkl)
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GTSAM 4 Compatibility
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---------------------
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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.
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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.
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Additional Information
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----------------------
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Read about important [`GTSAM-Concepts`](GTSAM-Concepts.md) here.
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See the [`INSTALL`](INSTALL) file for more detailed installation instructions.
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GTSAM is open source under the BSD license, see the [`LICENSE`](LICENSE) and [`LICENSE.BSD`](LICENSE.BSD) files.
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Please see the [`examples/`](examples) directory and the [`USAGE`](USAGE.md) file for examples on how to use GTSAM.
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README - Georgia Tech Smoothing and Mapping library
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===================================================
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What is GTSAM?
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--------------
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GTSAM is a library of C++ classes that implement smoothing and
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mapping (SAM) in robotics and vision, using factor graphs and Bayes
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networks as the underlying computing paradigm rather than sparse
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matrices.
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On top of the C++ library, GTSAM includes a MATLAB interface (enable
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GTSAM_INSTALL_MATLAB_TOOLBOX in CMake to build it). A Python interface
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is under development.
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Quickstart
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----------
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In the root library folder execute:
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```
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#!bash
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$ mkdir build
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$ cd build
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$ cmake ..
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$ make check (optional, runs unit tests)
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$ make install
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```
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Prerequisites:
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- [Boost](http://www.boost.org/users/download/) >= 1.43 (Ubuntu: `sudo apt-get install libboost-all-dev`)
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- [CMake](http://www.cmake.org/cmake/resources/software.html) >= 2.6 (Ubuntu: `sudo apt-get install cmake`)
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- A modern compiler, i.e., at least gcc 4.7.3 on Linux.
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Optional prerequisites - used automatically if findable by CMake:
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- [Intel Threaded Building Blocks (TBB)](http://www.threadingbuildingblocks.org/) (Ubuntu: `sudo apt-get install libtbb-dev`)
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- [Intel Math Kernel Library (MKL)](http://software.intel.com/en-us/intel-mkl)
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GTSAM 4 Compatibility
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---------------------
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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.
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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.
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The Preintegrated IMU Factor
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----------------------------
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GTSAM includes a state of the art IMU handling scheme based on
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- 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.
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Our implementation improves on this using integration on the manifold, as detailed in
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- 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.
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- 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.
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If you are using the factor in academic work, please cite the publications above.
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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.
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Additional Information
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----------------------
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Read about important [`GTSAM-Concepts`](GTSAM-Concepts.md) here.
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See the [`INSTALL`](INSTALL) file for more detailed installation instructions.
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GTSAM is open source under the BSD license, see the [`LICENSE`](LICENSE) and [`LICENSE.BSD`](LICENSE.BSD) files.
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Please see the [`examples/`](examples) directory and the [`USAGE`](USAGE.md) file for examples on how to use GTSAM.
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GTSAM was developed in the lab of [Frank Dellaert](http://www.cc.gatech.edu/~dellaert) at the [Georgia Institute of Technology](http://www.gatech.edu), with the help of many contributors over the years, see [THANKS](THANKS).
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