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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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# README - Georgia Tech Smoothing and Mapping library
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What is GTSAM?
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--------------
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## What is GTSAM?
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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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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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## Quickstart
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In the root library folder execute:
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```
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```sh
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#!bash
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$ mkdir build
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$ cd build
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- See [INSTALL.md](INSTALL.md) for more installation information
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- Note that MKL may not provide a speedup in all cases. Make sure to benchmark your problem with and without MKL.
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GTSAM 4 Compatibility
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---------------------
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## GTSAM 4 Compatibility
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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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## The Preintegrated IMU Factor
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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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- 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]](https://ieeexplore.ieee.org/document/6092505)
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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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- 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]](https://ieeexplore.ieee.org/abstract/document/6907483)
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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. [[link]](http://www.roboticsproceedings.org/rss11/p06.pdf)
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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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## Additional Information
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There is a [`GTSAM users Google group`](https://groups.google.com/forum/#!forum/gtsam-users) for general discussion.
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<?xml version="1.0"?>
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<package format="2">
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<name>gtsam</name>
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<version>3.2.1</version>
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<version>4.0.0</version>
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<description>gtsam</description>
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<maintainer email="gtsam@lists.gatech.edu">Frank Dellaert</maintainer>
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