81 lines
3.6 KiB
Plaintext
81 lines
3.6 KiB
Plaintext
USAGE - Georgia Tech Smoothing and Mapping library
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What is this file?
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This file explains how to make use of the library for common SLAM tasks,
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using a visual SLAM implementation as an example.
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Getting Started
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Install:
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Follow the installation instructions in the README file to build and
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install gtsam, as well as running tests to ensure the library is working
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properly.
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Compiling/Linking with gtsam:
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The installation creates a binary "libgtsam" at the installation prefix,
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and an include folder "gtsam". These are the only required includes, but
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the library has also been designed to make use of XML serialization through
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the Boost.serialization library, which requires the the Boost.serialization
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headers and binaries to be linked.
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If you use CMake for your project, you can use the CMake scripts in the
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cmake folder for finding GTSAM, CppUnitLite, and Wrap.
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Examples:
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To see how the library works, examine the unit tests provided.
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Overview
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---------------------------------------------------
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The GTSAM library has three primary components necessary for the construction
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of factor graph representation and optimization which users will need to
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adapt to their particular problem.
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FactorGraph:
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A factor graph contains a set of variables to solve for (i.e., robot poses,
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landmark poses, etc.) and a set of constraints between these variables, which
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make up factors.
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Values:
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Values is a single object containing labeled values for all of the
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variables. Currently, all variables are labeled with strings, but the type
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or organization of the variables can change
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Factors:
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A nonlinear factor expresses a constraint between variables, which in the
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SLAM example, is a measurement such as a visual reading on a landmark or
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odometry.
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The library is organized according to the following directory structure:
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3rdparty local copies of third party libraries - Eigen3 and CCOLAMD
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base provides some base Math and data structures, as well as test-related utilities
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geometry points, poses, tensors, etc
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inference core graphical model inference such as factor graphs, junction trees, Bayes nets, Bayes trees
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linear inference specialized to Gaussian linear case, GaussianFactorGraph etc...
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nonlinear non-linear factor graphs and non-linear optimization
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slam SLAM and visual SLAM application code
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VSLAM Example
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The visual slam example shows a full implementation of a slam system. The example contains
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derived versions of NonlinearFactor, NonlinearFactorGraph, in classes visualSLAM::ProjectionFactor,
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visualSLAM::Graph, respectively. The values for the system are stored in the generic
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Values structure. For definitions and interface, see gtsam/slam/visualSLAM.h.
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The clearest example of the use of the graph to find a solution is in
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testVSLAM. The basic process for using graphs is as follows (and can be seen in
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the test):
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- Create a NonlinearFactorGraph object (visualSLAM::Graph)
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- Add factors to the graph (note the use of Boost.shared_ptr here) (visualSLAM::ProjectionFactor)
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- Create an initial configuration (Values)
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- Create an elimination ordering of variables (this must include all variables)
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- Create and initialize a NonlinearOptimizer object (Note that this is a generic
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algorithm that does not need to be derived for a particular problem)
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- Call optimization functions with the optimizer to optimize the graph
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- Extract an updated values from the optimizer
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