This directory contains a number of exapmples that illustrate the use of GTSAM:
SimpleRotation: a super-simple example of optimizing a single rotation according to a single prior
Kalman Filter Examples
======================
elaboratePoint2KalmanFilter: simple linear Kalman filter on a moving 2D point, but done using factor graphs
easyPoint2KalmanFilter: uses the cool generic templated Kalman filter class to do the same
fullStateKalmanFilter: simple 1D example with a full-state filter
errorStateKalmanFilter: simple 1D example of a moving target measured by a accelerometer, incl. drift-rate bias
2D Pose SLAM
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Pose2SLAMExample_easy: A 2D Pose SLAM example using the predefined typedefs in gtsam/slam/pose2SLAM.h
Pose2SLAMExample_advanced: same, but uses an Optimizer object
Pose2SLAMwSPCG_easy: solve a simple 3 by 3 grid of Pose2 SLAM problem by using easy SPCG interface
Pose2SLAMwSPCG_advanced: solve a simple 3 by 3 grid of Pose2 SLAM problem by using advanced SPCG interface
Planar SLAM with landmarks
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PlanarSLAMExample: simple robotics example using the pre-built planar SLAM domain
PlanarSLAMSelfContained_advanced: simple robotics example with all typedefs internal to this script.
Visual SLAM
===========
The directory vSLAMexample includes 2 simple examples using GTSAM:
- vSFMexample using visualSLAM in for structure-from-motion (SFM), and
- vISAMexample using visualSLAM and ISAM for incremental SLAM updates
See the separate README file there.
Undirected Graphical Models (UGM)
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The best representation for a Markov Random Field is a factor graph :-)
This is illustrated with some discrete examples from the UGM MATLAB toolbox, which
can be found at http://www.di.ens.fr/~mschmidt/Software/UGM
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