2012-06-04 03:26:28 +08:00
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This directory contains a number of examples that illustrate the use of GTSAM:
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2011-08-19 21:11:04 +08:00
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SimpleRotation: a super-simple example of optimizing a single rotation according to a single prior
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2011-08-20 05:27:47 +08:00
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Kalman Filter Examples
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======================
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elaboratePoint2KalmanFilter: simple linear Kalman filter on a moving 2D point, but done using factor graphs
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easyPoint2KalmanFilter: uses the cool generic templated Kalman filter class to do the same
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fullStateKalmanFilter: simple 1D example with a full-state filter
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2011-08-27 20:28:47 +08:00
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errorStateKalmanFilter: simple 1D example of a moving target measured by a accelerometer, incl. drift-rate bias
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2011-08-20 05:27:47 +08:00
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2011-08-19 21:11:04 +08:00
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2D Pose SLAM
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============
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2012-06-04 03:52:35 +08:00
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LocalizationExample.cpp: modeling robot motion
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LocalizationExample2.cpp: example with GPS like measurements
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Pose2SLAMExample: A 2D Pose SLAM example using the predefined typedefs in gtsam/slam/pose2SLAM.h
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2011-08-19 21:11:04 +08:00
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Pose2SLAMExample_advanced: same, but uses an Optimizer object
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2012-06-04 03:52:35 +08:00
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Pose2SLAMwSPCG: solve a simple 3 by 3 grid of Pose2 SLAM problem by using easy SPCG interface
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2011-08-19 21:11:04 +08:00
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Planar SLAM with landmarks
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==========================
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PlanarSLAMExample: simple robotics example using the pre-built planar SLAM domain
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2012-06-04 04:11:40 +08:00
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PlanarSLAMExample_selfcontained: simple robotics example with all typedefs internal to this script.
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2011-08-19 21:11:04 +08:00
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Visual SLAM
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===========
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2012-06-04 03:52:35 +08:00
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CameraResectioning.cpp: An example of gtsam for solving the camera resectioning problem
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2011-08-19 21:11:04 +08:00
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The directory vSLAMexample includes 2 simple examples using GTSAM:
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- vSFMexample using visualSLAM in for structure-from-motion (SFM), and
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- vISAMexample using visualSLAM and ISAM for incremental SLAM updates
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2012-05-03 13:09:22 +08:00
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See the separate README file there.
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Undirected Graphical Models (UGM)
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=================================
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The best representation for a Markov Random Field is a factor graph :-)
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This is illustrated with some discrete examples from the UGM MATLAB toolbox, which
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can be found at http://www.di.ens.fr/~mschmidt/Software/UGM
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