Imroved ReadMe, now in markup
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This directory contains a number of examples that illustrate the use of GTSAM:
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SimpleRotation: a super-simple example of optimizing a single rotation according to a single prior
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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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errorStateKalmanFilter: simple 1D example of a moving target measured by a accelerometer, incl. drift-rate bias
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2D Pose SLAM
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============
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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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Pose2SLAMExample_advanced: same, but uses an Optimizer object
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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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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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PlanarSLAMExample_selfcontained: simple robotics example with all typedefs internal to this script.
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Visual SLAM
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===========
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CameraResectioning.cpp: An example of gtsam for solving the camera resectioning problem
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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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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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# GTSAM Examples
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This directory contains all GTSAM C++ examples GTSAM pertaining to SFM
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## Basic Examples:
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* **SimpleRotation**: a simple example of optimizing a single rotation according to a single prior
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* **CameraResectioning**: resection camera from some known points
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* **SFMExample**: basic structure from motion
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* **SFMExample_bal**: same, but read data from read from BAL file
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* **SelfCalibrationExample**: Do SFM while also optimizing for calibration
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## Stereo Visual Odometry Examples
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Visual odometry using a stereo rig:
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* **StereoVOExample**: basic example of stereo VO
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* **StereoVOExample_large**: larger, with a snippet of Kitti data
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## More Advanced Examples
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The following examples illustrate some concepts from Georgia Tech's research papers, listed in the references section at the end:
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* **VisualISAMExample**: uses iSAM [TRO08]
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* **VisualISAM2Example**: uses iSAM2 [IJRR12]
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* **SFMExample_SmartFactor**: uses smartFactors [ICRA14]
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## Kalman Filter Examples
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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 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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* **errorStateKalmanFilter**: simple 1D example of a moving target measured by a accelerometer, incl. drift-rate bias
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## 2D Pose SLAM
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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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* **Pose2SLAMExample_advanced**: same, but uses an Optimizer object
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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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## Planar SLAM with landmarks
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* **PlanarSLAMExample**: simple robotics example using the pre-built planar SLAM domain
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* **PlanarSLAMExample_selfcontained**: simple robotics example with all typedefs internal to this script.
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## Visual SLAM
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The directory **vSLAMexample** includes 2 simple examples using GTSAM:
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- **vSFMexample** using visual SLAM for structure-from-motion (SFM)
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- **vISAMexample** using visual SLAM and ISAM for incremental SLAM updates
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See the separate README file there.
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##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
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can be found at <http://www.di.ens.fr/~mschmidt/Software/UGM>
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##Building and Running
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To build, cd into the directory and do:
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```
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mkdir build
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cd build
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cmake ..
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```
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For each .cpp file in this directory two make targets are created, one to build the executable, and one to build and run it. For example, the file `CameraResectioning.cpp` contains simple example to resection a camera from 4 known points. You can build it using
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```
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make CameraResectioning
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```
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or build and run it immediately with
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```
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make CameraResectioning.run
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```
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which should output:
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```
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Final result:
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Values with 1 values:
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Value x1: R:
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[
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1, 0.0, 0.0,
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0.0, -1, 0.0,
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0.0, 0.0, -1,
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];
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t: [0, 0, 2]';
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```
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## References
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- [TRO08]: [iSAM: Incremental Smoothing and Mapping, Michael Kaess](http://frank.dellaert.com/pub/Kaess08tro.pdf), Michael Kaess, Ananth Ranganathan, and Frank Dellaert, IEEE Transactions on Robotics, 2008
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- [IJRR12]: [iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree](http://www.cc.gatech.edu/~dellaert/pub/Kaess12ijrr.pdf), Michael Kaess, Hordur Johannsson, Richard Roberts, Viorela Ila, John Leonard, and Frank Dellaert, International Journal of Robotics Research, 2012
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- [ICRA14]: [Eliminating Conditionally Independent Sets in Factor Graphs: A Unifying Perspective based on Smart Factors](http://frank.dellaert.com/pub/Carlone14icra.pdf), Luca Carlone, Zsolt Kira, Chris Beall, Vadim Indelman, and Frank Dellaert, IEEE International Conference on Robotics and Automation (ICRA), 2014
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