Orocos Bayesian Filtering Library Fork
This library is based on Orocos BFL 0.8.0 and will be extended and used for implementing BERDY's framework.
// This library is free software; you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation; either version 2 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
//
This library encoporates ideas from several available software libraries:
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Scene (Andrew Davison). See http://www.robots.ox.ac.uk/~ajd/Scene/
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Bayes++ (from ACFR). See http://www.acfr.usyd.edu.au/technology/bayesianfilter/Bayes++.htm
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The CES programming library (Sebastian Thrun). See http://www-2.cs.cmu.edu/afs/cs.cmu.edu/user/thrun/public_html/papers/thrun.ces-tr.html
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Our own research with Bayesian methods for compliant motion problems http://www.mech.kuleuven.be/pma/research/manip/default_en.phtml
It's most important features are:
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Released under the GNU LGPL licence
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Wrapper around matrix and RNG libraries, so you can use your own favourite matrix library. At 2004/03/02 wrappers exist for
- The matrix/RNG wrapper library of LTIlib http://ltilib.sourceforge.net/doc/homepage/index.shtml: a library with algorithms and data structures frequently used in image processing and computer vision.
- NEWMAT http://www.robertnz.net/nm_intro.htm Matrix Library
- boost http://www.boost.org/ RNG
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"Bayesian unifying Design". This allows to incorporate any Bayesian filtering algorithm!
Currently the following filter schemes are implemented.
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Standard KF, EKF, IEKF and Non-minimal State KF (See http://people.mech.kuleuven.ac.be/~tlefebvr/publicaties/BayesStat.ps.gz
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Standard Particle filter (arbitrary proposal), BootstrapFilter (Proposal = System Model PDF), Auxiliary Particle filter, Extended Kalman Particle Filter.
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For further details about the design ideas, see the poster about the library presented at Valencia 7, a conference about Bayesian Statistics, available from http://people.mech.kuleuven.ac.be/~kgadeyne/doctoraat.html Also have a look at the filtering libraries home page http://www.orocos.org/bfl
Tinne De Laet Contributed a tutorial which can be found on the website. http://people.mech.kuleuven.be/~tdelaet/bfl_doc/getting_started_guide/getting_started_guide.html It discusses how to construct your first filter in bfl.
Wim Meeussen and Tinne De Laet contributed a installation guide which can be found on the website. http://people.mech.kuleuven.be/~tdelaet/bfl_doc/installation_guide/installation_guide.html