$Id: README 28203 2007-05-29 07:58:40Z tdelaet $ // // BFL: BAYESIAN FILTERING LIBRARY // // // Copyright (C) 2002/2003/2004 Klaas Gadeyne <first dot last at gmail dot com> // // 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: - Scene (Andrew Davison). See <http://www.robots.ox.ac.uk/~ajd/Scene/> - Bayes++ (from ACFR). See <http://www.acfr.usyd.edu.au/technology/bayesianfilter/Bayes++.htm> - 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> - 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: - Released under the GNU LGPL licence - 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 - "Bayesian unifying Design". This allows to incorporate any Bayesian filtering algorithm! Currently the following filter schemes are implemented. * Standard KF, EKF, IEKF and Non-minimal State KF (See <http://people.mech.kuleuven.ac.be/~tlefebvr/publicaties/BayesStat.ps.gz> * Standard Particle filter (arbitrary proposal), BootstrapFilter (Proposal = System Model PDF), Auxiliary Particle filter, Extended Kalman Particle Filter. 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>