No version for distro eloquent. Known supported distros are highlighted in the buttons above.
No version for distro dashing. Known supported distros are highlighted in the buttons above.
Repository Summary
Checkout URI | https://github.com/ros-gbp/bfl-release.git |
VCS Type | git |
VCS Version | upstream |
Last Updated | 2019-02-09 |
Dev Status | UNMAINTAINED |
CI status | No Continuous Integration |
Released | RELEASED |
Package Tags | No category tags. |
Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Packages
Name | Version |
---|---|
bfl | 0.8.0 |
README
$Id$
//
// 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>
CONTRIBUTING
No CONTRIBUTING.md found.
Repository Summary
Checkout URI | https://github.com/ros-gbp/bfl-release.git |
VCS Type | git |
VCS Version | upstream |
Last Updated | 2019-02-09 |
Dev Status | UNMAINTAINED |
CI status | No Continuous Integration |
Released | RELEASED |
Package Tags | No category tags. |
Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Packages
Name | Version |
---|---|
bfl | 0.8.0 |
README
$Id$
//
// 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>
CONTRIBUTING
No CONTRIBUTING.md found.
No version for distro ardent. Known supported distros are highlighted in the buttons above.
No version for distro bouncy. Known supported distros are highlighted in the buttons above.
No version for distro crystal. Known supported distros are highlighted in the buttons above.
Repository Summary
Checkout URI | https://github.com/ros-gbp/bfl-release.git |
VCS Type | git |
VCS Version | debian/ros-lunar-bfl_0.7.0-0_stretch |
Last Updated | 2017-03-23 |
Dev Status | UNMAINTAINED |
CI status | No Continuous Integration |
Released | UNRELEASED |
Package Tags | No category tags. |
Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Packages
Name | Version |
---|---|
bfl | 0.7.0 |
README
$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>
CONTRIBUTING
No CONTRIBUTING.md found.
Repository Summary
Checkout URI | https://github.com/ros-gbp/bfl-release.git |
VCS Type | git |
VCS Version | upstream |
Last Updated | 2019-02-09 |
Dev Status | UNMAINTAINED |
CI status | No Continuous Integration |
Released | RELEASED |
Package Tags | No category tags. |
Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Packages
Name | Version |
---|---|
bfl | 0.8.0 |
README
$Id$
//
// 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>
CONTRIBUTING
No CONTRIBUTING.md found.
Repository Summary
Checkout URI | https://github.com/ros-gbp/bfl-release.git |
VCS Type | git |
VCS Version | upstream |
Last Updated | 2019-02-09 |
Dev Status | UNMAINTAINED |
CI status | No Continuous Integration |
Released | RELEASED |
Package Tags | No category tags. |
Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Packages
Name | Version |
---|---|
bfl | 0.8.0 |
README
$Id$
//
// 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>
CONTRIBUTING
No CONTRIBUTING.md found.
Repository Summary
Checkout URI | https://github.com/ros-gbp/bfl-release.git |
VCS Type | git |
VCS Version | upstream |
Last Updated | 2019-02-09 |
Dev Status | UNMAINTAINED |
CI status | No Continuous Integration |
Released | RELEASED |
Package Tags | No category tags. |
Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Packages
Name | Version |
---|---|
bfl | 0.8.0 |
README
$Id$
//
// 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>
CONTRIBUTING
No CONTRIBUTING.md found.