The Bayesian Filtering Library development team is pleased to announce the 0.6.0 release of BFL.
You can download this release from here and read the installation instructions online (also reachable through the orocos website).
This release includes support for lti, boost and newmat as matrix library and lti and boost as random number generator.
A new feature is the backward filter and smoother algorithm and the CPPUnit tests.
Furthermore for the first time, a step-by-step installation guide is available for Visual Studio on Windows.
In detail this release addresses the following reported issues:
ID Summary
303 The future of BFL (aka: BFL needs new maintainer)
319 add backward filter and tests to build system
320 Default implementation for virtual functions
321 const function arguments in mcpdf class
329 Add function to get one sample + change int into unsigned...
330 Sample::ValueSet() does not adjust dimension
331 BFL should use return codes or c++ exceptions
333 Sample stores dimension
334 No need to re-implement virtual functions
335 Cleanup of some pdf code
343 PostGet() should return a more specific Pdf if possible
349 Add SVN revision number to doxygen generated docu
350 make analytic system and measurement model consistent
351 Extension for IteratedExtendedKalmanFilter
389 Examples refuse to compile
392 Change build system to cmake
393 Not possible to build static libraries
395 Automate building of Ubuntu/Debian packages
400 Cholesky decomposition
403 Building BFL in Windows
411 Boost needs pinv implementation
416 License issues for BFL template code
Details are available through: this link
The Bayesian Filtering Library (BFL) provides an application independent framework for inference in Dynamic Bayesian Networks, i.e., recursive information processing and estimation algorithms based on Bayes' rule, such as (Extended) Kalman Filters, Particle Filters (or Sequential Monte Carlo methods), etc. These algorithms can, for example, be run on top of the Realtime Services, or be used for estimation in Kinematics & Dynamics applications.