The Bayesian Filtering Library (BFL) [ref] 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. Click below to read the rest of this post.The Bayesian Filtering Library (BFL) [ref] 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.

Particles for pallet localization using sick laser scanner

Kalman localization: Robot localization based on deadreckoning

Particles for mobile robot localisation (deadreckoning)