# BFL - bind variables

We're trying to use a Bayesian Filter to do some sensor fusion for estimating the joints positions on a joint based robot. For this we're using the BFL library as is done in the robot-pose-ekf package.

We now have two versions of the same filter (one using ExtendedKalmanFilter, the other one a particle filter: BootstrapFilter) that converges but haven't yet found a way of binding the BFL state representation variables. In our case, we'd like to make sure that each variable of the state stays within the joint limits.

Feel free to ask for more details as I'm not exactly sure what is most relevant.

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from Enrico on BFL mailing list

This is a constrained optimization problem. I am afraid there is no "clean" way of doing this. If the state variables are defined by Gaussian pdfs, than by definition those span an infinite support.

You could try to make variables outside of the joint limits "highly unlikely" by filtering your measurements, and rejecting those outside a validation area, or carefully defining Process noise and Measurement Noise Covariance Matrices, but if you really want to use constraints, that you're actually solving an Estimation + Constrained Optimization problem, and you'll need different tools for that.

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