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Probability of a measurement to belong to a given measurement model not bound between 0 and 1

Hello,

I'm trying to get the probability of a new measurement: meas belonging to a given measurement model, given the current state of the system (the prediction of the filter).

MatrixWrapper::ColumnVector post = kalman_filter_->PostGet()->ExpectedValueGet();

[...]

proba = measurement_models_[MeasurementType::FACE]->ProbabilityGet(meas, post);

However this probability is often greater than 1, for example:

post = [10](4.66914e-310,30,0.5,0.587388,1.18765,1.07375,0,0,0,0) / meas = [3](0.57807,1.23188,1.07589) / proba: 2.32501

Am I doing something wrong?

Cheers,

Ugo

Write specialization BFL::Pdf<BFL::Quaternion>

There's no specialization for a single Quaternion state. This might become handy for a distributed orientation filter or for more specialized orientation filters such as the multiplicative one, where a specialized class derived from AnalyticConditionalGaussianAdditiveNoise might not be enough!

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