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@ -11,7 +11,7 @@
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/**
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/**
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* @file GaussMarkov1stOrderFactor.h
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* @file GaussMarkov1stOrderFactor.h
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* @author Vadim Indelman, Stephen Williams
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* @author Vadim Indelman, Stephen Williams, Luca Carlone
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* @date Jan 17, 2012
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* @date Jan 17, 2012
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**/
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**/
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#pragma once
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#pragma once
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@ -63,7 +63,7 @@ public:
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/** Constructor */
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/** Constructor */
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GaussMarkov1stOrderFactor(const Key& key1, const Key& key2, double delta_t, Vector tau,
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GaussMarkov1stOrderFactor(const Key& key1, const Key& key2, double delta_t, Vector tau,
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const SharedGaussian& model) :
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const SharedGaussian& model) :
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Base(calc_descrete_noise_model(model, delta_t), key1, key2), dt_(delta_t), tau_(tau) {
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Base(calcDiscreteNoiseModel(model, delta_t), key1, key2), dt_(delta_t), tau_(tau) {
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}
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}
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virtual ~GaussMarkov1stOrderFactor() {}
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virtual ~GaussMarkov1stOrderFactor() {}
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@ -120,7 +120,7 @@ private:
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ar & BOOST_SERIALIZATION_NVP(tau_);
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ar & BOOST_SERIALIZATION_NVP(tau_);
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}
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}
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SharedGaussian calc_descrete_noise_model(const SharedGaussian& model, double delta_t){
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SharedGaussian calcDiscreteNoiseModel(const SharedGaussian& model, double delta_t){
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/* Q_d (approx)= Q * delta_t */
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/* Q_d (approx)= Q * delta_t */
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/* In practice, square root of the information matrix is represented, so that:
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/* In practice, square root of the information matrix is represented, so that:
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* R_d (approx)= R / sqrt(delta_t)
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* R_d (approx)= R / sqrt(delta_t)
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