114 lines
3.6 KiB
C++
114 lines
3.6 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file Expression.h
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* @date September 18, 2014
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* @author Frank Dellaert
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* @author Paul Furgale
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* @brief Expressions for Block Automatic Differentiation
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*/
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#pragma once
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#include <gtsam/nonlinear/Expression.h>
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#include <gtsam/nonlinear/NonlinearFactor.h>
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#include <gtsam/base/Testable.h>
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#include <numeric>
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namespace gtsam {
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/**
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* Factor that supports arbitrary expressions via AD
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*/
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template<class T>
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class ExpressionFactor: public NoiseModelFactor {
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protected:
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T measurement_; ///< the measurement to be compared with the expression
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Expression<T> expression_; ///< the expression that is AD enabled
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FastVector<int> dims_; ///< dimensions of the Jacobian matrices
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static const int Dim = traits_x<T>::dimension;
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public:
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/// Constructor
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ExpressionFactor(const SharedNoiseModel& noiseModel, //
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const T& measurement, const Expression<T>& expression) :
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measurement_(measurement), expression_(expression) {
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if (!noiseModel)
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throw std::invalid_argument("ExpressionFactor: no NoiseModel.");
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if (noiseModel->dim() != Dim)
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throw std::invalid_argument(
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"ExpressionFactor was created with a NoiseModel of incorrect dimension.");
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noiseModel_ = noiseModel;
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// Get keys and dimensions for Jacobian matrices
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// An Expression is assumed unmutable, so we do this now
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boost::tie(keys_, dims_) = expression_.keysAndDims();
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}
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/**
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* Error function *without* the NoiseModel, \f$ h(x)-z \f$.
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* We override this method to provide
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* both the function evaluation and its derivative(s) in H.
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*/
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virtual Vector unwhitenedError(const Values& x,
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boost::optional<std::vector<Matrix>&> H = boost::none) const {
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if (H) {
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const T value = expression_.value(x, keys_, dims_, *H);
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return traits_x<T>::Local(measurement_, value);
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} else {
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const T value = expression_.value(x);
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return traits_x<T>::Local(measurement_, value);
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}
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}
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virtual boost::shared_ptr<GaussianFactor> linearize(const Values& x) const {
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// Only linearize if the factor is active
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if (!active(x))
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return boost::shared_ptr<JacobianFactor>();
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// Create a writeable JacobianFactor in advance
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// In case noise model is constrained, we need to provide a noise model
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bool constrained = noiseModel_->isConstrained();
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boost::shared_ptr<JacobianFactor> factor(
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constrained ? new JacobianFactor(keys_, dims_, Dim,
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boost::static_pointer_cast<noiseModel::Constrained>(noiseModel_)->unit()) :
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new JacobianFactor(keys_, dims_, Dim));
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// Wrap keys and VerticalBlockMatrix into structure passed to expression_
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VerticalBlockMatrix& Ab = factor->matrixObject();
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JacobianMap jacobianMap(keys_, Ab);
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// Zero out Jacobian so we can simply add to it
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Ab.matrix().setZero();
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// Get value and Jacobians, writing directly into JacobianFactor
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T value = expression_.value(x, jacobianMap); // <<< Reverse AD happens here !
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// Evaluate error and set RHS vector b
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Ab(size()).col(0) = -traits_x<T>::Local(measurement_, value);
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// Whiten the corresponding system, Ab already contains RHS
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Vector dummy(Dim);
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noiseModel_->WhitenSystem(Ab.matrix(), dummy);
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return factor;
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}
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};
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// ExpressionFactor
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}
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