gtsam/gtsam_unstable/nonlinear/ExpressionFactor.h

123 lines
3.8 KiB
C++

/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file Expression.h
* @date September 18, 2014
* @author Frank Dellaert
* @author Paul Furgale
* @brief Expressions for Block Automatic Differentiation
*/
#include <gtsam_unstable/nonlinear/Expression.h>
#include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/base/Testable.h>
namespace gtsam {
/**
* Factor that supports arbitrary expressions via AD
*/
template<class T>
class ExpressionFactor: public NoiseModelFactor {
const T measurement_;
const Expression<T> expression_;
public:
/// Constructor
ExpressionFactor(const SharedNoiseModel& noiseModel, //
const T& measurement, const Expression<T>& expression) :
NoiseModelFactor(noiseModel, expression.keys()), //
measurement_(measurement), expression_(expression) {
}
/**
* Error function *without* the NoiseModel, \f$ z-h(x) \f$.
* We override this method to provide
* both the function evaluation and its derivative(s) in H.
*/
virtual Vector unwhitenedError(const Values& x,
boost::optional<std::vector<Matrix>&> H = boost::none) const {
if (H) {
assert(H->size()==size());
Augmented<T> augmented = expression_.augmented(x);
// move terms to H, which is pre-allocated to correct size
augmented.move(*H);
return measurement_.localCoordinates(augmented.value());
} else {
const T& value = expression_.value(x);
return measurement_.localCoordinates(value);
}
}
virtual boost::shared_ptr<GaussianFactor> linearize(const Values& x) const {
// Only linearize if the factor is active
if (!this->active(x))
return boost::shared_ptr<JacobianFactor>();
// Evaluate error to get Jacobians and RHS vector b
Augmented<T> augmented = expression_.augmented(x);
Vector b = -measurement_.localCoordinates(augmented.value());
// check(noiseModel_, b); // TODO: use, but defined in NonlinearFactor.cpp
// Whiten the corresponding system now
// TODO ! this->noiseModel_->WhitenSystem(A, b);
// Terms, needed to create JacobianFactor below, are already here!
const JacobianMap& terms = augmented.jacobians();
size_t n = terms.size();
// Get dimensions of matrices
std::vector<size_t> dimensions;
dimensions.reserve(n);
std::vector<Key> keys;
keys.reserve(n);
for (JacobianMap::const_iterator it = terms.begin(); it != terms.end();
++it) {
const std::pair<Key, Matrix>& term = *it;
Key key = term.first;
const Matrix& Ai = term.second;
dimensions.push_back(Ai.cols());
keys.push_back(key);
}
// Construct block matrix
VerticalBlockMatrix Ab(dimensions, b.size(), true);
// Check and add terms
DenseIndex i = 0; // For block index
for (JacobianMap::const_iterator it = terms.begin(); it != terms.end();
++it) {
const std::pair<Key, Matrix>& term = *it;
const Matrix& Ai = term.second;
Ab(i++) = Ai;
}
Ab(n).col(0) = b;
// TODO pass unwhitened + noise model to Gaussian factor
// For now, only linearized constrained factors have noise model at linear level!!!
noiseModel::Constrained::shared_ptr constrained = //
boost::dynamic_pointer_cast<noiseModel::Constrained>(this->noiseModel_);
if (constrained) {
return boost::make_shared<JacobianFactor>(keys, Ab, constrained->unit());
} else
return boost::make_shared<JacobianFactor>(keys, Ab);
}
};
// ExpressionFactor
}