129 lines
3.9 KiB
C++
129 lines
3.9 KiB
C++
/*
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* @file NonlinearEquality.h
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* @brief Factor to handle enforced equality between factors
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* @author Alex Cunningham
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*/
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#pragma once
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#include <limits>
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#include <iostream>
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#include "Key.h"
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#include "NonlinearFactor.h"
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namespace gtsam {
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/**
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* Template default compare function that assumes a testable T
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*/
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template<class T>
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bool compare(const T& a, const T& b) { return a.equals(b); }
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/**
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* An equality factor that forces either one variable to a constant,
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* or a set of variables to be equal to each other.
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*
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* Depending on flag, throws an error at linearization if the constraints are not met.
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*
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* Switchable implementation:
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* - ALLLOW_ERROR : if we allow that there can be nonzero error, does not throw, and uses gain
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* - ONLY_EXACT : throws error at linearization if not at exact feasible point, and infinite error
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*/
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template<class Config, class Key, class T>
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class NonlinearEquality: public NonlinearFactor1<Config, Key, T> {
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private:
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// feasible value
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T feasible_;
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// error handling flag
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bool allow_error_;
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// error gain in allow error case
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double error_gain_;
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public:
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/**
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* Function that compares two values
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*/
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bool (*compare_)(const T& a, const T& b);
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typedef NonlinearFactor1<Config, Key, T> Base;
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/**
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* Constructor - forces exact evaluation
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*/
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NonlinearEquality(const Key& j, const T& feasible, bool (*compare)(const T&, const T&) = compare<T>) :
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Base(noiseModel::Constrained::All(dim(feasible)), j), feasible_(feasible),
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allow_error_(false), error_gain_(std::numeric_limits<double>::infinity()),
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compare_(compare) {
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}
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/**
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* Constructor - allows inexact evaluation
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*/
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NonlinearEquality(const Key& j, const T& feasible, double error_gain, bool (*compare)(const T&, const T&) = compare<T>) :
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Base(noiseModel::Constrained::All(dim(feasible)), j), feasible_(feasible),
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allow_error_(true), error_gain_(error_gain),
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compare_(compare) {
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}
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void print(const std::string& s = "") const {
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std::cout << "Constraint: " << s << " on [" << (std::string)(this->key_) << "]\n";
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gtsam::print(feasible_,"Feasible Point");
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std::cout << "Variable Dimension: " << dim(feasible_) << std::endl;
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}
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/** Check if two factors are equal */
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bool equals(const Factor<Config>& f, double tol = 1e-9) const {
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const NonlinearEquality<Config,Key,T>* p =
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dynamic_cast<const NonlinearEquality<Config,Key,T>*> (&f);
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if (p == NULL) return false;
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if (!Base::equals(*p)) return false;
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return compare_(feasible_, p->feasible_);
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}
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/** actual error function calculation */
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virtual double error(const Config& c) const {
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const T& xj = c[this->key_];
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Vector e = this->unwhitenedError(c);
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if (allow_error_ || !compare_(xj, feasible_)) {
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return error_gain_ * inner_prod(e,e);
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} else {
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return 0.0;
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}
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}
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/** error function */
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inline Vector evaluateError(const T& xj, boost::optional<Matrix&> H = boost::none) const {
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size_t nj = dim(feasible_);
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if (allow_error_) {
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if (H) *H = eye(nj); // FIXME: this is not the right linearization for nonlinear compare
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return logmap(xj, feasible_);
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} else if (compare_(feasible_,xj)) {
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if (H) *H = eye(nj);
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return zero(nj); // set error to zero if equal
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} else {
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if (H) throw std::invalid_argument(
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"Linearization point not feasible for " + (std::string)(this->key_) + "!");
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return repeat(nj, std::numeric_limits<double>::infinity()); // set error to infinity if not equal
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}
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}
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// Linearize is over-written, because base linearization tries to whiten
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virtual boost::shared_ptr<GaussianFactor> linearize(const Config& x) const {
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const T& xj = x[this->key_];
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Matrix A;
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Vector b = evaluateError(xj, A);
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// TODO pass unwhitened + noise model to Gaussian factor
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SharedDiagonal model = noiseModel::Constrained::All(b.size());
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return GaussianFactor::shared_ptr(new GaussianFactor(this->key_, A, b, model));
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}
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}; // NonlinearEquality
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} // namespace gtsam
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