gtsam/cpp/ISAM2-inl.h

111 lines
3.7 KiB
C++

/**
* @file ISAM2-inl.h
* @brief Incremental update functionality (ISAM2) for BayesTree, with fluid relinearization.
* @author Michael Kaess
*/
#include <boost/foreach.hpp>
#include <boost/assign/std/list.hpp> // for operator +=
using namespace boost::assign;
#include "NonlinearFactorGraph.h"
#include "GaussianFactor.h"
#include "VectorConfig.h"
#include "Conditional.h"
#include "BayesTree-inl.h"
#include "ISAM2.h"
namespace gtsam {
using namespace std;
/** Create an empty Bayes Tree */
template<class Conditional, class Config>
ISAM2<Conditional, Config>::ISAM2() : BayesTree<Conditional>() {}
/** Create a Bayes Tree from a nonlinear factor graph */
template<class Conditional, class Config>
ISAM2<Conditional, Config>::ISAM2(const NonlinearFactorGraph<Config>& nlfg, const Ordering& ordering, const Config& config) {
BayesTree<Conditional>(nlfg.linearize(config).eliminate(ordering));
nonlinearFactors_ = nlfg;
config_ = config;
}
/* ************************************************************************* */
template<class Conditional, class Config>
void ISAM2<Conditional, Config>::update_internal(const NonlinearFactorGraph<Config>& newFactors,
const Config& config, Cliques& orphans) {
// copy variables into config_, but don't overwrite existing entries (current linearization point!)
for (typename Config::const_iterator it = config.begin(); it!=config.end(); it++) {
if (!config_.contains(it->first)) {
config_.insert(it->first, it->second);
}
}
nonlinearFactors_.push_back(newFactors);
FactorGraph<GaussianFactor> newFactorsLinearized = newFactors.linearize(config_);
// Remove the contaminated part of the Bayes tree
FactorGraph<GaussianFactor> affectedFactors;
boost::tie(affectedFactors, orphans) = this->removeTop(newFactorsLinearized);
// find the corresponding original nonlinear factors, and relinearize them
NonlinearFactorGraph<Config> nonlinearAffectedFactors;
list<string> keys = affectedFactors.keys();
for (list<string>::iterator keyIt = keys.begin(); keyIt!=keys.end(); keyIt++) {
list<int> indices = nonlinearFactors_.factors(*keyIt);
for (list<int>::iterator indIt = indices.begin(); indIt!=indices.end(); indIt++) {
// todo - do we need to check if it already exists? probably... if (*indIt)
nonlinearAffectedFactors.push_back(nonlinearFactors_[*indIt]);
}
}
FactorGraph<GaussianFactor> factors = nonlinearAffectedFactors.linearize(config_);
// add the new factors themselves
factors.push_back(newFactorsLinearized);
// create an ordering for the new and contaminated factors
Ordering ordering;
if (true) {
ordering = factors.getOrdering();
} else {
list<string> keys = factors.keys();
keys.sort(); // todo: correct sorting order?
ordering = keys;
}
// eliminate into a Bayes net
BayesNet<Conditional> bayesNet = eliminate<GaussianFactor, Conditional>(factors,ordering);
// insert conditionals back in, straight into the topless bayesTree
typename BayesNet<Conditional>::const_reverse_iterator rit;
for ( rit=bayesNet.rbegin(); rit != bayesNet.rend(); ++rit )
this->insert(*rit);
int count = 0;
// add orphans to the bottom of the new tree
BOOST_FOREACH(sharedClique orphan, orphans) {
string key = orphan->separator_.front();
sharedClique parent = (*this)[key];
parent->children_ += orphan;
orphan->parent_ = parent; // set new parent!
}
}
template<class Conditional, class Config>
void ISAM2<Conditional, Config>::update(const NonlinearFactorGraph<Config>& newFactors, const Config& config) {
Cliques orphans;
this->update_internal(newFactors, config, orphans);
}
/* ************************************************************************* */
}
/// namespace gtsam