246 lines
9.0 KiB
C++
246 lines
9.0 KiB
C++
/**
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* @file ISAM2-inl.h
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* @brief Incremental update functionality (ISAM2) for BayesTree, with fluid relinearization.
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* @author Michael Kaess
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*/
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#include <boost/foreach.hpp>
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#include <boost/assign/std/list.hpp> // for operator +=
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using namespace boost::assign;
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#include <set>
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#include "NonlinearFactorGraph-inl.h"
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#include "GaussianFactor.h"
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#include "VectorConfig.h"
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#include "Conditional.h"
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#include "BayesTree-inl.h"
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#include "ISAM2.h"
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namespace gtsam {
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using namespace std;
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// from inference-inl.h - need to additionally return the newly created factor for caching
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boost::shared_ptr<GaussianConditional> _eliminateOne(FactorGraph<GaussianFactor>& graph, CachedFactors& cached, const Symbol& key) {
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// combine the factors of all nodes connected to the variable to be eliminated
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// if no factors are connected to key, returns an empty factor
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boost::shared_ptr<GaussianFactor> joint_factor = removeAndCombineFactors(graph,key);
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// eliminate that joint factor
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boost::shared_ptr<GaussianFactor> factor;
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boost::shared_ptr<GaussianConditional> conditional;
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boost::tie(conditional, factor) = joint_factor->eliminate(key);
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// ADDED: remember the intermediate result to be able to later restart computation in the middle
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cached[key] = factor;
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// add new factor on separator back into the graph
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if (!factor->empty()) graph.push_back(factor);
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// return the conditional Gaussian
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return conditional;
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}
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// from GaussianFactorGraph.cpp, see _eliminateOne above
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GaussianBayesNet _eliminate(FactorGraph<GaussianFactor>& graph, CachedFactors& cached, const Ordering& ordering) {
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GaussianBayesNet chordalBayesNet; // empty
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BOOST_FOREACH(const Symbol& key, ordering) {
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GaussianConditional::shared_ptr cg = _eliminateOne(graph, cached, key);
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chordalBayesNet.push_back(cg);
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}
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return chordalBayesNet;
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}
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GaussianBayesNet _eliminate_const(const FactorGraph<GaussianFactor>& graph, CachedFactors& cached, const Ordering& ordering) {
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// make a copy that can be modified locally
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FactorGraph<GaussianFactor> graph_ignored = graph;
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return _eliminate(graph_ignored, cached, ordering);
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}
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/** Create an empty Bayes Tree */
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template<class Conditional, class Config>
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ISAM2<Conditional, Config>::ISAM2() : BayesTree<Conditional>() {}
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/** Create a Bayes Tree from a nonlinear factor graph */
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template<class Conditional, class Config>
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ISAM2<Conditional, Config>::ISAM2(const NonlinearFactorGraph<Config>& nlfg, const Ordering& ordering, const Config& config)
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: BayesTree<Conditional>(nlfg.linearize(config).eliminate(ordering)), nonlinearFactors_(nlfg), linPoint_(config) {
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// todo: repeats calculation above, just to set "cached"
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_eliminate_const(nlfg.linearize(config), cached_, ordering);
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}
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/* ************************************************************************* */
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template<class Conditional, class Config>
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boost::shared_ptr<FactorGraph<NonlinearFactor<Config> > >
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ISAM2<Conditional, Config>::getAffectedFactors(const list<Symbol>& keys) const {
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boost::shared_ptr<FactorGraph<NonlinearFactor<Config> > > allAffected(new FactorGraph<NonlinearFactor<Config> >);
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list<int> indices;
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BOOST_FOREACH(const Symbol& key, keys) {
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const list<int> l = nonlinearFactors_.factors(key);
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indices.insert(indices.begin(), l.begin(), l.end());
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}
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indices.sort();
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indices.unique();
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BOOST_FOREACH(int i, indices) {
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allAffected->push_back(nonlinearFactors_[i]);
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}
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return allAffected;
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}
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/* ************************************************************************* */
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// retrieve all factors that ONLY contain the affected variables
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// (note that the remaining stuff is summarized in the cached factors)
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template<class Conditional, class Config>
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FactorGraph<GaussianFactor> ISAM2<Conditional, Config>::relinearizeAffectedFactors(const list<Symbol>& affectedKeys) const {
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boost::shared_ptr<FactorGraph<NonlinearFactor<Config> > > candidates = getAffectedFactors(affectedKeys);
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NonlinearFactorGraph<Config> nonlinearAffectedFactors;
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typename FactorGraph<NonlinearFactor<Config> >::const_iterator it;
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for(it = candidates->begin(); it != candidates->end(); it++) {
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bool inside = true;
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BOOST_FOREACH(const Symbol& key, (*it)->keys()) {
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if (find(affectedKeys.begin(), affectedKeys.end(), key) == affectedKeys.end()) {
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inside = false;
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break;
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}
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}
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if (inside)
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nonlinearAffectedFactors.push_back(*it);
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}
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return nonlinearAffectedFactors.linearize(linPoint_); // todo: use shared_ptr to avoid copying?
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}
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/* ************************************************************************* */
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// find intermediate (linearized) factors from cache that are passed into the affected area
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template<class Conditional, class Config>
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FactorGraph<GaussianFactor> ISAM2<Conditional, Config>::getCachedBoundaryFactors(Cliques& orphans) {
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FactorGraph<GaussianFactor> cachedBoundary;
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BOOST_FOREACH(sharedClique orphan, orphans) {
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// find the last variable that was eliminated
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const Symbol& key = orphan->ordering().back();
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// retrieve the cached factor and add to boundary
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cachedBoundary.push_back(cached_[key]);
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}
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return cachedBoundary; // todo: use shared_ptr to avoid copying?
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}
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/* ************************************************************************* */
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template<class Conditional, class Config>
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void ISAM2<Conditional, Config>::update_internal(const NonlinearFactorGraph<Config>& newFactors,
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const Config& config, Cliques& orphans) {
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#if 1 // 0=skip most, do batch
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FactorGraph<GaussianFactor> affectedFactors;
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list<Symbol> newFactorsKeys = newFactors.keys();
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#if 1 // 0=relinearize all in each step
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// relinearize all keys that are in newFactors, and already exist (not new variables!)
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list<Symbol> keysToRelinearize;
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BOOST_FOREACH(const Symbol& key, newFactorsKeys) {
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if (nonlinearFactors_.involves(key))
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keysToRelinearize.push_back(key);
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}
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// basically calculate all the keys contained in the factors that contain any of the keys...
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// the goal is to relinearize all variables directly affected by new factors
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boost::shared_ptr<FactorGraph<NonlinearFactor<Config> > > allAffected = getAffectedFactors(keysToRelinearize);
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list<Symbol> keysToBeRemoved = allAffected->keys();
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#else
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// debug only: full relinearization in each step
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list<Symbol> keysToRelinearize = nonlinearFactors_.keys();
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list<Symbol> keysToBeRemoved = nonlinearFactors_.keys();
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#endif
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// remove affected factors
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this->removeTop(keysToBeRemoved, affectedFactors, orphans);
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// selectively update the linearization point
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VectorConfig selected_delta;
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BOOST_FOREACH(const Symbol& key, keysToRelinearize) {
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if (delta_.contains(key)) // after constructor call, delta is empty...
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selected_delta.insert(key, delta_[key]);
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}
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linPoint_ = expmap(linPoint_, selected_delta);
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// relinearize the affected factors ...
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list<Symbol> affectedKeys = affectedFactors.keys();
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FactorGraph<GaussianFactor> factors = relinearizeAffectedFactors(affectedKeys);
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// ... add the cached intermediate results from the boundary of the orphans ...
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FactorGraph<GaussianFactor> cachedBoundary = getCachedBoundaryFactors(orphans);
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factors.push_back(cachedBoundary);
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#else
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// todo - debug only: batch operation
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FactorGraph<GaussianFactor> affectedFactors;
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list<Symbol> keysToBeRemoved = nonlinearFactors_.keys();
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this->removeTop(keysToBeRemoved, affectedFactors, orphans);
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this->print("---------------");
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linPoint_ = expmap(linPoint_, delta_); // todo-debug only
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FactorGraph<GaussianFactor> factors = nonlinearFactors_.linearize(linPoint_);
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#endif
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// add new variables
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linPoint_.insert(config);
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// ... and finally add the new linearized factors themselves
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FactorGraph<GaussianFactor> newFactorsLinearized = newFactors.linearize(linPoint_);
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factors.push_back(newFactorsLinearized);
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// create an ordering for the new and contaminated factors
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Ordering ordering;
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if (true) {
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ordering = factors.getOrdering();
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} else {
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list<Symbol> keys = factors.keys();
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keys.sort(); // todo: correct sorting order?
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ordering = keys;
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}
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// eliminate into a Bayes net
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BayesNet<Conditional> bayesNet = _eliminate(factors, cached_, ordering);
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// remember the new factors for later relinearization
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nonlinearFactors_.push_back(newFactors);
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// insert conditionals back in, straight into the topless bayesTree
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typename BayesNet<Conditional>::const_reverse_iterator rit;
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for ( rit=bayesNet.rbegin(); rit != bayesNet.rend(); ++rit ) {
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this->insert(*rit, &ordering);
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}
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// add orphans to the bottom of the new tree
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BOOST_FOREACH(sharedClique orphan, orphans) {
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Symbol key = findParentClique(orphan->separator_, ordering);
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sharedClique parent = (*this)[key];
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parent->children_ += orphan;
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orphan->parent_ = parent; // set new parent!
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}
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// update solution - todo: potentially expensive
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delta_ = optimize2(*this);
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}
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template<class Conditional, class Config>
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void ISAM2<Conditional, Config>::update(const NonlinearFactorGraph<Config>& newFactors, const Config& config) {
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Cliques orphans;
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this->update_internal(newFactors, config, orphans);
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}
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/* ************************************************************************* */
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}
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/// namespace gtsam
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