update+relin combined for speed; new backsub/threshold confirmed to yield correct result
Michael Kaess
parent
aca6602a32
commit
b825655ba6
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@ -83,7 +83,7 @@ void tictoc_print() {
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timing.print();
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}
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#else
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void tictoc_print() {}
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void tictoc_print() {}
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double tic(string id) {return 0.;}
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double toc(string id) {return 0.;}
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#endif
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@ -91,225 +91,360 @@ double toc(string id) {return 0.;}
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namespace gtsam {
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using namespace std;
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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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// 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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tic("eliminate_removeandcombinefactors");
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boost::shared_ptr<GaussianFactor> joint_factor = removeAndCombineFactors(graph,key);
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toc("eliminate_removeandcombinefactors");
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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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tic("eliminate_removeandcombinefactors");
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boost::shared_ptr<GaussianFactor> joint_factor = removeAndCombineFactors(graph,key);
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toc("eliminate_removeandcombinefactors");
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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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tic("eliminate_eliminate");
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boost::tie(conditional, factor) = joint_factor->eliminate(key);
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toc("eliminate_eliminate");
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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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tic("eliminate_eliminate");
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boost::tie(conditional, factor) = joint_factor->eliminate(key);
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toc("eliminate_eliminate");
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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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// 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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// 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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// 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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// 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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// special const version used in constructor below
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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)),
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theta_(config), delta_(VectorConfig()), nonlinearFactors_(nlfg) {
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// todo: repeats calculation above, just to set "cached"
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// De-referencing shared pointer can be quite expensive because creates temporary
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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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list<size_t> ISAM2<Conditional, Config>::getAffectedFactors(const list<Symbol>& keys) const {
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FactorGraph<NonlinearFactor<Config> > allAffected;
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list<size_t> indices;
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BOOST_FOREACH(const Symbol& key, keys) {
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const list<size_t> 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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return indices;
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}
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// special const version used in constructor below
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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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/* ************************************************************************* */
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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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boost::shared_ptr<GaussianFactorGraph> ISAM2<Conditional, Config>::relinearizeAffectedFactors
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(const set<Symbol>& affectedKeys) const {
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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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list<Symbol> affectedKeysList; // todo: shouldn't have to convert back to list...
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affectedKeysList.insert(affectedKeysList.begin(), affectedKeys.begin(), affectedKeys.end());
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list<size_t> candidates = getAffectedFactors(affectedKeysList);
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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)),
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theta_(config), delta_(VectorConfig()), nonlinearFactors_(nlfg) {
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// todo: repeats calculation above, just to set "cached"
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// De-referencing shared pointer can be quite expensive because creates temporary
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_eliminate_const(*nlfg.linearize(config), cached_, ordering);
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}
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NonlinearFactorGraph<Config> nonlinearAffectedFactors;
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/* ************************************************************************* */
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template<class Conditional, class Config>
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list<size_t> ISAM2<Conditional, Config>::getAffectedFactors(const list<Symbol>& keys) const {
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FactorGraph<NonlinearFactor<Config> > allAffected;
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list<size_t> indices;
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BOOST_FOREACH(const Symbol& key, keys) {
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const list<size_t> 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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return indices;
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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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boost::shared_ptr<GaussianFactorGraph> ISAM2<Conditional, Config>::relinearizeAffectedFactors
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(const set<Symbol>& affectedKeys) const {
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list<Symbol> affectedKeysList; // todo: shouldn't have to convert back to list...
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affectedKeysList.insert(affectedKeysList.begin(), affectedKeys.begin(), affectedKeys.end());
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list<size_t> candidates = getAffectedFactors(affectedKeysList);
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NonlinearFactorGraph<Config> nonlinearAffectedFactors;
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BOOST_FOREACH(size_t idx, candidates) {
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bool inside = true;
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BOOST_FOREACH(const Symbol& key, nonlinearFactors_[idx]->keys()) {
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if (affectedKeys.find(key) == affectedKeys.end()) {
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inside = false;
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break;
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}
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BOOST_FOREACH(size_t idx, candidates) {
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bool inside = true;
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BOOST_FOREACH(const Symbol& key, nonlinearFactors_[idx]->keys()) {
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if (affectedKeys.find(key) == affectedKeys.end()) {
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inside = false;
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break;
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}
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if (inside)
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nonlinearAffectedFactors.push_back(nonlinearFactors_[idx]);
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}
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// TODO: temporary might be expensive, return shared pointer ?
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return nonlinearAffectedFactors.linearize(theta_);
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if (inside)
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nonlinearAffectedFactors.push_back(nonlinearFactors_[idx]);
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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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// TODO: temporary might be expensive, return shared pointer ?
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return nonlinearAffectedFactors.linearize(theta_);
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}
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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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/* ************************************************************************* */
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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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return 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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/* ************************************************************************* */
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template<class Conditional, class Config>
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void ISAM2<Conditional, Config>::linear_update(const FactorGraph<GaussianFactor>& newFactors) {
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return cachedBoundary;
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}
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// Input: BayesTree(this), newFactors
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/* ************************************************************************* */
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template<class Conditional, class Config>
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void ISAM2<Conditional, Config>::recalculate(const list<Symbol>& markedKeys, const FactorGraph<GaussianFactor>* newFactors) {
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//#define PRINT_STATS // figures for paper, disable for timing
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// Input: BayesTree(this), newFactors
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//#define PRINT_STATS // figures for paper, disable for timing
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#ifdef PRINT_STATS
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static int counter = 0;
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int maxClique = 0;
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double avgClique = 0;
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int numCliques = 0;
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int nnzR = 0;
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if (counter>0) { // cannot call on empty tree
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GaussianISAM2_P::CliqueData cdata = this->getCliqueData();
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GaussianISAM2_P::CliqueStats cstats = cdata.getStats();
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maxClique = cstats.maxConditionalSize;
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avgClique = cstats.avgConditionalSize;
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numCliques = cdata.conditionalSizes.size();
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nnzR = calculate_nnz(this->root());
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}
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counter++;
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static int counter = 0;
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int maxClique = 0;
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double avgClique = 0;
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int numCliques = 0;
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int nnzR = 0;
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if (counter>0) { // cannot call on empty tree
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GaussianISAM2_P::CliqueData cdata = this->getCliqueData();
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GaussianISAM2_P::CliqueStats cstats = cdata.getStats();
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maxClique = cstats.maxConditionalSize;
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avgClique = cstats.avgConditionalSize;
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numCliques = cdata.conditionalSizes.size();
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nnzR = calculate_nnz(this->root());
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}
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counter++;
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#endif
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// 1. Remove top of Bayes tree and convert to a factor graph:
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// (a) For each affected variable, remove the corresponding clique and all parents up to the root.
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// (b) Store orphaned sub-trees \BayesTree_{O} of removed cliques.
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const list<Symbol> markedKeys = newFactors.keys();
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// 1. Remove top of Bayes tree and convert to a factor graph:
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// (a) For each affected variable, remove the corresponding clique and all parents up to the root.
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// (b) Store orphaned sub-trees \BayesTree_{O} of removed cliques.
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tic("re-removetop");
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Cliques orphans;
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BayesNet<GaussianConditional> affectedBayesNet;
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this->removeTop(markedKeys, affectedBayesNet, orphans);
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toc("re-removetop");
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// FactorGraph<GaussianFactor> factors(affectedBayesNet);
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// bug was here: we cannot reuse the original factors, because then the cached factors get messed up
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// [all the necessary data is actually contained in the affectedBayesNet, including what was passed in from the boundaries,
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// so this would be correct; however, in the process we also generate new cached_ entries that will be wrong (ie. they don't
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// contain what would be passed up at a certain point if batch elimination was done, but that's what we need); we could choose
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// not to update cached_ from here, but then the new information (and potentially different variable ordering) is not reflected
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// in the cached_ values which again will be wrong]
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// so instead we have to retrieve the original linearized factors AND add the cached factors from the boundary
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// BEGIN OF COPIED CODE
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tic("re-lookup");
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// ordering provides all keys in conditionals, there cannot be others because path to root included
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set<Symbol> affectedKeys;
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list<Symbol> tmp = affectedBayesNet.ordering();
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affectedKeys.insert(tmp.begin(), tmp.end());
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FactorGraph<GaussianFactor> factors(*relinearizeAffectedFactors(affectedKeys)); // todo: no need to relinearize here, should have cached linearized factors
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lastAffectedMarkedCount = markedKeys.size();
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lastAffectedVariableCount = affectedKeys.size();
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lastAffectedFactorCount = factors.size();
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#ifdef PRINT_STATS
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// output for generating figures
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cout << "linear: #markedKeys: " << markedKeys.size() << " #affectedVariables: " << affectedKeys.size()
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<< " #affectedFactors: " << factors.size() << " maxCliqueSize: " << maxClique
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<< " avgCliqueSize: " << avgClique << " #Cliques: " << numCliques << " nnzR: " << nnzR << endl;
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#endif
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toc("re-lookup");
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tic("re-cached");
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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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toc("re-cached");
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// END OF COPIED CODE
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// 2. Add the new factors \Factors' into the resulting factor graph
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tic("re-newfactors");
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if (newFactors) {
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factors.push_back(*newFactors);
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}
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toc("re-newfactors");
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// 3. Re-order and eliminate the factor graph into a Bayes net (Algorithm [alg:eliminate]), and re-assemble into a new Bayes tree (Algorithm [alg:BayesTree])
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// create an ordering for the new and contaminated factors
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// markedKeys are passed in: those variables will be forced to the end in the ordering
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set<Symbol> markedKeysSet;
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markedKeysSet.insert(markedKeys.begin(), markedKeys.end());
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Ordering ordering = factors.getConstrainedOrdering(markedKeysSet); // intelligent ordering
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// Ordering ordering = factors.getOrdering(); // original ordering, yields in bad performance
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// eliminate into a Bayes net
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tic("eliminate");
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BayesNet<Conditional> bayesNet = _eliminate(factors, cached_, ordering);
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toc("eliminate");
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tic("re-assemble");
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// Create Index from ordering
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IndexTable<Symbol> index(ordering);
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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, index);
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// Save number of affectedCliques
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lastAffectedCliqueCount = this->size();
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tic("re-assemble");
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// 4. Insert the orphans back into the new Bayes tree.
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tic("re-orphans");
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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 parentRepresentative = findParentClique(orphan->separator_, index);
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sharedClique parent = (*this)[parentRepresentative];
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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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toc("re-orphans");
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// Output: BayesTree(this)
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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>::linear_update(const FactorGraph<GaussianFactor>& newFactors) {
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const list<Symbol> markedKeys = newFactors.keys();
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recalculate(markedKeys, &newFactors);
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}
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/* ************************************************************************* */
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// find all variables that are directly connected by a measurement to one of the marked variables
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template<class Conditional, class Config>
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void ISAM2<Conditional, Config>::find_all(sharedClique clique, list<Symbol>& keys, const list<Symbol>& marked) {
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// does the separator contain any of the variables?
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bool found = false;
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BOOST_FOREACH(const Symbol& key, clique->separator_) {
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if (find(marked.begin(), marked.end(), key) != marked.end())
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found = true;
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}
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if (found) {
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// then add this clique
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keys.push_back(clique->keys().front());
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}
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BOOST_FOREACH(const sharedClique& child, clique->children_) {
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find_all(child, keys, marked);
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}
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}
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/* ************************************************************************* */
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template<class Conditional, class Config>
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list<Symbol> ISAM2<Conditional, Config>::fluid_relinearization(double relinearize_threshold) {
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// Input: nonlinear factors factors_, linearization point theta_, Bayes tree (this), delta_
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// 1. Mark variables in \Delta above threshold \beta: J=\{\Delta_{j}\in\Delta|\Delta_{j}\geq\beta\}.
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list<Symbol> marked;
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VectorConfig deltaMarked;
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for (VectorConfig::const_iterator it = delta_.begin(); it!=delta_.end(); it++) {
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Symbol key = it->first;
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Vector v = it->second;
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if (max(abs(v)) >= relinearize_threshold) {
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marked.push_back(key);
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deltaMarked.insert(key, v);
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}
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}
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if (marked.size()>0) {
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// 2. Update linearization point for marked variables: \Theta_{J}:=\Theta_{J}+\Delta_{J}.
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theta_ = theta_.expmap(deltaMarked);
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// 3. Mark all cliques that involve marked variables \Theta_{J} and all their ancestors.
|
||||
|
||||
// mark all cliques that involve marked variables
|
||||
list<Symbol> affectedSymbols(marked); // add all marked
|
||||
tic("nonlin-find_all");
|
||||
find_all(this->root(), affectedSymbols, marked); // add other cliques that have the marked ones in the separator
|
||||
affectedSymbols.sort(); // remove duplicates
|
||||
affectedSymbols.unique();
|
||||
toc("nonlin-find_all");
|
||||
|
||||
// 4. From the leaves to the top, if a clique is marked:
|
||||
// re-linearize the original factors in \Factors associated with the clique,
|
||||
// add the cached marginal factors from its children, and re-eliminate.
|
||||
|
||||
// todo: for simplicity, currently simply remove the top and recreate it using the original ordering
|
||||
//recalculate(affectedSymbols);
|
||||
return affectedSymbols;
|
||||
|
||||
#if 0
|
||||
|
||||
tic("nonlin-mess");
|
||||
Cliques orphans;
|
||||
BayesNet<GaussianConditional> affectedBayesNet;
|
||||
this->removeTop(markedKeys, affectedBayesNet, orphans);
|
||||
this->removeTop(affectedSymbols, affectedBayesNet, orphans);
|
||||
// remember original ordering
|
||||
// todo Ordering original_ordering = affectedBayesNet.ordering(); // does not yield original ordering...
|
||||
FactorGraph<GaussianFactor> tmp_factors(affectedBayesNet); // so instead we recalculate an acceptable ordering here
|
||||
Ordering original_ordering = tmp_factors.getOrdering(); // todo - remove multiple lines up to here
|
||||
|
||||
// FactorGraph<GaussianFactor> factors(affectedBayesNet);
|
||||
// bug was here: we cannot reuse the original factors, because then the cached factors get messed up
|
||||
// [all the necessary data is actually contained in the affectedBayesNet, including what was passed in from the boundaries,
|
||||
// so this would be correct; however, in the process we also generate new cached_ entries that will be wrong (ie. they don't
|
||||
// contain what would be passed up at a certain point if batch elimination was done, but that's what we need); we could choose
|
||||
// not to update cached_ from here, but then the new information (and potentially different variable ordering) is not reflected
|
||||
// in the cached_ values which again will be wrong]
|
||||
// so instead we have to retrieve the original linearized factors AND add the cached factors from the boundary
|
||||
boost::shared_ptr<GaussianFactorGraph> factors;
|
||||
|
||||
// BEGIN OF COPIED CODE
|
||||
|
||||
tic("linear_lookup1");
|
||||
// ordering provides all keys in conditionals, there cannot be others because path to root included
|
||||
set<Symbol> affectedKeys;
|
||||
list<Symbol> tmp = affectedBayesNet.ordering();
|
||||
affectedKeys.insert(tmp.begin(), tmp.end());
|
||||
toc("nonlin-mess");
|
||||
|
||||
FactorGraph<GaussianFactor> factors(*relinearizeAffectedFactors(affectedKeys)); // todo: no need to relinearize here, should have cached linearized factors
|
||||
tic("nonlin_relin");
|
||||
factors = relinearizeAffectedFactors(affectedKeys);
|
||||
toc("nonlin_relin");
|
||||
|
||||
lastAffectedMarkedCount = markedKeys.size();
|
||||
lastAffectedVariableCount = affectedKeys.size();
|
||||
lastAffectedFactorCount = factors.size();
|
||||
lastNonlinearMarkedCount = marked.size();
|
||||
lastNonlinearAffectedVariableCount = affectedKeys.size();
|
||||
lastNonlinearAffectedFactorCount = factors->size();
|
||||
|
||||
#ifdef PRINT_STATS
|
||||
// output for generating figures
|
||||
cout << "linear: #markedKeys: " << markedKeys.size() << " #affectedVariables: " << affectedKeys.size()
|
||||
<< " #affectedFactors: " << factors.size() << " maxCliqueSize: " << maxClique
|
||||
<< " avgCliqueSize: " << avgClique << " #Cliques: " << numCliques << " nnzR: " << nnzR << endl;
|
||||
#endif
|
||||
|
||||
toc("linear_lookup1");
|
||||
// cout << "nonlinear: #marked: " << marked.size() << " #affected: " << affectedKeys.size() << " #factors: " << factors->size() << endl;
|
||||
|
||||
// add the cached intermediate results from the boundary of the orphans ...
|
||||
FactorGraph<GaussianFactor> cachedBoundary = getCachedBoundaryFactors(orphans);
|
||||
factors.push_back(cachedBoundary);
|
||||
|
||||
// END OF COPIED CODE
|
||||
|
||||
|
||||
// 2. Add the new factors \Factors' into the resulting factor graph
|
||||
factors.push_back(newFactors);
|
||||
|
||||
// 3. Re-order and eliminate the factor graph into a Bayes net (Algorithm [alg:eliminate]), and re-assemble into a new Bayes tree (Algorithm [alg:BayesTree])
|
||||
|
||||
// create an ordering for the new and contaminated factors
|
||||
// markedKeys are passed in: those variables will be forced to the end in the ordering
|
||||
set<Symbol> markedKeysSet;
|
||||
markedKeysSet.insert(markedKeys.begin(), markedKeys.end());
|
||||
Ordering ordering = factors.getConstrainedOrdering(markedKeysSet); // intelligent ordering
|
||||
// Ordering ordering = factors.getOrdering(); // original ordering, yields in bad performance
|
||||
factors->push_back(cachedBoundary);
|
||||
|
||||
// eliminate into a Bayes net
|
||||
tic("linear_eliminate");
|
||||
BayesNet<Conditional> bayesNet = _eliminate(factors, cached_, ordering);
|
||||
toc("linear_eliminate");
|
||||
tic("nonlin_eliminate");
|
||||
BayesNet<Conditional> bayesNet = _eliminate(*factors, cached_, original_ordering);
|
||||
toc("nonlin_eliminate");
|
||||
|
||||
// Create Index from ordering
|
||||
IndexTable<Symbol> index(ordering);
|
||||
IndexTable<Symbol> index(original_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, index);
|
||||
|
||||
// Save number of affectedCliques
|
||||
lastAffectedCliqueCount = this->size();
|
||||
|
||||
// 4. Insert the orphans back into the new Bayes tree.
|
||||
|
||||
// add orphans to the bottom of the new tree
|
||||
BOOST_FOREACH(sharedClique orphan, orphans) {
|
||||
Symbol parentRepresentative = findParentClique(orphan->separator_, index);
|
||||
|
|
@ -317,179 +452,102 @@ namespace gtsam {
|
|||
parent->children_ += orphan;
|
||||
orphan->parent_ = parent; // set new parent!
|
||||
}
|
||||
#endif
|
||||
|
||||
// Output: BayesTree(this)
|
||||
// Output: updated Bayes tree (this), updated linearization point theta_
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// find all variables that are directly connected by a measurement to one of the marked variables
|
||||
template<class Conditional, class Config>
|
||||
void ISAM2<Conditional, Config>::find_all(sharedClique clique, list<Symbol>& keys, const list<Symbol>& marked) {
|
||||
// does the separator contain any of the variables?
|
||||
bool found = false;
|
||||
BOOST_FOREACH(const Symbol& key, clique->separator_) {
|
||||
if (find(marked.begin(), marked.end(), key) != marked.end())
|
||||
found = true;
|
||||
}
|
||||
if (found) {
|
||||
// then add this clique
|
||||
keys.push_back(clique->keys().front());
|
||||
}
|
||||
BOOST_FOREACH(const sharedClique& child, clique->children_) {
|
||||
find_all(child, keys, marked);
|
||||
}
|
||||
list<Symbol> empty;
|
||||
return empty;
|
||||
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
template<class Conditional, class Config>
|
||||
void ISAM2<Conditional, Config>::update(
|
||||
const NonlinearFactorGraph<Config>& newFactors, const Config& newTheta,
|
||||
double wildfire_threshold, double relinearize_threshold, bool relinearize) {
|
||||
|
||||
lastAffectedVariableCount = 0;
|
||||
lastAffectedFactorCount = 0;
|
||||
lastAffectedCliqueCount = 0;
|
||||
lastAffectedMarkedCount = 0;
|
||||
lastNonlinearMarkedCount = 0;
|
||||
lastNonlinearAffectedVariableCount = 0;
|
||||
lastNonlinearAffectedFactorCount = 0;
|
||||
|
||||
tic("all");
|
||||
|
||||
tic("step1");
|
||||
// 1. Add any new factors \Factors:=\Factors\cup\Factors'.
|
||||
nonlinearFactors_.push_back(newFactors);
|
||||
toc("step1");
|
||||
|
||||
tic("step2");
|
||||
// 2. Initialize any new variables \Theta_{new} and add \Theta:=\Theta\cup\Theta_{new}.
|
||||
theta_.insert(newTheta);
|
||||
toc("step2");
|
||||
|
||||
tic("step3");
|
||||
// 3. Linearize new factor
|
||||
// boost::shared_ptr<GaussianFactorGraph> linearFactors = newFactors.linearize(theta_);
|
||||
toc("step3");
|
||||
|
||||
#if 0 // original algorithm
|
||||
tic("step4");
|
||||
// 4. Linear iSAM step (alg 3)
|
||||
linear_update(*linearFactors); // in: this
|
||||
toc("step4");
|
||||
|
||||
tic("step5");
|
||||
// 5. Calculate Delta (alg 0)
|
||||
delta_ = optimize2(*this, wildfire_threshold);
|
||||
toc("step5");
|
||||
|
||||
tic("step6");
|
||||
// 6. Iterate Algorithm 4 until no more re-linearizations occur
|
||||
if (relinearize) {
|
||||
fluid_relinearization(relinearize_threshold); // in: delta_, theta_, nonlinearFactors_, this
|
||||
}
|
||||
toc("step6");
|
||||
|
||||
/* ************************************************************************* */
|
||||
template<class Conditional, class Config>
|
||||
void ISAM2<Conditional, Config>::fluid_relinearization(double relinearize_threshold) {
|
||||
// todo: not part of algorithm in paper: linearization point and delta_ do not fit... have to update delta again
|
||||
delta_ = optimize2(*this, wildfire_threshold);
|
||||
#else // new algorithm
|
||||
|
||||
// Input: nonlinear factors factors_, linearization point theta_, Bayes tree (this), delta_
|
||||
tic("step4B");
|
||||
// 4B. Mark linear update
|
||||
list<Symbol> markedKeys = newFactors.keys();
|
||||
toc("step4B");
|
||||
|
||||
// 1. Mark variables in \Delta above threshold \beta: J=\{\Delta_{j}\in\Delta|\Delta_{j}\geq\beta\}.
|
||||
list<Symbol> marked;
|
||||
VectorConfig deltaMarked;
|
||||
for (VectorConfig::const_iterator it = delta_.begin(); it!=delta_.end(); it++) {
|
||||
Symbol key = it->first;
|
||||
Vector v = it->second;
|
||||
if (max(abs(v)) >= relinearize_threshold) {
|
||||
marked.push_back(key);
|
||||
deltaMarked.insert(key, v);
|
||||
}
|
||||
}
|
||||
tic("step5B");
|
||||
// 5B. Mark nonlinear update
|
||||
if (relinearize) {
|
||||
list<Symbol> markedRelin = fluid_relinearization(relinearize_threshold); // in: delta_, theta_, nonlinearFactors_, this
|
||||
|
||||
if (marked.size()>0) {
|
||||
|
||||
// 2. Update linearization point for marked variables: \Theta_{J}:=\Theta_{J}+\Delta_{J}.
|
||||
theta_ = theta_.expmap(deltaMarked);
|
||||
|
||||
// 3. Mark all cliques that involve marked variables \Theta_{J} and all their ancestors.
|
||||
|
||||
// mark all cliques that involve marked variables
|
||||
list<Symbol> affectedSymbols(marked); // add all marked
|
||||
tic("nonlin-find_all");
|
||||
find_all(this->root(), affectedSymbols, marked); // add other cliques that have the marked ones in the separator
|
||||
affectedSymbols.sort(); // remove duplicates
|
||||
affectedSymbols.unique();
|
||||
toc("nonlin-find_all");
|
||||
|
||||
// 4. From the leaves to the top, if a clique is marked:
|
||||
// re-linearize the original factors in \Factors associated with the clique,
|
||||
// add the cached marginal factors from its children, and re-eliminate.
|
||||
|
||||
// todo: for simplicity, currently simply remove the top and recreate it using the original ordering
|
||||
|
||||
tic("nonlin-mess");
|
||||
Cliques orphans;
|
||||
BayesNet<GaussianConditional> affectedBayesNet;
|
||||
this->removeTop(affectedSymbols, affectedBayesNet, orphans);
|
||||
// remember original ordering
|
||||
// todo Ordering original_ordering = affectedBayesNet.ordering(); // does not yield original ordering...
|
||||
FactorGraph<GaussianFactor> tmp_factors(affectedBayesNet); // so instead we recalculate an acceptable ordering here
|
||||
Ordering original_ordering = tmp_factors.getOrdering(); // todo - remove multiple lines up to here
|
||||
|
||||
boost::shared_ptr<GaussianFactorGraph> factors;
|
||||
|
||||
// ordering provides all keys in conditionals, there cannot be others because path to root included
|
||||
set<Symbol> affectedKeys;
|
||||
list<Symbol> tmp = affectedBayesNet.ordering();
|
||||
affectedKeys.insert(tmp.begin(), tmp.end());
|
||||
toc("nonlin-mess");
|
||||
|
||||
tic("nonlin_relin");
|
||||
factors = relinearizeAffectedFactors(affectedKeys);
|
||||
toc("nonlin_relin");
|
||||
|
||||
lastNonlinearMarkedCount = marked.size();
|
||||
lastNonlinearAffectedVariableCount = affectedKeys.size();
|
||||
lastNonlinearAffectedFactorCount = factors->size();
|
||||
|
||||
// cout << "nonlinear: #marked: " << marked.size() << " #affected: " << affectedKeys.size() << " #factors: " << factors->size() << endl;
|
||||
|
||||
// add the cached intermediate results from the boundary of the orphans ...
|
||||
FactorGraph<GaussianFactor> cachedBoundary = getCachedBoundaryFactors(orphans);
|
||||
factors->push_back(cachedBoundary);
|
||||
|
||||
// eliminate into a Bayes net
|
||||
tic("nonlin_eliminate");
|
||||
BayesNet<Conditional> bayesNet = _eliminate(*factors, cached_, original_ordering);
|
||||
toc("nonlin_eliminate");
|
||||
|
||||
// Create Index from ordering
|
||||
IndexTable<Symbol> index(original_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, index);
|
||||
|
||||
// add orphans to the bottom of the new tree
|
||||
BOOST_FOREACH(sharedClique orphan, orphans) {
|
||||
Symbol parentRepresentative = findParentClique(orphan->separator_, index);
|
||||
sharedClique parent = (*this)[parentRepresentative];
|
||||
parent->children_ += orphan;
|
||||
orphan->parent_ = parent; // set new parent!
|
||||
}
|
||||
|
||||
// Output: updated Bayes tree (this), updated linearization point theta_
|
||||
}
|
||||
// merge with markedKeys
|
||||
markedKeys.splice(markedKeys.begin(), markedRelin, markedRelin.begin(), markedRelin.end());
|
||||
markedKeys.sort(); // remove duplicates
|
||||
markedKeys.unique();
|
||||
}
|
||||
toc("step5B");
|
||||
|
||||
/* ************************************************************************* */
|
||||
template<class Conditional, class Config>
|
||||
void ISAM2<Conditional, Config>::update(
|
||||
const NonlinearFactorGraph<Config>& newFactors, const Config& newTheta,
|
||||
double wildfire_threshold, double relinearize_threshold, bool relinearize) {
|
||||
tic("step6B");
|
||||
// 6B. Redo top of Bayes tree
|
||||
boost::shared_ptr<GaussianFactorGraph> linearFactors = newFactors.linearize(theta_);
|
||||
recalculate(markedKeys, &(*linearFactors));
|
||||
toc("step6B");
|
||||
|
||||
lastAffectedVariableCount = 0;
|
||||
lastAffectedFactorCount = 0;
|
||||
lastAffectedCliqueCount = 0;
|
||||
lastAffectedMarkedCount = 0;
|
||||
lastNonlinearMarkedCount = 0;
|
||||
lastNonlinearAffectedVariableCount = 0;
|
||||
lastNonlinearAffectedFactorCount = 0;
|
||||
tic("step7B");
|
||||
// 7B. Solve
|
||||
delta_ = optimize2(*this, wildfire_threshold);
|
||||
toc("step7B");
|
||||
|
||||
tic("all");
|
||||
#endif
|
||||
toc("all");
|
||||
|
||||
tic("step1");
|
||||
// 1. Add any new factors \Factors:=\Factors\cup\Factors'.
|
||||
nonlinearFactors_.push_back(newFactors);
|
||||
toc("step1");
|
||||
|
||||
tic("step2");
|
||||
// 2. Initialize any new variables \Theta_{new} and add \Theta:=\Theta\cup\Theta_{new}.
|
||||
theta_.insert(newTheta);
|
||||
toc("step2");
|
||||
|
||||
tic("step3");
|
||||
// 3. Linearize new factor
|
||||
boost::shared_ptr<GaussianFactorGraph> linearFactors = newFactors.linearize(theta_);
|
||||
toc("step3");
|
||||
|
||||
tic("step4");
|
||||
// 4. Linear iSAM step (alg 3)
|
||||
linear_update(*linearFactors); // in: this
|
||||
toc("step4");
|
||||
|
||||
tic("step5");
|
||||
// 5. Calculate Delta (alg 0)
|
||||
delta_ = optimize2(*this, wildfire_threshold);
|
||||
toc("step5");
|
||||
|
||||
tic("step6");
|
||||
// 6. Iterate Algorithm 4 until no more re-linearizations occur
|
||||
if (relinearize) {
|
||||
fluid_relinearization(relinearize_threshold); // in: delta_, theta_, nonlinearFactors_, this
|
||||
}
|
||||
toc("step6");
|
||||
|
||||
// todo: not part of algorithm in paper: linearization point and delta_ do not fit... have to update delta again
|
||||
delta_ = optimize2(*this, wildfire_threshold);
|
||||
|
||||
toc("all");
|
||||
|
||||
tictoc_print();
|
||||
}
|
||||
tictoc_print();
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
||||
|
|
|
|||
|
|
@ -25,75 +25,76 @@
|
|||
|
||||
namespace gtsam {
|
||||
|
||||
typedef SymbolMap<GaussianFactor::shared_ptr> CachedFactors;
|
||||
typedef SymbolMap<GaussianFactor::shared_ptr> CachedFactors;
|
||||
|
||||
template<class Conditional, class Config>
|
||||
class ISAM2: public BayesTree<Conditional> {
|
||||
template<class Conditional, class Config>
|
||||
class ISAM2: public BayesTree<Conditional> {
|
||||
|
||||
protected:
|
||||
protected:
|
||||
|
||||
// current linearization point
|
||||
Config theta_;
|
||||
// current linearization point
|
||||
Config theta_;
|
||||
|
||||
// the linear solution, an update to the estimate in theta
|
||||
VectorConfig delta_;
|
||||
// the linear solution, an update to the estimate in theta
|
||||
VectorConfig delta_;
|
||||
|
||||
// for keeping all original nonlinear factors
|
||||
NonlinearFactorGraph<Config> nonlinearFactors_;
|
||||
// for keeping all original nonlinear factors
|
||||
NonlinearFactorGraph<Config> nonlinearFactors_;
|
||||
|
||||
// cached intermediate results for restarting computation in the middle
|
||||
CachedFactors cached_;
|
||||
// cached intermediate results for restarting computation in the middle
|
||||
CachedFactors cached_;
|
||||
|
||||
public:
|
||||
public:
|
||||
|
||||
/** Create an empty Bayes Tree */
|
||||
ISAM2();
|
||||
/** Create an empty Bayes Tree */
|
||||
ISAM2();
|
||||
|
||||
/** Create a Bayes Tree from a Bayes Net */
|
||||
ISAM2(const NonlinearFactorGraph<Config>& fg, const Ordering& ordering, const Config& config);
|
||||
/** Create a Bayes Tree from a Bayes Net */
|
||||
ISAM2(const NonlinearFactorGraph<Config>& fg, const Ordering& ordering, const Config& config);
|
||||
|
||||
/** Destructor */
|
||||
virtual ~ISAM2() {}
|
||||
/** Destructor */
|
||||
virtual ~ISAM2() {}
|
||||
|
||||
typedef typename BayesTree<Conditional>::sharedClique sharedClique;
|
||||
typedef typename BayesTree<Conditional>::sharedClique sharedClique;
|
||||
|
||||
typedef typename BayesTree<Conditional>::Cliques Cliques;
|
||||
typedef typename BayesTree<Conditional>::Cliques Cliques;
|
||||
|
||||
/**
|
||||
* ISAM2.
|
||||
*/
|
||||
void update(const NonlinearFactorGraph<Config>& newFactors, const Config& newTheta,
|
||||
double wildfire_threshold = 0., double relinearize_threshold = 0., bool relinearize = true);
|
||||
/**
|
||||
* ISAM2.
|
||||
*/
|
||||
void update(const NonlinearFactorGraph<Config>& newFactors, const Config& newTheta,
|
||||
double wildfire_threshold = 0., double relinearize_threshold = 0., bool relinearize = true);
|
||||
|
||||
// needed to create initial estimates
|
||||
const Config getLinearizationPoint() const {return theta_;}
|
||||
// needed to create initial estimates
|
||||
const Config getLinearizationPoint() const {return theta_;}
|
||||
|
||||
// estimate based on incomplete delta (threshold!)
|
||||
const Config calculateEstimate() const {return theta_.expmap(delta_);}
|
||||
// estimate based on incomplete delta (threshold!)
|
||||
const Config calculateEstimate() const {return theta_.expmap(delta_);}
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||||
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||||
// estimate based on full delta (note that this is based on the current linearization point)
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||||
const Config calculateBestEstimate() const {return theta_.expmap(optimize2(*this, 0.));}
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// estimate based on full delta (note that this is based on the current linearization point)
|
||||
const Config calculateBestEstimate() const {return theta_.expmap(optimize2(*this, 0.));}
|
||||
|
||||
const NonlinearFactorGraph<Config>& getFactorsUnsafe() const { return nonlinearFactors_; }
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||||
const NonlinearFactorGraph<Config>& getFactorsUnsafe() const { return nonlinearFactors_; }
|
||||
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||||
size_t lastAffectedVariableCount;
|
||||
size_t lastAffectedFactorCount;
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||||
size_t lastAffectedCliqueCount;
|
||||
size_t lastAffectedMarkedCount;
|
||||
size_t lastNonlinearMarkedCount;
|
||||
size_t lastNonlinearAffectedVariableCount;
|
||||
size_t lastNonlinearAffectedFactorCount;
|
||||
size_t lastAffectedVariableCount;
|
||||
size_t lastAffectedFactorCount;
|
||||
size_t lastAffectedCliqueCount;
|
||||
size_t lastAffectedMarkedCount;
|
||||
size_t lastNonlinearMarkedCount;
|
||||
size_t lastNonlinearAffectedVariableCount;
|
||||
size_t lastNonlinearAffectedFactorCount;
|
||||
|
||||
private:
|
||||
private:
|
||||
|
||||
std::list<size_t> getAffectedFactors(const std::list<Symbol>& keys) const;
|
||||
boost::shared_ptr<GaussianFactorGraph> relinearizeAffectedFactors(const std::set<Symbol>& affectedKeys) const;
|
||||
FactorGraph<GaussianFactor> getCachedBoundaryFactors(Cliques& orphans);
|
||||
std::list<size_t> getAffectedFactors(const std::list<Symbol>& keys) const;
|
||||
boost::shared_ptr<GaussianFactorGraph> relinearizeAffectedFactors(const std::set<Symbol>& affectedKeys) const;
|
||||
FactorGraph<GaussianFactor> getCachedBoundaryFactors(Cliques& orphans);
|
||||
|
||||
void linear_update(const FactorGraph<GaussianFactor>& newFactors);
|
||||
void find_all(sharedClique clique, std::list<Symbol>& keys, const std::list<Symbol>& marked); // helper function
|
||||
void fluid_relinearization(double relinearize_threshold);
|
||||
void recalculate(const std::list<Symbol>& markedKeys, const FactorGraph<GaussianFactor>* newFactors = NULL);
|
||||
void linear_update(const FactorGraph<GaussianFactor>& newFactors);
|
||||
void find_all(sharedClique clique, std::list<Symbol>& keys, const std::list<Symbol>& marked); // helper function
|
||||
std::list<Symbol> fluid_relinearization(double relinearize_threshold);
|
||||
|
||||
}; // ISAM2
|
||||
}; // ISAM2
|
||||
|
||||
} /// namespace gtsam
|
||||
|
|
|
|||
|
|
@ -63,7 +63,7 @@ void optimize2(const GaussianISAM2::sharedClique& clique, double threshold, set<
|
|||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// fast version without threshold
|
||||
// fast full version without threshold
|
||||
void optimize2(const GaussianISAM2::sharedClique& clique, VectorConfig& result) {
|
||||
// parents are assumed to already be solved and available in result
|
||||
GaussianISAM2::Clique::const_reverse_iterator it;
|
||||
|
|
|
|||
Loading…
Reference in New Issue