Improved function findMinimumSpanningTree to cope with general graphs (and added comments)
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0d957084c0
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@ -73,34 +73,41 @@ SDGraph<KEY> toBoostGraph(const G& graph) {
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SDGraph<KEY> g;
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SDGraph<KEY> g;
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typedef typename boost::graph_traits<SDGraph<KEY> >::vertex_descriptor BoostVertex;
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typedef typename boost::graph_traits<SDGraph<KEY> >::vertex_descriptor BoostVertex;
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std::map<KEY, BoostVertex> key2vertex;
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std::map<KEY, BoostVertex> key2vertex;
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BoostVertex v1, v2;
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typename G::const_iterator itFactor;
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typename G::const_iterator itFactor;
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// Loop over the factors
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for(itFactor=graph.begin(); itFactor!=graph.end(); itFactor++) {
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for(itFactor=graph.begin(); itFactor!=graph.end(); itFactor++) {
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if ((*itFactor)->keys().size() > 2)
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throw(std::invalid_argument("toBoostGraph: only support factors with at most two keys"));
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if ((*itFactor)->keys().size() == 1)
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// Ignore factors that are not binary
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if ((*itFactor)->keys().size() != 2)
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continue;
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continue;
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// Cast the factor to the user-specified factor type F
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boost::shared_ptr<F> factor = boost::dynamic_pointer_cast<F>(*itFactor);
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boost::shared_ptr<F> factor = boost::dynamic_pointer_cast<F>(*itFactor);
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// Ignore factors that are not of type F
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if (!factor) continue;
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if (!factor) continue;
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KEY key1 = factor->key1();
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// Retrieve the 2 keys (nodes) the factor (edge) is incident on
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KEY key2 = factor->key2();
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KEY key1 = factor->keys()[0];
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KEY key2 = factor->keys()[1];
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BoostVertex v1, v2;
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// If key1 is a new key, add it to the key2vertex map, else get the corresponding vertex id
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if (key2vertex.find(key1) == key2vertex.end()) {
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if (key2vertex.find(key1) == key2vertex.end()) {
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v1 = add_vertex(key1, g);
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v1 = add_vertex(key1, g);
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key2vertex.insert(std::pair<KEY,KEY>(key1, v1));
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key2vertex.insert(std::pair<KEY,KEY>(key1, v1));
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} else
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} else
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v1 = key2vertex[key1];
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v1 = key2vertex[key1];
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// If key2 is a new key, add it to the key2vertex map, else get the corresponding vertex id
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if (key2vertex.find(key2) == key2vertex.end()) {
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if (key2vertex.find(key2) == key2vertex.end()) {
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v2 = add_vertex(key2, g);
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v2 = add_vertex(key2, g);
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key2vertex.insert(std::pair<KEY,KEY>(key2, v2));
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key2vertex.insert(std::pair<KEY,KEY>(key2, v2));
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} else
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} else
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v2 = key2vertex[key2];
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v2 = key2vertex[key2];
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// Add an edge with weight 1.0
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boost::property<boost::edge_weight_t, double> edge_property(1.0); // assume constant edge weight here
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boost::property<boost::edge_weight_t, double> edge_property(1.0); // assume constant edge weight here
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boost::add_edge(v1, v2, edge_property, g);
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boost::add_edge(v1, v2, edge_property, g);
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}
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}
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@ -222,12 +229,11 @@ boost::shared_ptr<Values> composePoses(const G& graph, const PredecessorMap<KEY>
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return config;
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return config;
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}
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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/* ************************************************************************* */
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template<class G, class KEY, class FACTOR2>
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template<class G, class KEY, class FACTOR2>
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PredecessorMap<KEY> findMinimumSpanningTree(const G& fg) {
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PredecessorMap<KEY> findMinimumSpanningTree(const G& fg) {
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// Convert to a graph that boost understands
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SDGraph<KEY> g = gtsam::toBoostGraph<G, FACTOR2, KEY>(fg);
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SDGraph<KEY> g = gtsam::toBoostGraph<G, FACTOR2, KEY>(fg);
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// find minimum spanning tree
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// find minimum spanning tree
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@ -109,19 +109,6 @@ TEST( Graph, composePoses )
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}
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}
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///* ************************************************************************* */
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///* ************************************************************************* */
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// A linear factor implementing the functions key1 and key2
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// needed for findMinimumSpanningTree
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class Factor2 : public JacobianFactor {
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public:
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/** Construct binary factor */
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Factor2(Key i1, const Matrix& A1, Key i2, const Matrix& A2, const Vector& b,
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const SharedDiagonal& model = SharedDiagonal()) :
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JacobianFactor(i1, A1, i2, A2, b, model) {
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}
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Key key1() const {return keys_[0];}
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Key key2() const {return keys_[1];}
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};
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TEST( GaussianFactorGraph, findMinimumSpanningTree )
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TEST( GaussianFactorGraph, findMinimumSpanningTree )
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{
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{
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@ -130,14 +117,14 @@ TEST( GaussianFactorGraph, findMinimumSpanningTree )
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Vector2 b(0, 0);
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Vector2 b(0, 0);
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const SharedDiagonal model = noiseModel::Diagonal::Sigmas((Vector(2) << 0.5, 0.5));
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const SharedDiagonal model = noiseModel::Diagonal::Sigmas((Vector(2) << 0.5, 0.5));
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using namespace symbol_shorthand;
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using namespace symbol_shorthand;
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g += Factor2(X(1), I, X(2), I, b, model);
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g += JacobianFactor(X(1), I, X(2), I, b, model);
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g += Factor2(X(1), I, X(3), I, b, model);
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g += JacobianFactor(X(1), I, X(3), I, b, model);
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g += Factor2(X(1), I, X(4), I, b, model);
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g += JacobianFactor(X(1), I, X(4), I, b, model);
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g += Factor2(X(2), I, X(3), I, b, model);
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g += JacobianFactor(X(2), I, X(3), I, b, model);
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g += Factor2(X(2), I, X(4), I, b, model);
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g += JacobianFactor(X(2), I, X(4), I, b, model);
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g += Factor2(X(3), I, X(4), I, b, model);
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g += JacobianFactor(X(3), I, X(4), I, b, model);
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PredecessorMap<Key> tree = findMinimumSpanningTree<GaussianFactorGraph, Key, Factor2>(g);
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PredecessorMap<Key> tree = findMinimumSpanningTree<GaussianFactorGraph, Key, JacobianFactor>(g);
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EXPECT_LONGS_EQUAL(tree[X(1)], X(1));
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EXPECT_LONGS_EQUAL(tree[X(1)], X(1));
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EXPECT_LONGS_EQUAL(tree[X(2)], X(1));
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EXPECT_LONGS_EQUAL(tree[X(2)], X(1));
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EXPECT_LONGS_EQUAL(tree[X(3)], X(1));
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EXPECT_LONGS_EQUAL(tree[X(3)], X(1));
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