Added planar graph with easy subtree
Frank Dellaert
parent
07cc95e4c4
commit
730f4a546f
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@ -26,446 +26,558 @@ using namespace std;
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namespace gtsam {
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typedef boost::shared_ptr<NonlinearFactor<VectorConfig> > shared;
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typedef boost::shared_ptr<NonlinearFactor<VectorConfig> > shared;
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/* ************************************************************************* */
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boost::shared_ptr<const ExampleNonlinearFactorGraph> sharedNonlinearFactorGraph() {
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// Create
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boost::shared_ptr<ExampleNonlinearFactorGraph> nlfg(new ExampleNonlinearFactorGraph);
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/* ************************************************************************* */
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boost::shared_ptr<const ExampleNonlinearFactorGraph> sharedNonlinearFactorGraph() {
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// Create
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boost::shared_ptr<ExampleNonlinearFactorGraph> nlfg(
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new ExampleNonlinearFactorGraph);
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// prior on x1
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double sigma1=0.1;
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Vector mu = zero(2);
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shared f1(new Point2Prior(mu, sigma1, "x1"));
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nlfg->push_back(f1);
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// prior on x1
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double sigma1 = 0.1;
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Vector mu = zero(2);
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shared f1(new Point2Prior(mu, sigma1, "x1"));
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nlfg->push_back(f1);
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// odometry between x1 and x2
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double sigma2=0.1;
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Vector z2(2); z2(0) = 1.5 ; z2(1) = 0;
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shared f2(new Simulated2DOdometry(z2, sigma2, "x1", "x2"));
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nlfg->push_back(f2);
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// odometry between x1 and x2
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double sigma2 = 0.1;
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Vector z2(2);
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z2(0) = 1.5;
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z2(1) = 0;
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shared f2(new Simulated2DOdometry(z2, sigma2, "x1", "x2"));
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nlfg->push_back(f2);
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// measurement between x1 and l1
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double sigma3=0.2;
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Vector z3(2); z3(0) = 0. ; z3(1) = -1.;
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shared f3(new Simulated2DMeasurement(z3, sigma3, "x1", "l1"));
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nlfg->push_back(f3);
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// measurement between x1 and l1
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double sigma3 = 0.2;
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Vector z3(2);
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z3(0) = 0.;
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z3(1) = -1.;
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shared f3(new Simulated2DMeasurement(z3, sigma3, "x1", "l1"));
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nlfg->push_back(f3);
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// measurement between x2 and l1
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double sigma4=0.2;
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Vector z4(2); z4(0)= -1.5 ; z4(1) = -1.;
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shared f4(new Simulated2DMeasurement(z4, sigma4, "x2", "l1"));
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nlfg->push_back(f4);
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// measurement between x2 and l1
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double sigma4 = 0.2;
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Vector z4(2);
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z4(0) = -1.5;
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z4(1) = -1.;
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shared f4(new Simulated2DMeasurement(z4, sigma4, "x2", "l1"));
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nlfg->push_back(f4);
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return nlfg;
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}
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/* ************************************************************************* */
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ExampleNonlinearFactorGraph createNonlinearFactorGraph() {
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return *sharedNonlinearFactorGraph();
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}
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/* ************************************************************************* */
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VectorConfig createConfig() {
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VectorConfig c;
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c.insert("x1", Vector_(2, 0.0, 0.0));
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c.insert("x2", Vector_(2, 1.5, 0.0));
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c.insert("l1", Vector_(2, 0.0,-1.0));
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return c;
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}
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/* ************************************************************************* */
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boost::shared_ptr<const VectorConfig> sharedNoisyConfig() {
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boost::shared_ptr<VectorConfig> c(new VectorConfig);
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c->insert("x1", Vector_(2, 0.1, 0.1));
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c->insert("x2", Vector_(2, 1.4, 0.2));
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c->insert("l1", Vector_(2, 0.1,-1.1));
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return c;
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}
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/* ************************************************************************* */
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VectorConfig createNoisyConfig() { return *sharedNoisyConfig();}
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/* ************************************************************************* */
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VectorConfig createCorrectDelta() {
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VectorConfig c;
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c.insert("x1", Vector_(2,-0.1,-0.1));
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c.insert("x2", Vector_(2, 0.1,-0.2));
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c.insert("l1", Vector_(2,-0.1, 0.1));
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return c;
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}
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/* ************************************************************************* */
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VectorConfig createZeroDelta() {
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VectorConfig c;
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c.insert("x1", zero(2));
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c.insert("x2", zero(2));
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c.insert("l1", zero(2));
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return c;
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}
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/* ************************************************************************* */
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GaussianFactorGraph createGaussianFactorGraph()
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{
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Matrix I = eye(2);
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VectorConfig c = createNoisyConfig();
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// Create empty graph
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GaussianFactorGraph fg;
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// linearized prior on x1: c["x1"]+x1=0 i.e. x1=-c["x1"]
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double sigma1 = 0.1;
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Vector b1 = - c["x1"];
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fg.add("x1", I, b1, sigma1);
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// odometry between x1 and x2: x2-x1=[0.2;-0.1]
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double sigma2 = 0.1;
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Vector b2 = Vector_(2,0.2,-0.1);
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fg.add("x1", -I, "x2", I, b2, sigma2);
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// measurement between x1 and l1: l1-x1=[0.0;0.2]
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double sigma3 = 0.2;
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Vector b3 = Vector_(2,0.0,0.2);
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fg.add("x1", -I, "l1", I, b3, sigma3);
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// measurement between x2 and l1: l1-x2=[-0.2;0.3]
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double sigma4 = 0.2;
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Vector b4 = Vector_(2,-0.2,0.3);
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fg.add("x2", -I, "l1", I, b4, sigma4);
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return fg;
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}
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/* ************************************************************************* */
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/** create small Chordal Bayes Net x <- y
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* x y d
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* 1 1 9
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* 1 5
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*/
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GaussianBayesNet createSmallGaussianBayesNet()
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{
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Matrix R11 = Matrix_(1,1,1.0), S12 = Matrix_(1,1,1.0);
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Matrix R22 = Matrix_(1,1,1.0);
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Vector d1(1), d2(1);
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d1(0) = 9; d2(0) = 5;
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Vector tau(1); tau(0) = 1.0;
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// define nodes and specify in reverse topological sort (i.e. parents last)
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GaussianConditional::shared_ptr
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Px_y(new GaussianConditional("x",d1,R11,"y",S12,tau)),
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Py(new GaussianConditional("y",d2,R22,tau));
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GaussianBayesNet cbn;
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cbn.push_back(Px_y);
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cbn.push_back(Py);
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return cbn;
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}
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/* ************************************************************************* */
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// Some nonlinear functions to optimize
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/* ************************************************************************* */
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namespace smallOptimize {
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Vector h(const Vector& v) {
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double x = v(0);
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return Vector_(2,cos(x),sin(x));
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};
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Matrix H(const Vector& v) {
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double x = v(0);
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return Matrix_(2,1,-sin(x),cos(x));
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};
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}
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/* ************************************************************************* */
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boost::shared_ptr<const ExampleNonlinearFactorGraph> sharedReallyNonlinearFactorGraph()
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{
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boost::shared_ptr<ExampleNonlinearFactorGraph> fg(new ExampleNonlinearFactorGraph);
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Vector z = Vector_(2,1.0,0.0);
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double sigma = 0.1;
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boost::shared_ptr<NonlinearFactor1>
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factor(new NonlinearFactor1(z,sigma,&smallOptimize::h,"x",&smallOptimize::H));
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fg->push_back(factor);
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return fg;
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}
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ExampleNonlinearFactorGraph createReallyNonlinearFactorGraph() {
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return *sharedReallyNonlinearFactorGraph();
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}
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/* ************************************************************************* */
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pair<ExampleNonlinearFactorGraph, VectorConfig> createNonlinearSmoother(int T) {
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// noise on measurements and odometry, respectively
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double sigma1 = 1, sigma2 = 1;
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// Create
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ExampleNonlinearFactorGraph nlfg;
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VectorConfig poses;
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// prior on x1
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Vector x1 = Vector_(2,1.0,0.0);
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string key1 = symbol('x', 1);
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shared prior(new Point2Prior(x1, sigma1, key1));
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nlfg.push_back(prior);
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poses.insert(key1, x1);
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for (int t = 2; t <= T; t++) {
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// odometry between x_t and x_{t-1}
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Vector odo = Vector_(2, 1.0, 0.0);
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string key = symbol('x', t);
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shared odometry(new Simulated2DOdometry(odo, sigma2, symbol('x', t - 1), key));
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nlfg.push_back(odometry);
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// measurement on x_t is like perfect GPS
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Vector xt = Vector_(2, (double)t, 0.0);
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shared measurement(new Point2Prior(xt, sigma1, key));
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nlfg.push_back(measurement);
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// initial estimate
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poses.insert(key, xt);
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return nlfg;
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}
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return make_pair(nlfg, poses);
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}
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/* ************************************************************************* */
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ExampleNonlinearFactorGraph createNonlinearFactorGraph() {
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return *sharedNonlinearFactorGraph();
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}
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/* ************************************************************************* */
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VectorConfig createConfig() {
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VectorConfig c;
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c.insert("x1", Vector_(2, 0.0, 0.0));
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c.insert("x2", Vector_(2, 1.5, 0.0));
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c.insert("l1", Vector_(2, 0.0, -1.0));
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return c;
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}
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/* ************************************************************************* */
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boost::shared_ptr<const VectorConfig> sharedNoisyConfig() {
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boost::shared_ptr<VectorConfig> c(new VectorConfig);
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c->insert("x1", Vector_(2, 0.1, 0.1));
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c->insert("x2", Vector_(2, 1.4, 0.2));
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c->insert("l1", Vector_(2, 0.1, -1.1));
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return c;
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}
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/* ************************************************************************* */
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VectorConfig createNoisyConfig() {
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return *sharedNoisyConfig();
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}
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/* ************************************************************************* */
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VectorConfig createCorrectDelta() {
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VectorConfig c;
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c.insert("x1", Vector_(2, -0.1, -0.1));
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c.insert("x2", Vector_(2, 0.1, -0.2));
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c.insert("l1", Vector_(2, -0.1, 0.1));
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return c;
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}
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/* ************************************************************************* */
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VectorConfig createZeroDelta() {
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VectorConfig c;
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c.insert("x1", zero(2));
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c.insert("x2", zero(2));
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c.insert("l1", zero(2));
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return c;
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}
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/* ************************************************************************* */
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GaussianFactorGraph createGaussianFactorGraph() {
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Matrix I = eye(2);
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VectorConfig c = createNoisyConfig();
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// Create empty graph
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GaussianFactorGraph fg;
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// linearized prior on x1: c["x1"]+x1=0 i.e. x1=-c["x1"]
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double sigma1 = 0.1;
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Vector b1 = -c["x1"];
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fg.add("x1", I, b1, sigma1);
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// odometry between x1 and x2: x2-x1=[0.2;-0.1]
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double sigma2 = 0.1;
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Vector b2 = Vector_(2, 0.2, -0.1);
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fg.add("x1", -I, "x2", I, b2, sigma2);
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// measurement between x1 and l1: l1-x1=[0.0;0.2]
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double sigma3 = 0.2;
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Vector b3 = Vector_(2, 0.0, 0.2);
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fg.add("x1", -I, "l1", I, b3, sigma3);
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// measurement between x2 and l1: l1-x2=[-0.2;0.3]
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double sigma4 = 0.2;
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Vector b4 = Vector_(2, -0.2, 0.3);
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fg.add("x2", -I, "l1", I, b4, sigma4);
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return fg;
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}
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/* ************************************************************************* */
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/** create small Chordal Bayes Net x <- y
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* x y d
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* 1 1 9
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* 1 5
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*/
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GaussianBayesNet createSmallGaussianBayesNet() {
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Matrix R11 = Matrix_(1, 1, 1.0), S12 = Matrix_(1, 1, 1.0);
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Matrix R22 = Matrix_(1, 1, 1.0);
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Vector d1(1), d2(1);
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d1(0) = 9;
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d2(0) = 5;
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Vector tau(1);
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tau(0) = 1.0;
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// define nodes and specify in reverse topological sort (i.e. parents last)
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GaussianConditional::shared_ptr Px_y(new GaussianConditional("x", d1, R11,
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"y", S12, tau)), Py(new GaussianConditional("y", d2, R22, tau));
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GaussianBayesNet cbn;
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cbn.push_back(Px_y);
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cbn.push_back(Py);
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return cbn;
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}
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/* ************************************************************************* */
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// Some nonlinear functions to optimize
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/* ************************************************************************* */
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namespace smallOptimize {
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Vector h(const Vector& v) {
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double x = v(0);
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return Vector_(2, cos(x), sin(x));
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}
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;
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Matrix H(const Vector& v) {
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double x = v(0);
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return Matrix_(2, 1, -sin(x), cos(x));
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}
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;
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}
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/* ************************************************************************* */
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boost::shared_ptr<const ExampleNonlinearFactorGraph> sharedReallyNonlinearFactorGraph() {
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boost::shared_ptr<ExampleNonlinearFactorGraph> fg(
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new ExampleNonlinearFactorGraph);
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Vector z = Vector_(2, 1.0, 0.0);
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double sigma = 0.1;
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boost::shared_ptr<NonlinearFactor1> factor(new NonlinearFactor1(z, sigma,
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&smallOptimize::h, "x", &smallOptimize::H));
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fg->push_back(factor);
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return fg;
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}
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ExampleNonlinearFactorGraph createReallyNonlinearFactorGraph() {
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return *sharedReallyNonlinearFactorGraph();
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}
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/* ************************************************************************* */
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pair<ExampleNonlinearFactorGraph, VectorConfig> createNonlinearSmoother(int T) {
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// noise on measurements and odometry, respectively
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double sigma1 = 1, sigma2 = 1;
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// Create
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ExampleNonlinearFactorGraph nlfg;
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VectorConfig poses;
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// prior on x1
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Vector x1 = Vector_(2, 1.0, 0.0);
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string key1 = symbol('x', 1);
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shared prior(new Point2Prior(x1, sigma1, key1));
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nlfg.push_back(prior);
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poses.insert(key1, x1);
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for (int t = 2; t <= T; t++) {
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// odometry between x_t and x_{t-1}
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Vector odo = Vector_(2, 1.0, 0.0);
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string key = symbol('x', t);
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shared odometry(new Simulated2DOdometry(odo, sigma2, symbol('x', t - 1),
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key));
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nlfg.push_back(odometry);
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// measurement on x_t is like perfect GPS
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Vector xt = Vector_(2, (double) t, 0.0);
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shared measurement(new Point2Prior(xt, sigma1, key));
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nlfg.push_back(measurement);
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// initial estimate
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poses.insert(key, xt);
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}
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return make_pair(nlfg, poses);
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}
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/* ************************************************************************* */
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GaussianFactorGraph createSmoother(int T) {
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ExampleNonlinearFactorGraph nlfg;
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VectorConfig poses;
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boost::tie(nlfg, poses) = createNonlinearSmoother(T);
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GaussianFactorGraph lfg = nlfg.linearize(poses);
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return lfg;
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}
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/* ************************************************************************* */
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GaussianFactorGraph createSimpleConstraintGraph() {
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// create unary factor
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// prior on "x", mean = [1,-1], sigma=0.1
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double sigma = 0.1;
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Matrix Ax = eye(2);
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Vector b1(2);
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b1(0) = 1.0;
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b1(1) = -1.0;
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GaussianFactor::shared_ptr f1(new GaussianFactor("x", Ax, b1, sigma));
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// create binary constraint factor
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// between "x" and "y", that is going to be the only factor on "y"
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// |1 0||x_1| + |-1 0||y_1| = |0|
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// |0 1||x_2| | 0 -1||y_2| |0|
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Matrix Ax1 = eye(2);
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Matrix Ay1 = eye(2) * -1;
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Vector b2 = Vector_(2, 0.0, 0.0);
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GaussianFactor::shared_ptr f2(new GaussianFactor("x", Ax1, "y", Ay1, b2,
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0.0));
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// construct the graph
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GaussianFactorGraph fg;
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fg.push_back(f1);
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fg.push_back(f2);
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return fg;
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}
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/* ************************************************************************* */
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VectorConfig createSimpleConstraintConfig() {
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VectorConfig config;
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Vector v = Vector_(2, 1.0, -1.0);
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config.insert("x", v);
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config.insert("y", v);
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return config;
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}
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/* ************************************************************************* */
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GaussianFactorGraph createSingleConstraintGraph() {
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// create unary factor
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// prior on "x", mean = [1,-1], sigma=0.1
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double sigma = 0.1;
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Matrix Ax = eye(2);
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Vector b1(2);
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b1(0) = 1.0;
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b1(1) = -1.0;
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GaussianFactor::shared_ptr f1(new GaussianFactor("x", Ax, b1, sigma));
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// create binary constraint factor
|
||||
// between "x" and "y", that is going to be the only factor on "y"
|
||||
// |1 2||x_1| + |10 0||y_1| = |1|
|
||||
// |2 1||x_2| |0 10||y_2| |2|
|
||||
Matrix Ax1(2, 2);
|
||||
Ax1(0, 0) = 1.0;
|
||||
Ax1(0, 1) = 2.0;
|
||||
Ax1(1, 0) = 2.0;
|
||||
Ax1(1, 1) = 1.0;
|
||||
Matrix Ay1 = eye(2) * 10;
|
||||
Vector b2 = Vector_(2, 1.0, 2.0);
|
||||
GaussianFactor::shared_ptr f2(new GaussianFactor("x", Ax1, "y", Ay1, b2,
|
||||
0.0));
|
||||
|
||||
// construct the graph
|
||||
GaussianFactorGraph fg;
|
||||
fg.push_back(f1);
|
||||
fg.push_back(f2);
|
||||
|
||||
return fg;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
VectorConfig createSingleConstraintConfig() {
|
||||
VectorConfig config;
|
||||
config.insert("x", Vector_(2, 1.0, -1.0));
|
||||
config.insert("y", Vector_(2, 0.2, 0.1));
|
||||
return config;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
GaussianFactorGraph createMultiConstraintGraph() {
|
||||
// unary factor 1
|
||||
double sigma = 0.1;
|
||||
Matrix A = eye(2);
|
||||
Vector b = Vector_(2, -2.0, 2.0);
|
||||
GaussianFactor::shared_ptr lf1(new GaussianFactor("x", A, b, sigma));
|
||||
|
||||
// constraint 1
|
||||
Matrix A11(2, 2);
|
||||
A11(0, 0) = 1.0;
|
||||
A11(0, 1) = 2.0;
|
||||
A11(1, 0) = 2.0;
|
||||
A11(1, 1) = 1.0;
|
||||
|
||||
Matrix A12(2, 2);
|
||||
A12(0, 0) = 10.0;
|
||||
A12(0, 1) = 0.0;
|
||||
A12(1, 0) = 0.0;
|
||||
A12(1, 1) = 10.0;
|
||||
|
||||
Vector b1(2);
|
||||
b1(0) = 1.0;
|
||||
b1(1) = 2.0;
|
||||
GaussianFactor::shared_ptr lc1(new GaussianFactor("x", A11, "y", A12, b1,
|
||||
0.0));
|
||||
|
||||
// constraint 2
|
||||
Matrix A21(2, 2);
|
||||
A21(0, 0) = 3.0;
|
||||
A21(0, 1) = 4.0;
|
||||
A21(1, 0) = -1.0;
|
||||
A21(1, 1) = -2.0;
|
||||
|
||||
Matrix A22(2, 2);
|
||||
A22(0, 0) = 1.0;
|
||||
A22(0, 1) = 1.0;
|
||||
A22(1, 0) = 1.0;
|
||||
A22(1, 1) = 2.0;
|
||||
|
||||
Vector b2(2);
|
||||
b2(0) = 3.0;
|
||||
b2(1) = 4.0;
|
||||
GaussianFactor::shared_ptr lc2(new GaussianFactor("x", A21, "z", A22, b2,
|
||||
0.0));
|
||||
|
||||
// construct the graph
|
||||
GaussianFactorGraph fg;
|
||||
fg.push_back(lf1);
|
||||
fg.push_back(lc1);
|
||||
fg.push_back(lc2);
|
||||
|
||||
return fg;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
VectorConfig createMultiConstraintConfig() {
|
||||
VectorConfig config;
|
||||
config.insert("x", Vector_(2, -2.0, 2.0));
|
||||
config.insert("y", Vector_(2, -0.1, 0.4));
|
||||
config.insert("z", Vector_(2, -4.0, 5.0));
|
||||
return config;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
//GaussianFactorGraph createConstrainedGaussianFactorGraph()
|
||||
//{
|
||||
// GaussianFactorGraph graph;
|
||||
//
|
||||
// // add an equality factor
|
||||
// Vector v1(2); v1(0)=1.;v1(1)=2.;
|
||||
// GaussianFactor::shared_ptr f1(new GaussianFactor(v1, "x0"));
|
||||
// graph.push_back_eq(f1);
|
||||
//
|
||||
// // add a normal linear factor
|
||||
// Matrix A21 = -1 * eye(2);
|
||||
//
|
||||
// Matrix A22 = eye(2);
|
||||
//
|
||||
// Vector b(2);
|
||||
// b(0) = 2 ; b(1) = 3;
|
||||
//
|
||||
// double sigma = 0.1;
|
||||
// GaussianFactor::shared_ptr f2(new GaussianFactor("x0", A21/sigma, "x1", A22/sigma, b/sigma));
|
||||
// graph.push_back(f2);
|
||||
// return graph;
|
||||
//}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// ConstrainedNonlinearFactorGraph<NonlinearFactor<VectorConfig> , VectorConfig> createConstrainedNonlinearFactorGraph() {
|
||||
// ConstrainedNonlinearFactorGraph<NonlinearFactor<VectorConfig> , VectorConfig> graph;
|
||||
// VectorConfig c = createConstrainedConfig();
|
||||
//
|
||||
// // equality constraint for initial pose
|
||||
// GaussianFactor::shared_ptr f1(new GaussianFactor(c["x0"], "x0"));
|
||||
// graph.push_back_eq(f1);
|
||||
//
|
||||
// // odometry between x0 and x1
|
||||
// double sigma = 0.1;
|
||||
// shared f2(new Simulated2DOdometry(c["x1"] - c["x0"], sigma, "x0", "x1"));
|
||||
// graph.push_back(f2); // TODO
|
||||
// return graph;
|
||||
// }
|
||||
|
||||
/* ************************************************************************* */
|
||||
//VectorConfig createConstrainedConfig()
|
||||
//{
|
||||
// VectorConfig config;
|
||||
//
|
||||
// Vector x0(2); x0(0)=1.0; x0(1)=2.0;
|
||||
// config.insert("x0", x0);
|
||||
//
|
||||
// Vector x1(2); x1(0)=3.0; x1(1)=5.0;
|
||||
// config.insert("x1", x1);
|
||||
//
|
||||
// return config;
|
||||
//}
|
||||
|
||||
/* ************************************************************************* */
|
||||
//VectorConfig createConstrainedLinConfig()
|
||||
//{
|
||||
// VectorConfig config;
|
||||
//
|
||||
// Vector x0(2); x0(0)=1.0; x0(1)=2.0; // value doesn't actually matter
|
||||
// config.insert("x0", x0);
|
||||
//
|
||||
// Vector x1(2); x1(0)=2.3; x1(1)=5.3;
|
||||
// config.insert("x1", x1);
|
||||
//
|
||||
// return config;
|
||||
//}
|
||||
|
||||
/* ************************************************************************* */
|
||||
//VectorConfig createConstrainedCorrectDelta()
|
||||
//{
|
||||
// VectorConfig config;
|
||||
//
|
||||
// Vector x0(2); x0(0)=0.; x0(1)=0.;
|
||||
// config.insert("x0", x0);
|
||||
//
|
||||
// Vector x1(2); x1(0)= 0.7; x1(1)= -0.3;
|
||||
// config.insert("x1", x1);
|
||||
//
|
||||
// return config;
|
||||
//}
|
||||
|
||||
/* ************************************************************************* */
|
||||
//ConstrainedGaussianBayesNet createConstrainedGaussianBayesNet()
|
||||
//{
|
||||
// ConstrainedGaussianBayesNet cbn;
|
||||
// VectorConfig c = createConstrainedConfig();
|
||||
//
|
||||
// // add regular conditional gaussian - no parent
|
||||
// Matrix R = eye(2);
|
||||
// Vector d = c["x1"];
|
||||
// double sigma = 0.1;
|
||||
// GaussianConditional::shared_ptr f1(new GaussianConditional(d/sigma, R/sigma));
|
||||
// cbn.insert("x1", f1);
|
||||
//
|
||||
// // add a delta function to the cbn
|
||||
// ConstrainedGaussianConditional::shared_ptr f2(new ConstrainedGaussianConditional); //(c["x0"], "x0"));
|
||||
// cbn.insert_df("x0", f2);
|
||||
//
|
||||
// return cbn;
|
||||
//}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// Create key for simulated planar graph
|
||||
string key(int x, int y) {
|
||||
stringstream ss;
|
||||
ss << "x" << x << y;
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
pair<GaussianFactorGraph, VectorConfig> planarGraph(size_t N) {
|
||||
|
||||
// create empty graph
|
||||
NonlinearFactorGraph<VectorConfig> nlfg;
|
||||
|
||||
// Create almost hard constraint on x11, sigma=0 will work for PCG not for normal
|
||||
double sigma0 = 1e-3;
|
||||
shared constraint(new Point2Prior(Vector_(2, 1.0, 1.0), sigma0, "x11"));
|
||||
nlfg.push_back(constraint);
|
||||
|
||||
double sigma = 0.01;
|
||||
|
||||
// Create horizontal constraints, 1...N*(N-1)
|
||||
Vector z1 = Vector_(2, 1.0, 0.0); // move right
|
||||
for (size_t x = 1; x < N; x++)
|
||||
for (size_t y = 1; y <= N; y++) {
|
||||
shared f(new Simulated2DOdometry(z1, sigma, key(x, y), key(x + 1, y)));
|
||||
nlfg.push_back(f);
|
||||
}
|
||||
|
||||
// Create vertical constraints, N*(N-1)+1..2*N*(N-1)
|
||||
Vector z2 = Vector_(2, 0.0, 1.0); // move up
|
||||
for (size_t x = 1; x <= N; x++)
|
||||
for (size_t y = 1; y < N; y++) {
|
||||
shared f(new Simulated2DOdometry(z2, sigma, key(x, y), key(x, y + 1)));
|
||||
nlfg.push_back(f);
|
||||
}
|
||||
|
||||
// Create linearization and ground xtrue config
|
||||
VectorConfig zeros, xtrue;
|
||||
for (size_t x = 1; x <= N; x++)
|
||||
for (size_t y = 1; y <= N; y++) {
|
||||
zeros.add(key(x, y), zero(2));
|
||||
xtrue.add(key(x, y), Vector_(2, (double) x, double(y)));
|
||||
}
|
||||
|
||||
// linearize around zero
|
||||
GaussianFactorGraph A = nlfg.linearize(zeros);
|
||||
|
||||
return make_pair(A, xtrue);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
Ordering planarOrdering(size_t N) {
|
||||
Ordering ordering;
|
||||
for (size_t y = N; y >= 1; y--)
|
||||
for (size_t x = N; x >= 1; x--)
|
||||
ordering.push_back(key(x, y));
|
||||
return ordering;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
pair<GaussianFactorGraph, GaussianFactorGraph> splitOffPlanarTree(size_t N,
|
||||
const GaussianFactorGraph& original) {
|
||||
GaussianFactorGraph T, C;
|
||||
|
||||
// Add the x11 constraint to the tree
|
||||
T.push_back(original[0]);
|
||||
|
||||
// Add all horizontal constraints to the tree
|
||||
size_t i = 1;
|
||||
for (size_t x = 1; x < N; x++)
|
||||
for (size_t y = 1; y <= N; y++, i++)
|
||||
T.push_back(original[i]);
|
||||
|
||||
// Add first vertical column of constraints to T, others to C
|
||||
for (size_t x = 1; x <= N; x++)
|
||||
for (size_t y = 1; y < N; y++, i++)
|
||||
if (x == 1)
|
||||
T.push_back(original[i]);
|
||||
else
|
||||
C.push_back(original[i]);
|
||||
|
||||
return make_pair(T, C);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
GaussianFactorGraph createSmoother(int T) {
|
||||
ExampleNonlinearFactorGraph nlfg;
|
||||
VectorConfig poses;
|
||||
boost::tie(nlfg, poses) = createNonlinearSmoother(T);
|
||||
|
||||
GaussianFactorGraph lfg = nlfg.linearize(poses);
|
||||
return lfg;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
GaussianFactorGraph createSimpleConstraintGraph() {
|
||||
// create unary factor
|
||||
// prior on "x", mean = [1,-1], sigma=0.1
|
||||
double sigma = 0.1;
|
||||
Matrix Ax = eye(2);
|
||||
Vector b1(2);
|
||||
b1(0) = 1.0;
|
||||
b1(1) = -1.0;
|
||||
GaussianFactor::shared_ptr f1(new GaussianFactor("x", Ax, b1, sigma));
|
||||
|
||||
// create binary constraint factor
|
||||
// between "x" and "y", that is going to be the only factor on "y"
|
||||
// |1 0||x_1| + |-1 0||y_1| = |0|
|
||||
// |0 1||x_2| | 0 -1||y_2| |0|
|
||||
Matrix Ax1 = eye(2);
|
||||
Matrix Ay1 = eye(2) * -1;
|
||||
Vector b2 = Vector_(2, 0.0, 0.0);
|
||||
GaussianFactor::shared_ptr f2(
|
||||
new GaussianFactor("x", Ax1, "y", Ay1, b2, 0.0));
|
||||
|
||||
// construct the graph
|
||||
GaussianFactorGraph fg;
|
||||
fg.push_back(f1);
|
||||
fg.push_back(f2);
|
||||
|
||||
return fg;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
VectorConfig createSimpleConstraintConfig() {
|
||||
VectorConfig config;
|
||||
Vector v = Vector_(2, 1.0, -1.0);
|
||||
config.insert("x", v);
|
||||
config.insert("y", v);
|
||||
return config;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
GaussianFactorGraph createSingleConstraintGraph() {
|
||||
// create unary factor
|
||||
// prior on "x", mean = [1,-1], sigma=0.1
|
||||
double sigma = 0.1;
|
||||
Matrix Ax = eye(2);
|
||||
Vector b1(2);
|
||||
b1(0) = 1.0;
|
||||
b1(1) = -1.0;
|
||||
GaussianFactor::shared_ptr f1(new GaussianFactor("x", Ax, b1, sigma));
|
||||
|
||||
// create binary constraint factor
|
||||
// between "x" and "y", that is going to be the only factor on "y"
|
||||
// |1 2||x_1| + |10 0||y_1| = |1|
|
||||
// |2 1||x_2| |0 10||y_2| |2|
|
||||
Matrix Ax1(2, 2);
|
||||
Ax1(0, 0) = 1.0; Ax1(0, 1) = 2.0;
|
||||
Ax1(1, 0) = 2.0; Ax1(1, 1) = 1.0;
|
||||
Matrix Ay1 = eye(2) * 10;
|
||||
Vector b2 = Vector_(2, 1.0, 2.0);
|
||||
GaussianFactor::shared_ptr f2(
|
||||
new GaussianFactor("x", Ax1, "y", Ay1, b2, 0.0));
|
||||
|
||||
// construct the graph
|
||||
GaussianFactorGraph fg;
|
||||
fg.push_back(f1);
|
||||
fg.push_back(f2);
|
||||
|
||||
return fg;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
VectorConfig createSingleConstraintConfig() {
|
||||
VectorConfig config;
|
||||
config.insert("x", Vector_(2, 1.0, -1.0));
|
||||
config.insert("y", Vector_(2, 0.2, 0.1));
|
||||
return config;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
GaussianFactorGraph createMultiConstraintGraph() {
|
||||
// unary factor 1
|
||||
double sigma = 0.1;
|
||||
Matrix A = eye(2);
|
||||
Vector b = Vector_(2, -2.0, 2.0);
|
||||
GaussianFactor::shared_ptr lf1(new GaussianFactor("x", A, b, sigma));
|
||||
|
||||
// constraint 1
|
||||
Matrix A11(2,2);
|
||||
A11(0,0) = 1.0 ; A11(0,1) = 2.0;
|
||||
A11(1,0) = 2.0 ; A11(1,1) = 1.0;
|
||||
|
||||
Matrix A12(2,2);
|
||||
A12(0,0) = 10.0 ; A12(0,1) = 0.0;
|
||||
A12(1,0) = 0.0 ; A12(1,1) = 10.0;
|
||||
|
||||
Vector b1(2);
|
||||
b1(0) = 1.0; b1(1) = 2.0;
|
||||
GaussianFactor::shared_ptr lc1(new GaussianFactor("x", A11, "y", A12, b1, 0.0));
|
||||
|
||||
// constraint 2
|
||||
Matrix A21(2,2);
|
||||
A21(0,0) = 3.0 ; A21(0,1) = 4.0;
|
||||
A21(1,0) = -1.0 ; A21(1,1) = -2.0;
|
||||
|
||||
Matrix A22(2,2);
|
||||
A22(0,0) = 1.0 ; A22(0,1) = 1.0;
|
||||
A22(1,0) = 1.0 ; A22(1,1) = 2.0;
|
||||
|
||||
Vector b2(2);
|
||||
b2(0) = 3.0; b2(1) = 4.0;
|
||||
GaussianFactor::shared_ptr lc2(new GaussianFactor("x", A21, "z", A22, b2, 0.0));
|
||||
|
||||
// construct the graph
|
||||
GaussianFactorGraph fg;
|
||||
fg.push_back(lf1);
|
||||
fg.push_back(lc1);
|
||||
fg.push_back(lc2);
|
||||
|
||||
return fg;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
VectorConfig createMultiConstraintConfig() {
|
||||
VectorConfig config;
|
||||
config.insert("x", Vector_(2, -2.0, 2.0));
|
||||
config.insert("y", Vector_(2, -0.1, 0.4));
|
||||
config.insert("z", Vector_(2, -4.0, 5.0));
|
||||
return config;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
//GaussianFactorGraph createConstrainedGaussianFactorGraph()
|
||||
//{
|
||||
// GaussianFactorGraph graph;
|
||||
//
|
||||
// // add an equality factor
|
||||
// Vector v1(2); v1(0)=1.;v1(1)=2.;
|
||||
// GaussianFactor::shared_ptr f1(new GaussianFactor(v1, "x0"));
|
||||
// graph.push_back_eq(f1);
|
||||
//
|
||||
// // add a normal linear factor
|
||||
// Matrix A21 = -1 * eye(2);
|
||||
//
|
||||
// Matrix A22 = eye(2);
|
||||
//
|
||||
// Vector b(2);
|
||||
// b(0) = 2 ; b(1) = 3;
|
||||
//
|
||||
// double sigma = 0.1;
|
||||
// GaussianFactor::shared_ptr f2(new GaussianFactor("x0", A21/sigma, "x1", A22/sigma, b/sigma));
|
||||
// graph.push_back(f2);
|
||||
// return graph;
|
||||
//}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// ConstrainedNonlinearFactorGraph<NonlinearFactor<VectorConfig> , VectorConfig> createConstrainedNonlinearFactorGraph() {
|
||||
// ConstrainedNonlinearFactorGraph<NonlinearFactor<VectorConfig> , VectorConfig> graph;
|
||||
// VectorConfig c = createConstrainedConfig();
|
||||
//
|
||||
// // equality constraint for initial pose
|
||||
// GaussianFactor::shared_ptr f1(new GaussianFactor(c["x0"], "x0"));
|
||||
// graph.push_back_eq(f1);
|
||||
//
|
||||
// // odometry between x0 and x1
|
||||
// double sigma = 0.1;
|
||||
// shared f2(new Simulated2DOdometry(c["x1"] - c["x0"], sigma, "x0", "x1"));
|
||||
// graph.push_back(f2); // TODO
|
||||
// return graph;
|
||||
// }
|
||||
|
||||
/* ************************************************************************* */
|
||||
//VectorConfig createConstrainedConfig()
|
||||
//{
|
||||
// VectorConfig config;
|
||||
//
|
||||
// Vector x0(2); x0(0)=1.0; x0(1)=2.0;
|
||||
// config.insert("x0", x0);
|
||||
//
|
||||
// Vector x1(2); x1(0)=3.0; x1(1)=5.0;
|
||||
// config.insert("x1", x1);
|
||||
//
|
||||
// return config;
|
||||
//}
|
||||
|
||||
/* ************************************************************************* */
|
||||
//VectorConfig createConstrainedLinConfig()
|
||||
//{
|
||||
// VectorConfig config;
|
||||
//
|
||||
// Vector x0(2); x0(0)=1.0; x0(1)=2.0; // value doesn't actually matter
|
||||
// config.insert("x0", x0);
|
||||
//
|
||||
// Vector x1(2); x1(0)=2.3; x1(1)=5.3;
|
||||
// config.insert("x1", x1);
|
||||
//
|
||||
// return config;
|
||||
//}
|
||||
|
||||
/* ************************************************************************* */
|
||||
//VectorConfig createConstrainedCorrectDelta()
|
||||
//{
|
||||
// VectorConfig config;
|
||||
//
|
||||
// Vector x0(2); x0(0)=0.; x0(1)=0.;
|
||||
// config.insert("x0", x0);
|
||||
//
|
||||
// Vector x1(2); x1(0)= 0.7; x1(1)= -0.3;
|
||||
// config.insert("x1", x1);
|
||||
//
|
||||
// return config;
|
||||
//}
|
||||
|
||||
/* ************************************************************************* */
|
||||
//ConstrainedGaussianBayesNet createConstrainedGaussianBayesNet()
|
||||
//{
|
||||
// ConstrainedGaussianBayesNet cbn;
|
||||
// VectorConfig c = createConstrainedConfig();
|
||||
//
|
||||
// // add regular conditional gaussian - no parent
|
||||
// Matrix R = eye(2);
|
||||
// Vector d = c["x1"];
|
||||
// double sigma = 0.1;
|
||||
// GaussianConditional::shared_ptr f1(new GaussianConditional(d/sigma, R/sigma));
|
||||
// cbn.insert("x1", f1);
|
||||
//
|
||||
// // add a delta function to the cbn
|
||||
// ConstrainedGaussianConditional::shared_ptr f2(new ConstrainedGaussianConditional); //(c["x0"], "x0"));
|
||||
// cbn.insert_df("x0", f2);
|
||||
//
|
||||
// return cbn;
|
||||
//}
|
||||
|
||||
} // namespace gtsam
|
||||
|
|
|
|||
|
|
@ -130,4 +130,38 @@ namespace gtsam {
|
|||
*/
|
||||
// ConstrainedNonlinearFactorGraph<NonlinearFactor<VectorConfig>,VectorConfig>
|
||||
// createConstrainedNonlinearFactorGraph();
|
||||
}
|
||||
|
||||
/* ******************************************************* */
|
||||
// Planar graph with easy subtree for SubgraphPreconditioner
|
||||
/* ******************************************************* */
|
||||
|
||||
/*
|
||||
* Create factor graph with N^2 nodes, for example for N=3
|
||||
* x13-x23-x33
|
||||
* | | |
|
||||
* x12-x22-x32
|
||||
* | | |
|
||||
* -x11-x21-x31
|
||||
* with x11 clamped at (1,1), and others related by 2D odometry.
|
||||
*/
|
||||
std::pair<GaussianFactorGraph, VectorConfig> planarGraph(size_t N);
|
||||
|
||||
/*
|
||||
* Create canonical ordering for planar graph that also works for tree
|
||||
* With x11 the root, e.g. for N=3
|
||||
* x33 x23 x13 x32 x22 x12 x31 x21 x11
|
||||
*/
|
||||
Ordering planarOrdering(size_t N);
|
||||
|
||||
/*
|
||||
* Split graph into tree and loop closing constraints, e.g., with N=3
|
||||
* x13-x23-x33
|
||||
* |
|
||||
* x12-x22-x32
|
||||
* |
|
||||
* -x11-x21-x31
|
||||
*/
|
||||
std::pair<GaussianFactorGraph, GaussianFactorGraph> splitOffPlanarTree(size_t N,
|
||||
const GaussianFactorGraph& original);
|
||||
|
||||
} // gtsam
|
||||
|
|
|
|||
Loading…
Reference in New Issue