516 lines
14 KiB
C++
516 lines
14 KiB
C++
/**
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* @file smallExample.cpp
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* @brief Create small example with two poses and one landmark
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* @brief smallExample
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* @author Carlos Nieto
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* @author Frank dellaert
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*/
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#include <iostream>
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#include <string>
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using namespace std;
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#include "Ordering.h"
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#include "Matrix.h"
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#include "NonlinearFactor.h"
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#include "smallExample.h"
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#include "Point2Prior.h"
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#include "Simulated2DOdometry.h"
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#include "Simulated2DMeasurement.h"
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#include "simulated2D.h"
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// template definitions
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#include "FactorGraph-inl.h"
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#include "NonlinearFactorGraph-inl.h"
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namespace gtsam {
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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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// prior on x1
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double sigma1=0.1;
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Vector mu(2); mu(0) = 0 ; mu(1) = 0;
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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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// 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 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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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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{
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Vector v_x1(2); v_x1(0) = 0.; v_x1(1) = 0.;
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Vector v_x2(2); v_x2(0) = 1.5; v_x2(1) = 0.;
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Vector v_l1(2); v_l1(0) = 0.; v_l1(1) = -1.;
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VectorConfig c;
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c.insert("x1", v_x1);
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c.insert("x2", v_x2);
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c.insert("l1", v_l1);
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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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{
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Vector v_x1(2); v_x1(0) = 0.1; v_x1(1) = 0.1;
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Vector v_x2(2); v_x2(0) = 1.4; v_x2(1) = 0.2;
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Vector v_l1(2); v_l1(0) = 0.1; v_l1(1) = -1.1;
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boost::shared_ptr<VectorConfig> c(new VectorConfig);
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c->insert("x1", v_x1);
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c->insert("x2", v_x2);
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c->insert("l1", v_l1);
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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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Vector v_x1(2); v_x1(0) = -0.1; v_x1(1) = -0.1;
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Vector v_x2(2); v_x2(0) = 0.1; v_x2(1) = -0.2;
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Vector v_l1(2); v_l1(0) = -0.1; v_l1(1) = 0.1;
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VectorConfig c;
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c.insert("x1", v_x1);
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c.insert("x2", v_x2);
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c.insert("l1", v_l1);
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return c;
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}
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/* ************************************************************************* */
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VectorConfig createZeroDelta() {
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Vector v_x1(2); v_x1(0) = 0; v_x1(1) = 0;
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Vector v_x2(2); v_x2(0) = 0; v_x2(1) = 0;
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Vector v_l1(2); v_l1(0) = 0; v_l1(1) = 0;
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VectorConfig c;
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c.insert("x1", v_x1);
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c.insert("x2", v_x2);
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c.insert("l1", v_l1);
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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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VectorConfig c = createNoisyConfig();
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// Create
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GaussianFactorGraph fg;
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double sigma1 = 0.1;
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// prior on x1
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Matrix A11(2,2);
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A11(0,0) = 1; A11(0,1) = 0;
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A11(1,0) = 0; A11(1,1) = 1;
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Vector b = - c["x1"];
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GaussianFactor::shared_ptr f1(new GaussianFactor("x1", A11, b, sigma1));
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fg.push_back(f1);
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// odometry between x1 and x2
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double sigma2 = 0.1;
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Matrix A21(2,2);
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A21(0,0) = -1 ; A21(0,1) = 0;
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A21(1,0) = 0 ; A21(1,1) = -1;
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Matrix A22(2,2);
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A22(0,0) = 1 ; A22(0,1) = 0;
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A22(1,0) = 0 ; A22(1,1) = 1;
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// Vector b(2);
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b(0) = 0.2 ; b(1) = -0.1;
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GaussianFactor::shared_ptr f2(new GaussianFactor("x1", A21, "x2", A22, b, sigma2));
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fg.push_back(f2);
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// measurement between x1 and l1
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double sigma3 = 0.2;
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Matrix A31(2,2);
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A31(0,0) = -1; A31(0,1) = 0;
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A31(1,0) = 0; A31(1,1) = -1;
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Matrix A32(2,2);
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A32(0,0) = 1 ; A32(0,1) = 0;
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A32(1,0) = 0 ; A32(1,1) = 1;
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b(0) = 0 ; b(1) = 0.2;
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GaussianFactor::shared_ptr f3(new GaussianFactor("x1", A31, "l1", A32, b, sigma3));
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fg.push_back(f3);
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// measurement between x2 and l1
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double sigma4 = 0.2;
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Matrix A41(2,2);
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A41(0,0) = -1 ; A41(0,1) = 0;
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A41(1,0) = 0 ; A41(1,1) = -1;
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Matrix A42(2,2);
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A42(0,0) = 1 ; A42(0,1) = 0;
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A42(1,0) = 0 ; A42(1,1) = 1;
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b(0)= -0.2 ; b(1) = 0.3;
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GaussianFactor::shared_ptr f4(new GaussianFactor("x2", A41, "l1", A42, b, sigma4));
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fg.push_back(f4);
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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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GaussianFactorGraph createSmoother(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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}
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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(
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new GaussianFactor("x", Ax1, "y", Ay1, b2, 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
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// between "x" and "y", that is going to be the only factor on "y"
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// |1 2||x_1| + |10 0||y_1| = |1|
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// |2 1||x_2| |0 10||y_2| |2|
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Matrix Ax1(2, 2);
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Ax1(0, 0) = 1.0; Ax1(0, 1) = 2.0;
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Ax1(1, 0) = 2.0; Ax1(1, 1) = 1.0;
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Matrix Ay1 = eye(2) * 10;
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Vector b2 = Vector_(2, 1.0, 2.0);
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GaussianFactor::shared_ptr f2(
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new GaussianFactor("x", Ax1, "y", Ay1, b2, 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 createSingleConstraintConfig() {
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VectorConfig config;
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config.insert("x", Vector_(2, 1.0, -1.0));
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config.insert("y", Vector_(2, 0.2, 0.1));
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return config;
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}
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/* ************************************************************************* */
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GaussianFactorGraph createMultiConstraintGraph() {
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// unary factor 1
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double sigma = 0.1;
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Matrix A = eye(2);
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Vector b = Vector_(2, -2.0, 2.0);
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GaussianFactor::shared_ptr lf1(new GaussianFactor("x", A, b, sigma));
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// constraint 1
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Matrix A11(2,2);
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A11(0,0) = 1.0 ; A11(0,1) = 2.0;
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A11(1,0) = 2.0 ; A11(1,1) = 1.0;
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Matrix A12(2,2);
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A12(0,0) = 10.0 ; A12(0,1) = 0.0;
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A12(1,0) = 0.0 ; A12(1,1) = 10.0;
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Vector b1(2);
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b1(0) = 1.0; b1(1) = 2.0;
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GaussianFactor::shared_ptr lc1(new GaussianFactor("x", A11, "y", A12, b1, 0.0));
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// constraint 2
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Matrix A21(2,2);
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A21(0,0) = 3.0 ; A21(0,1) = 4.0;
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A21(1,0) = -1.0 ; A21(1,1) = -2.0;
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Matrix A22(2,2);
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A22(0,0) = 1.0 ; A22(0,1) = 1.0;
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A22(1,0) = 1.0 ; A22(1,1) = 2.0;
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Vector b2(2);
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b2(0) = 3.0; b2(1) = 4.0;
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GaussianFactor::shared_ptr lc2(new GaussianFactor("x", A21, "z", A22, b2, 0.0));
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// construct the graph
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GaussianFactorGraph fg;
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fg.push_back(lf1);
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fg.push_back(lc1);
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fg.push_back(lc2);
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return fg;
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}
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/* ************************************************************************* */
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VectorConfig createMultiConstraintConfig() {
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VectorConfig config;
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config.insert("x", Vector_(2, -2.0, 2.0));
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config.insert("y", Vector_(2, -0.1, 0.4));
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config.insert("z", Vector_(2, -4.0, 5.0));
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return config;
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}
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/* ************************************************************************* */
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//GaussianFactorGraph createConstrainedGaussianFactorGraph()
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//{
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// GaussianFactorGraph graph;
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//
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// // add an equality factor
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// Vector v1(2); v1(0)=1.;v1(1)=2.;
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// GaussianFactor::shared_ptr f1(new GaussianFactor(v1, "x0"));
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// graph.push_back_eq(f1);
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//
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// // add a normal linear factor
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// Matrix A21 = -1 * eye(2);
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//
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// Matrix A22 = eye(2);
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//
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// Vector b(2);
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// b(0) = 2 ; b(1) = 3;
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//
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// double sigma = 0.1;
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// GaussianFactor::shared_ptr f2(new GaussianFactor("x0", A21/sigma, "x1", A22/sigma, b/sigma));
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// graph.push_back(f2);
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// return graph;
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//}
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/* ************************************************************************* */
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// ConstrainedNonlinearFactorGraph<NonlinearFactor<VectorConfig> , VectorConfig> createConstrainedNonlinearFactorGraph() {
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// ConstrainedNonlinearFactorGraph<NonlinearFactor<VectorConfig> , VectorConfig> graph;
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// VectorConfig c = createConstrainedConfig();
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//
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// // equality constraint for initial pose
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// GaussianFactor::shared_ptr f1(new GaussianFactor(c["x0"], "x0"));
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// graph.push_back_eq(f1);
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//
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// // odometry between x0 and x1
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// double sigma = 0.1;
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// shared f2(new Simulated2DOdometry(c["x1"] - c["x0"], sigma, "x0", "x1"));
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// graph.push_back(f2); // TODO
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// return graph;
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// }
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/* ************************************************************************* */
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//VectorConfig createConstrainedConfig()
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//{
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// VectorConfig config;
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//
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// Vector x0(2); x0(0)=1.0; x0(1)=2.0;
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// config.insert("x0", x0);
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//
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// Vector x1(2); x1(0)=3.0; x1(1)=5.0;
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// config.insert("x1", x1);
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//
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// return config;
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//}
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/* ************************************************************************* */
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//VectorConfig createConstrainedLinConfig()
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//{
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// VectorConfig config;
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//
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// Vector x0(2); x0(0)=1.0; x0(1)=2.0; // value doesn't actually matter
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// config.insert("x0", x0);
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//
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// Vector x1(2); x1(0)=2.3; x1(1)=5.3;
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// config.insert("x1", x1);
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//
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// return config;
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//}
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/* ************************************************************************* */
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//VectorConfig createConstrainedCorrectDelta()
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//{
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// VectorConfig config;
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//
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// Vector x0(2); x0(0)=0.; x0(1)=0.;
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// config.insert("x0", x0);
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//
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// Vector x1(2); x1(0)= 0.7; x1(1)= -0.3;
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// config.insert("x1", x1);
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//
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// return config;
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//}
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/* ************************************************************************* */
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//ConstrainedGaussianBayesNet createConstrainedGaussianBayesNet()
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//{
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// ConstrainedGaussianBayesNet cbn;
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// VectorConfig c = createConstrainedConfig();
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//
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// // add regular conditional gaussian - no parent
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// Matrix R = eye(2);
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// Vector d = c["x1"];
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// double sigma = 0.1;
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// GaussianConditional::shared_ptr f1(new GaussianConditional(d/sigma, R/sigma));
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// cbn.insert("x1", f1);
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//
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// // add a delta function to the cbn
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// ConstrainedGaussianConditional::shared_ptr f2(new ConstrainedGaussianConditional); //(c["x0"], "x0"));
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// cbn.insert_df("x0", f2);
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//
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// return cbn;
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//}
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} // namespace gtsam
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