Removed 2 more ordered files
Richard Roberts
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667facdbce
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file smallExample.h
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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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*/
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// \callgraph
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#pragma once
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#include <tests/simulated2D.h>
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#include <gtsam/nonlinear/Symbol.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <boost/tuple/tuple.hpp>
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namespace gtsam {
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namespace example {
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namespace {
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typedef NonlinearFactorGraph Graph;
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/**
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* Create small example for non-linear factor graph
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*/
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boost::shared_ptr<const Graph> sharedNonlinearFactorGraph();
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Graph createNonlinearFactorGraph();
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/**
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* Create values structure to go with it
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* The ground truth values structure for the example above
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*/
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Values createValues();
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/** Vector Values equivalent */
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VectorValuesOrdered createVectorValues();
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/**
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* create a noisy values structure for a nonlinear factor graph
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*/
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boost::shared_ptr<const Values> sharedNoisyValues();
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Values createNoisyValues();
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/**
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* Zero delta config
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*/
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VectorValuesOrdered createZeroDelta(const OrderingOrdered& ordering);
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/**
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* Delta config that, when added to noisyValues, returns the ground truth
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*/
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VectorValuesOrdered createCorrectDelta(const OrderingOrdered& ordering);
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/**
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* create a linear factor graph
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* The non-linear graph above evaluated at NoisyValues
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*/
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GaussianFactorGraphOrdered createGaussianFactorGraph(const OrderingOrdered& ordering);
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/**
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* create small Chordal Bayes Net x <- y
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*/
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GaussianBayesNetOrdered createSmallGaussianBayesNet();
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/**
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* Create really non-linear factor graph (cos/sin)
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*/
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boost::shared_ptr<const Graph>
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sharedReallyNonlinearFactorGraph();
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Graph createReallyNonlinearFactorGraph();
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/**
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* Create a full nonlinear smoother
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* @param T number of time-steps
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*/
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std::pair<Graph, Values> createNonlinearSmoother(int T);
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/**
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* Create a Kalman smoother by linearizing a non-linear factor graph
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* @param T number of time-steps
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*/
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std::pair<FactorGraphOrdered<GaussianFactorOrdered>, OrderingOrdered> createSmoother(int T, boost::optional<OrderingOrdered> ordering = boost::none);
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/* ******************************************************* */
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// Linear Constrained Examples
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/* ******************************************************* */
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/**
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* Creates a simple constrained graph with one linear factor and
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* one binary equality constraint that sets x = y
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*/
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GaussianFactorGraphOrdered createSimpleConstraintGraph();
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VectorValuesOrdered createSimpleConstraintValues();
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/**
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* Creates a simple constrained graph with one linear factor and
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* one binary constraint.
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*/
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GaussianFactorGraphOrdered createSingleConstraintGraph();
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VectorValuesOrdered createSingleConstraintValues();
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/**
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* Creates a constrained graph with a linear factor and two
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* binary constraints that share a node
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*/
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GaussianFactorGraphOrdered createMultiConstraintGraph();
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VectorValuesOrdered createMultiConstraintValues();
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/* ******************************************************* */
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// Planar graph with easy subtree for SubgraphPreconditioner
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/* ******************************************************* */
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/*
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* Create factor graph with N^2 nodes, for example for N=3
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* x13-x23-x33
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* | | |
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* x12-x22-x32
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* | | |
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* -x11-x21-x31
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* with x11 clamped at (1,1), and others related by 2D odometry.
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*/
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boost::tuple<GaussianFactorGraphOrdered, VectorValuesOrdered> planarGraph(size_t N);
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/*
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* Create canonical ordering for planar graph that also works for tree
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* With x11 the root, e.g. for N=3
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* x33 x23 x13 x32 x22 x12 x31 x21 x11
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*/
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OrderingOrdered planarOrdering(size_t N);
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/*
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* Split graph into tree and loop closing constraints, e.g., with N=3
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* x13-x23-x33
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* |
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* x12-x22-x32
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* |
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* -x11-x21-x31
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*/
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std::pair<GaussianFactorGraphOrdered, GaussianFactorGraphOrdered > splitOffPlanarTree(
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size_t N, const GaussianFactorGraphOrdered& original);
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// Implementations
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// using namespace gtsam::noiseModel;
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namespace impl {
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typedef boost::shared_ptr<NonlinearFactor> shared_nlf;
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static SharedDiagonal sigma1_0 = noiseModel::Isotropic::Sigma(2,1.0);
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static SharedDiagonal sigma0_1 = noiseModel::Isotropic::Sigma(2,0.1);
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static SharedDiagonal sigma0_2 = noiseModel::Isotropic::Sigma(2,0.2);
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static SharedDiagonal constraintModel = noiseModel::Constrained::All(2);
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static const Index _l1_=0, _x1_=1, _x2_=2;
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static const Index _x_=0, _y_=1, _z_=2;
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} // \namespace impl
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/* ************************************************************************* */
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boost::shared_ptr<const Graph> sharedNonlinearFactorGraph() {
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using namespace impl;
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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// Create
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boost::shared_ptr<Graph> nlfg(
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new Graph);
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// prior on x1
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Point2 mu;
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shared_nlf f1(new simulated2D::Prior(mu, sigma0_1, X(1)));
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nlfg->push_back(f1);
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// odometry between x1 and x2
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Point2 z2(1.5, 0);
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shared_nlf f2(new simulated2D::Odometry(z2, sigma0_1, X(1), X(2)));
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nlfg->push_back(f2);
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// measurement between x1 and l1
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Point2 z3(0, -1);
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shared_nlf f3(new simulated2D::Measurement(z3, sigma0_2, X(1), L(1)));
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nlfg->push_back(f3);
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// measurement between x2 and l1
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Point2 z4(-1.5, -1.);
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shared_nlf f4(new simulated2D::Measurement(z4, sigma0_2, X(2), L(1)));
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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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Graph createNonlinearFactorGraph() {
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return *sharedNonlinearFactorGraph();
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}
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/* ************************************************************************* */
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Values createValues() {
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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Values c;
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c.insert(X(1), Point2(0.0, 0.0));
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c.insert(X(2), Point2(1.5, 0.0));
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c.insert(L(1), Point2(0.0, -1.0));
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return c;
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}
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/* ************************************************************************* */
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VectorValuesOrdered createVectorValues() {
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using namespace impl;
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VectorValuesOrdered c(std::vector<size_t>(3, 2));
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c[_l1_] = Vector_(2, 0.0, -1.0);
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c[_x1_] = Vector_(2, 0.0, 0.0);
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c[_x2_] = Vector_(2, 1.5, 0.0);
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return c;
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}
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/* ************************************************************************* */
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boost::shared_ptr<const Values> sharedNoisyValues() {
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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boost::shared_ptr<Values> c(new Values);
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c->insert(X(1), Point2(0.1, 0.1));
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c->insert(X(2), Point2(1.4, 0.2));
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c->insert(L(1), Point2(0.1, -1.1));
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return c;
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}
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/* ************************************************************************* */
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Values createNoisyValues() {
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return *sharedNoisyValues();
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}
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/* ************************************************************************* */
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VectorValuesOrdered createCorrectDelta(const OrderingOrdered& ordering) {
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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VectorValuesOrdered c(std::vector<size_t>(3,2));
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c[ordering[L(1)]] = Vector_(2, -0.1, 0.1);
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c[ordering[X(1)]] = Vector_(2, -0.1, -0.1);
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c[ordering[X(2)]] = Vector_(2, 0.1, -0.2);
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return c;
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}
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/* ************************************************************************* */
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VectorValuesOrdered createZeroDelta(const OrderingOrdered& ordering) {
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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VectorValuesOrdered c(std::vector<size_t>(3,2));
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c[ordering[L(1)]] = zero(2);
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c[ordering[X(1)]] = zero(2);
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c[ordering[X(2)]] = zero(2);
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return c;
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}
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/* ************************************************************************* */
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GaussianFactorGraphOrdered createGaussianFactorGraph(const OrderingOrdered& ordering) {
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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// Create empty graph
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GaussianFactorGraphOrdered fg;
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SharedDiagonal unit2 = noiseModel::Unit::Create(2);
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// linearized prior on x1: c[_x1_]+x1=0 i.e. x1=-c[_x1_]
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fg.push_back(boost::make_shared<JacobianFactorOrdered>(ordering[X(1)], 10*eye(2), -1.0*ones(2), unit2));
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// odometry between x1 and x2: x2-x1=[0.2;-0.1]
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fg.push_back(boost::make_shared<JacobianFactorOrdered>(ordering[X(1)], -10*eye(2),ordering[X(2)], 10*eye(2), Vector_(2, 2.0, -1.0), unit2));
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// measurement between x1 and l1: l1-x1=[0.0;0.2]
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fg.push_back(boost::make_shared<JacobianFactorOrdered>(ordering[X(1)], -5*eye(2), ordering[L(1)], 5*eye(2), Vector_(2, 0.0, 1.0), unit2));
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// measurement between x2 and l1: l1-x2=[-0.2;0.3]
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fg.push_back(boost::make_shared<JacobianFactorOrdered>(ordering[X(2)], -5*eye(2), ordering[L(1)], 5*eye(2), Vector_(2, -1.0, 1.5), unit2));
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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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GaussianBayesNetOrdered createSmallGaussianBayesNet() {
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using namespace impl;
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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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GaussianConditionalOrdered::shared_ptr Px_y(new GaussianConditionalOrdered(_x_, d1, R11, _y_, S12, tau));
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GaussianConditionalOrdered::shared_ptr Py(new GaussianConditionalOrdered(_y_, d2, R22, tau));
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GaussianBayesNetOrdered 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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Point2 h(const Point2& v) {
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return Point2(cos(v.x()), sin(v.y()));
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}
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Matrix H(const Point2& v) {
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return Matrix_(2, 2,
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-sin(v.x()), 0.0,
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0.0, cos(v.y()));
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}
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struct UnaryFactor: public gtsam::NoiseModelFactor1<Point2> {
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Point2 z_;
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UnaryFactor(const Point2& z, const SharedNoiseModel& model, Key key) :
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gtsam::NoiseModelFactor1<Point2>(model, key), z_(z) {
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}
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Vector evaluateError(const Point2& x, boost::optional<Matrix&> A = boost::none) const {
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if (A) *A = H(x);
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return (h(x) - z_).vector();
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}
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};
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}
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/* ************************************************************************* */
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boost::shared_ptr<const Graph> sharedReallyNonlinearFactorGraph() {
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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boost::shared_ptr<Graph> fg(new Graph);
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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<smallOptimize::UnaryFactor> factor(
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new smallOptimize::UnaryFactor(z, noiseModel::Isotropic::Sigma(2,sigma), X(1)));
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fg->push_back(factor);
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return fg;
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}
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Graph createReallyNonlinearFactorGraph() {
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return *sharedReallyNonlinearFactorGraph();
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}
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/* ************************************************************************* */
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std::pair<Graph, Values> createNonlinearSmoother(int T) {
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using namespace impl;
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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// Create
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Graph nlfg;
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Values poses;
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// prior on x1
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Point2 x1(1.0, 0.0);
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shared_nlf prior(new simulated2D::Prior(x1, sigma1_0, X(1)));
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nlfg.push_back(prior);
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poses.insert(X(1), 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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Point2 odo(1.0, 0.0);
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shared_nlf odometry(new simulated2D::Odometry(odo, sigma1_0, X(t - 1), X(t)));
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nlfg.push_back(odometry);
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// measurement on x_t is like perfect GPS
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Point2 xt(t, 0);
|
|
||||||
shared_nlf measurement(new simulated2D::Prior(xt, sigma1_0, X(t)));
|
|
||||||
nlfg.push_back(measurement);
|
|
||||||
|
|
||||||
// initial estimate
|
|
||||||
poses.insert(X(t), xt);
|
|
||||||
}
|
|
||||||
|
|
||||||
return std::make_pair(nlfg, poses);
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
std::pair<FactorGraphOrdered<GaussianFactorOrdered>, OrderingOrdered> createSmoother(int T, boost::optional<OrderingOrdered> ordering) {
|
|
||||||
Graph nlfg;
|
|
||||||
Values poses;
|
|
||||||
boost::tie(nlfg, poses) = createNonlinearSmoother(T);
|
|
||||||
|
|
||||||
if(!ordering) ordering = *poses.orderingArbitrary();
|
|
||||||
return std::make_pair(*nlfg.linearize(poses, *ordering), *ordering);
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
GaussianFactorGraphOrdered createSimpleConstraintGraph() {
|
|
||||||
using namespace impl;
|
|
||||||
// create unary factor
|
|
||||||
// prior on _x_, mean = [1,-1], sigma=0.1
|
|
||||||
Matrix Ax = eye(2);
|
|
||||||
Vector b1(2);
|
|
||||||
b1(0) = 1.0;
|
|
||||||
b1(1) = -1.0;
|
|
||||||
JacobianFactorOrdered::shared_ptr f1(new JacobianFactorOrdered(_x_, Ax, b1, sigma0_1));
|
|
||||||
|
|
||||||
// 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);
|
|
||||||
JacobianFactorOrdered::shared_ptr f2(new JacobianFactorOrdered(_x_, Ax1, _y_, Ay1, b2,
|
|
||||||
constraintModel));
|
|
||||||
|
|
||||||
// construct the graph
|
|
||||||
GaussianFactorGraphOrdered fg;
|
|
||||||
fg.push_back(f1);
|
|
||||||
fg.push_back(f2);
|
|
||||||
|
|
||||||
return fg;
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
VectorValuesOrdered createSimpleConstraintValues() {
|
|
||||||
using namespace impl;
|
|
||||||
using symbol_shorthand::X;
|
|
||||||
using symbol_shorthand::L;
|
|
||||||
VectorValuesOrdered config(std::vector<size_t>(2,2));
|
|
||||||
Vector v = Vector_(2, 1.0, -1.0);
|
|
||||||
config[_x_] = v;
|
|
||||||
config[_y_] = v;
|
|
||||||
return config;
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
GaussianFactorGraphOrdered createSingleConstraintGraph() {
|
|
||||||
using namespace impl;
|
|
||||||
// create unary factor
|
|
||||||
// prior on _x_, mean = [1,-1], sigma=0.1
|
|
||||||
Matrix Ax = eye(2);
|
|
||||||
Vector b1(2);
|
|
||||||
b1(0) = 1.0;
|
|
||||||
b1(1) = -1.0;
|
|
||||||
//GaussianFactorOrdered::shared_ptr f1(new JacobianFactor(_x_, sigma0_1->Whiten(Ax), sigma0_1->whiten(b1), sigma0_1));
|
|
||||||
JacobianFactorOrdered::shared_ptr f1(new JacobianFactorOrdered(_x_, Ax, b1, sigma0_1));
|
|
||||||
|
|
||||||
// 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);
|
|
||||||
JacobianFactorOrdered::shared_ptr f2(new JacobianFactorOrdered(_x_, Ax1, _y_, Ay1, b2,
|
|
||||||
constraintModel));
|
|
||||||
|
|
||||||
// construct the graph
|
|
||||||
GaussianFactorGraphOrdered fg;
|
|
||||||
fg.push_back(f1);
|
|
||||||
fg.push_back(f2);
|
|
||||||
|
|
||||||
return fg;
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
VectorValuesOrdered createSingleConstraintValues() {
|
|
||||||
using namespace impl;
|
|
||||||
VectorValuesOrdered config(std::vector<size_t>(2,2));
|
|
||||||
config[_x_] = Vector_(2, 1.0, -1.0);
|
|
||||||
config[_y_] = Vector_(2, 0.2, 0.1);
|
|
||||||
return config;
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
GaussianFactorGraphOrdered createMultiConstraintGraph() {
|
|
||||||
using namespace impl;
|
|
||||||
// unary factor 1
|
|
||||||
Matrix A = eye(2);
|
|
||||||
Vector b = Vector_(2, -2.0, 2.0);
|
|
||||||
JacobianFactorOrdered::shared_ptr lf1(new JacobianFactorOrdered(_x_, A, b, sigma0_1));
|
|
||||||
|
|
||||||
// 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;
|
|
||||||
JacobianFactorOrdered::shared_ptr lc1(new JacobianFactorOrdered(_x_, A11, _y_, A12, b1,
|
|
||||||
constraintModel));
|
|
||||||
|
|
||||||
// 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;
|
|
||||||
JacobianFactorOrdered::shared_ptr lc2(new JacobianFactorOrdered(_x_, A21, _z_, A22, b2,
|
|
||||||
constraintModel));
|
|
||||||
|
|
||||||
// construct the graph
|
|
||||||
GaussianFactorGraphOrdered fg;
|
|
||||||
fg.push_back(lf1);
|
|
||||||
fg.push_back(lc1);
|
|
||||||
fg.push_back(lc2);
|
|
||||||
|
|
||||||
return fg;
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
VectorValuesOrdered createMultiConstraintValues() {
|
|
||||||
using namespace impl;
|
|
||||||
VectorValuesOrdered config(std::vector<size_t>(3,2));
|
|
||||||
config[_x_] = Vector_(2, -2.0, 2.0);
|
|
||||||
config[_y_] = Vector_(2, -0.1, 0.4);
|
|
||||||
config[_z_] = Vector_(2, -4.0, 5.0);
|
|
||||||
return config;
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
// Create key for simulated planar graph
|
|
||||||
namespace impl {
|
|
||||||
Symbol key(int x, int y) {
|
|
||||||
using symbol_shorthand::X;
|
|
||||||
return X(1000*x+y);
|
|
||||||
}
|
|
||||||
} // \namespace impl
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
boost::tuple<GaussianFactorGraphOrdered, VectorValuesOrdered> planarGraph(size_t N) {
|
|
||||||
using namespace impl;
|
|
||||||
|
|
||||||
// create empty graph
|
|
||||||
NonlinearFactorGraph nlfg;
|
|
||||||
|
|
||||||
// Create almost hard constraint on x11, sigma=0 will work for PCG not for normal
|
|
||||||
shared_nlf constraint(new simulated2D::Prior(Point2(1.0, 1.0), noiseModel::Isotropic::Sigma(2,1e-3), key(1,1)));
|
|
||||||
nlfg.push_back(constraint);
|
|
||||||
|
|
||||||
// Create horizontal constraints, 1...N*(N-1)
|
|
||||||
Point2 z1(1.0, 0.0); // move right
|
|
||||||
for (size_t x = 1; x < N; x++)
|
|
||||||
for (size_t y = 1; y <= N; y++) {
|
|
||||||
shared_nlf f(new simulated2D::Odometry(z1, noiseModel::Isotropic::Sigma(2,0.01), key(x, y), key(x + 1, y)));
|
|
||||||
nlfg.push_back(f);
|
|
||||||
}
|
|
||||||
|
|
||||||
// Create vertical constraints, N*(N-1)+1..2*N*(N-1)
|
|
||||||
Point2 z2(0.0, 1.0); // move up
|
|
||||||
for (size_t x = 1; x <= N; x++)
|
|
||||||
for (size_t y = 1; y < N; y++) {
|
|
||||||
shared_nlf f(new simulated2D::Odometry(z2, noiseModel::Isotropic::Sigma(2,0.01), key(x, y), key(x, y + 1)));
|
|
||||||
nlfg.push_back(f);
|
|
||||||
}
|
|
||||||
|
|
||||||
// Create linearization and ground xtrue config
|
|
||||||
Values zeros;
|
|
||||||
for (size_t x = 1; x <= N; x++)
|
|
||||||
for (size_t y = 1; y <= N; y++)
|
|
||||||
zeros.insert(key(x, y), Point2());
|
|
||||||
OrderingOrdered ordering(planarOrdering(N));
|
|
||||||
VectorValuesOrdered xtrue(zeros.dims(ordering));
|
|
||||||
for (size_t x = 1; x <= N; x++)
|
|
||||||
for (size_t y = 1; y <= N; y++)
|
|
||||||
xtrue[ordering[key(x, y)]] = Point2(x,y).vector();
|
|
||||||
|
|
||||||
// linearize around zero
|
|
||||||
boost::shared_ptr<GaussianFactorGraphOrdered> gfg = nlfg.linearize(zeros, ordering);
|
|
||||||
return boost::make_tuple(*gfg, xtrue);
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
OrderingOrdered planarOrdering(size_t N) {
|
|
||||||
OrderingOrdered ordering;
|
|
||||||
for (size_t y = N; y >= 1; y--)
|
|
||||||
for (size_t x = N; x >= 1; x--)
|
|
||||||
ordering.push_back(impl::key(x, y));
|
|
||||||
return ordering;
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
std::pair<GaussianFactorGraphOrdered, GaussianFactorGraphOrdered > splitOffPlanarTree(size_t N,
|
|
||||||
const GaussianFactorGraphOrdered& original) {
|
|
||||||
GaussianFactorGraphOrdered 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 std::make_pair(T, C);
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
|
|
||||||
} // anonymous namespace
|
|
||||||
} // \namespace example
|
|
||||||
} // \namespace gtsam
|
|
||||||
|
|
@ -1,85 +0,0 @@
|
||||||
/* ----------------------------------------------------------------------------
|
|
||||||
|
|
||||||
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
|
||||||
* Atlanta, Georgia 30332-0415
|
|
||||||
* All Rights Reserved
|
|
||||||
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
|
||||||
|
|
||||||
* See LICENSE for the license information
|
|
||||||
|
|
||||||
* -------------------------------------------------------------------------- */
|
|
||||||
|
|
||||||
/**
|
|
||||||
* @file testGaussianISAM.cpp
|
|
||||||
* @brief Unit tests for GaussianISAM
|
|
||||||
* @author Michael Kaess
|
|
||||||
*/
|
|
||||||
|
|
||||||
#include <tests/smallExampleOrdered.h>
|
|
||||||
#include <gtsam/nonlinear/OrderingOrdered.h>
|
|
||||||
#include <gtsam/nonlinear/Symbol.h>
|
|
||||||
#include <gtsam/linear/GaussianBayesNetOrdered.h>
|
|
||||||
#include <gtsam/linear/GaussianISAMOrdered.h>
|
|
||||||
#include <gtsam/linear/GaussianSequentialSolver.h>
|
|
||||||
#include <gtsam/linear/GaussianMultifrontalSolver.h>
|
|
||||||
#include <gtsam/inference/ISAMOrdered.h>
|
|
||||||
#include <gtsam/geometry/Rot2.h>
|
|
||||||
|
|
||||||
#include <CppUnitLite/TestHarness.h>
|
|
||||||
|
|
||||||
#include <boost/foreach.hpp>
|
|
||||||
#include <boost/assign/std/list.hpp> // for operator +=
|
|
||||||
using namespace boost::assign;
|
|
||||||
|
|
||||||
using namespace std;
|
|
||||||
using namespace gtsam;
|
|
||||||
using namespace example;
|
|
||||||
|
|
||||||
using symbol_shorthand::X;
|
|
||||||
using symbol_shorthand::L;
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
// Some numbers that should be consistent among all smoother tests
|
|
||||||
|
|
||||||
static const double tol = 1e-4;
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
TEST( ISAMOrdered, iSAM_smoother )
|
|
||||||
{
|
|
||||||
OrderingOrdered ordering;
|
|
||||||
for (int t = 1; t <= 7; t++) ordering += X(t);
|
|
||||||
|
|
||||||
// Create smoother with 7 nodes
|
|
||||||
GaussianFactorGraphOrdered smoother = createSmoother(7, ordering).first;
|
|
||||||
|
|
||||||
// run iSAM for every factor
|
|
||||||
GaussianISAMOrdered actual;
|
|
||||||
BOOST_FOREACH(boost::shared_ptr<GaussianFactorOrdered> factor, smoother) {
|
|
||||||
GaussianFactorGraphOrdered factorGraph;
|
|
||||||
factorGraph.push_back(factor);
|
|
||||||
actual.update(factorGraph);
|
|
||||||
}
|
|
||||||
|
|
||||||
// Create expected Bayes Tree by solving smoother with "natural" ordering
|
|
||||||
BayesTreeOrdered<GaussianConditionalOrdered>::shared_ptr bayesTree = GaussianMultifrontalSolver(smoother).eliminate();
|
|
||||||
GaussianISAMOrdered expected(*bayesTree);
|
|
||||||
|
|
||||||
// Verify sigmas in the bayes tree
|
|
||||||
BOOST_FOREACH(const GaussianBayesTreeOrdered::sharedClique& clique, bayesTree->nodes()) {
|
|
||||||
GaussianConditionalOrdered::shared_ptr conditional = clique->conditional();
|
|
||||||
size_t dim = conditional->dim();
|
|
||||||
EXPECT(assert_equal(gtsam::ones(dim), conditional->get_sigmas(), tol));
|
|
||||||
}
|
|
||||||
|
|
||||||
// Check whether BayesTree is correct
|
|
||||||
EXPECT(assert_equal(expected, actual));
|
|
||||||
|
|
||||||
// obtain solution
|
|
||||||
VectorValuesOrdered e(VectorValuesOrdered::Zero(7,2)); // expected solution
|
|
||||||
VectorValuesOrdered optimized = optimize(actual); // actual solution
|
|
||||||
EXPECT(assert_equal(e, optimized));
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
|
|
||||||
/* ************************************************************************* */
|
|
||||||
Loading…
Reference in New Issue