Removed 2 more ordered files
Richard Roberts
parent
667facdbce
commit
4db55a3a6a
|
|
@ -1,651 +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 smallExample.h
|
||||
* @brief Create small example with two poses and one landmark
|
||||
* @brief smallExample
|
||||
* @author Carlos Nieto
|
||||
*/
|
||||
|
||||
// \callgraph
|
||||
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <tests/simulated2D.h>
|
||||
#include <gtsam/nonlinear/Symbol.h>
|
||||
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
|
||||
#include <boost/tuple/tuple.hpp>
|
||||
|
||||
namespace gtsam {
|
||||
namespace example {
|
||||
namespace {
|
||||
|
||||
typedef NonlinearFactorGraph Graph;
|
||||
|
||||
/**
|
||||
* Create small example for non-linear factor graph
|
||||
*/
|
||||
boost::shared_ptr<const Graph> sharedNonlinearFactorGraph();
|
||||
Graph createNonlinearFactorGraph();
|
||||
|
||||
/**
|
||||
* Create values structure to go with it
|
||||
* The ground truth values structure for the example above
|
||||
*/
|
||||
Values createValues();
|
||||
|
||||
/** Vector Values equivalent */
|
||||
VectorValuesOrdered createVectorValues();
|
||||
|
||||
/**
|
||||
* create a noisy values structure for a nonlinear factor graph
|
||||
*/
|
||||
boost::shared_ptr<const Values> sharedNoisyValues();
|
||||
Values createNoisyValues();
|
||||
|
||||
/**
|
||||
* Zero delta config
|
||||
*/
|
||||
VectorValuesOrdered createZeroDelta(const OrderingOrdered& ordering);
|
||||
|
||||
/**
|
||||
* Delta config that, when added to noisyValues, returns the ground truth
|
||||
*/
|
||||
VectorValuesOrdered createCorrectDelta(const OrderingOrdered& ordering);
|
||||
|
||||
/**
|
||||
* create a linear factor graph
|
||||
* The non-linear graph above evaluated at NoisyValues
|
||||
*/
|
||||
GaussianFactorGraphOrdered createGaussianFactorGraph(const OrderingOrdered& ordering);
|
||||
|
||||
/**
|
||||
* create small Chordal Bayes Net x <- y
|
||||
*/
|
||||
GaussianBayesNetOrdered createSmallGaussianBayesNet();
|
||||
|
||||
/**
|
||||
* Create really non-linear factor graph (cos/sin)
|
||||
*/
|
||||
boost::shared_ptr<const Graph>
|
||||
sharedReallyNonlinearFactorGraph();
|
||||
Graph createReallyNonlinearFactorGraph();
|
||||
|
||||
/**
|
||||
* Create a full nonlinear smoother
|
||||
* @param T number of time-steps
|
||||
*/
|
||||
std::pair<Graph, Values> createNonlinearSmoother(int T);
|
||||
|
||||
/**
|
||||
* Create a Kalman smoother by linearizing a non-linear factor graph
|
||||
* @param T number of time-steps
|
||||
*/
|
||||
std::pair<FactorGraphOrdered<GaussianFactorOrdered>, OrderingOrdered> createSmoother(int T, boost::optional<OrderingOrdered> ordering = boost::none);
|
||||
|
||||
/* ******************************************************* */
|
||||
// Linear Constrained Examples
|
||||
/* ******************************************************* */
|
||||
|
||||
/**
|
||||
* Creates a simple constrained graph with one linear factor and
|
||||
* one binary equality constraint that sets x = y
|
||||
*/
|
||||
GaussianFactorGraphOrdered createSimpleConstraintGraph();
|
||||
VectorValuesOrdered createSimpleConstraintValues();
|
||||
|
||||
/**
|
||||
* Creates a simple constrained graph with one linear factor and
|
||||
* one binary constraint.
|
||||
*/
|
||||
GaussianFactorGraphOrdered createSingleConstraintGraph();
|
||||
VectorValuesOrdered createSingleConstraintValues();
|
||||
|
||||
/**
|
||||
* Creates a constrained graph with a linear factor and two
|
||||
* binary constraints that share a node
|
||||
*/
|
||||
GaussianFactorGraphOrdered createMultiConstraintGraph();
|
||||
VectorValuesOrdered createMultiConstraintValues();
|
||||
|
||||
/* ******************************************************* */
|
||||
// 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.
|
||||
*/
|
||||
boost::tuple<GaussianFactorGraphOrdered, VectorValuesOrdered> 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
|
||||
*/
|
||||
OrderingOrdered 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<GaussianFactorGraphOrdered, GaussianFactorGraphOrdered > splitOffPlanarTree(
|
||||
size_t N, const GaussianFactorGraphOrdered& original);
|
||||
|
||||
|
||||
|
||||
// Implementations
|
||||
|
||||
// using namespace gtsam::noiseModel;
|
||||
|
||||
namespace impl {
|
||||
typedef boost::shared_ptr<NonlinearFactor> shared_nlf;
|
||||
|
||||
static SharedDiagonal sigma1_0 = noiseModel::Isotropic::Sigma(2,1.0);
|
||||
static SharedDiagonal sigma0_1 = noiseModel::Isotropic::Sigma(2,0.1);
|
||||
static SharedDiagonal sigma0_2 = noiseModel::Isotropic::Sigma(2,0.2);
|
||||
static SharedDiagonal constraintModel = noiseModel::Constrained::All(2);
|
||||
|
||||
static const Index _l1_=0, _x1_=1, _x2_=2;
|
||||
static const Index _x_=0, _y_=1, _z_=2;
|
||||
} // \namespace impl
|
||||
|
||||
|
||||
/* ************************************************************************* */
|
||||
boost::shared_ptr<const Graph> sharedNonlinearFactorGraph() {
|
||||
using namespace impl;
|
||||
using symbol_shorthand::X;
|
||||
using symbol_shorthand::L;
|
||||
// Create
|
||||
boost::shared_ptr<Graph> nlfg(
|
||||
new Graph);
|
||||
|
||||
// prior on x1
|
||||
Point2 mu;
|
||||
shared_nlf f1(new simulated2D::Prior(mu, sigma0_1, X(1)));
|
||||
nlfg->push_back(f1);
|
||||
|
||||
// odometry between x1 and x2
|
||||
Point2 z2(1.5, 0);
|
||||
shared_nlf f2(new simulated2D::Odometry(z2, sigma0_1, X(1), X(2)));
|
||||
nlfg->push_back(f2);
|
||||
|
||||
// measurement between x1 and l1
|
||||
Point2 z3(0, -1);
|
||||
shared_nlf f3(new simulated2D::Measurement(z3, sigma0_2, X(1), L(1)));
|
||||
nlfg->push_back(f3);
|
||||
|
||||
// measurement between x2 and l1
|
||||
Point2 z4(-1.5, -1.);
|
||||
shared_nlf f4(new simulated2D::Measurement(z4, sigma0_2, X(2), L(1)));
|
||||
nlfg->push_back(f4);
|
||||
|
||||
return nlfg;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
Graph createNonlinearFactorGraph() {
|
||||
return *sharedNonlinearFactorGraph();
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
Values createValues() {
|
||||
using symbol_shorthand::X;
|
||||
using symbol_shorthand::L;
|
||||
Values c;
|
||||
c.insert(X(1), Point2(0.0, 0.0));
|
||||
c.insert(X(2), Point2(1.5, 0.0));
|
||||
c.insert(L(1), Point2(0.0, -1.0));
|
||||
return c;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
VectorValuesOrdered createVectorValues() {
|
||||
using namespace impl;
|
||||
VectorValuesOrdered c(std::vector<size_t>(3, 2));
|
||||
c[_l1_] = Vector_(2, 0.0, -1.0);
|
||||
c[_x1_] = Vector_(2, 0.0, 0.0);
|
||||
c[_x2_] = Vector_(2, 1.5, 0.0);
|
||||
return c;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
boost::shared_ptr<const Values> sharedNoisyValues() {
|
||||
using symbol_shorthand::X;
|
||||
using symbol_shorthand::L;
|
||||
boost::shared_ptr<Values> c(new Values);
|
||||
c->insert(X(1), Point2(0.1, 0.1));
|
||||
c->insert(X(2), Point2(1.4, 0.2));
|
||||
c->insert(L(1), Point2(0.1, -1.1));
|
||||
return c;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
Values createNoisyValues() {
|
||||
return *sharedNoisyValues();
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
VectorValuesOrdered createCorrectDelta(const OrderingOrdered& ordering) {
|
||||
using symbol_shorthand::X;
|
||||
using symbol_shorthand::L;
|
||||
VectorValuesOrdered c(std::vector<size_t>(3,2));
|
||||
c[ordering[L(1)]] = Vector_(2, -0.1, 0.1);
|
||||
c[ordering[X(1)]] = Vector_(2, -0.1, -0.1);
|
||||
c[ordering[X(2)]] = Vector_(2, 0.1, -0.2);
|
||||
return c;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
VectorValuesOrdered createZeroDelta(const OrderingOrdered& ordering) {
|
||||
using symbol_shorthand::X;
|
||||
using symbol_shorthand::L;
|
||||
VectorValuesOrdered c(std::vector<size_t>(3,2));
|
||||
c[ordering[L(1)]] = zero(2);
|
||||
c[ordering[X(1)]] = zero(2);
|
||||
c[ordering[X(2)]] = zero(2);
|
||||
return c;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
GaussianFactorGraphOrdered createGaussianFactorGraph(const OrderingOrdered& ordering) {
|
||||
using symbol_shorthand::X;
|
||||
using symbol_shorthand::L;
|
||||
// Create empty graph
|
||||
GaussianFactorGraphOrdered fg;
|
||||
|
||||
SharedDiagonal unit2 = noiseModel::Unit::Create(2);
|
||||
|
||||
// linearized prior on x1: c[_x1_]+x1=0 i.e. x1=-c[_x1_]
|
||||
fg.push_back(boost::make_shared<JacobianFactorOrdered>(ordering[X(1)], 10*eye(2), -1.0*ones(2), unit2));
|
||||
|
||||
// odometry between x1 and x2: x2-x1=[0.2;-0.1]
|
||||
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));
|
||||
|
||||
// measurement between x1 and l1: l1-x1=[0.0;0.2]
|
||||
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));
|
||||
|
||||
// measurement between x2 and l1: l1-x2=[-0.2;0.3]
|
||||
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));
|
||||
|
||||
return fg;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
/** create small Chordal Bayes Net x <- y
|
||||
* x y d
|
||||
* 1 1 9
|
||||
* 1 5
|
||||
*/
|
||||
GaussianBayesNetOrdered createSmallGaussianBayesNet() {
|
||||
using namespace impl;
|
||||
Matrix R11 = Matrix_(1, 1, 1.0), S12 = Matrix_(1, 1, 1.0);
|
||||
Matrix R22 = Matrix_(1, 1, 1.0);
|
||||
Vector d1(1), d2(1);
|
||||
d1(0) = 9;
|
||||
d2(0) = 5;
|
||||
Vector tau(1);
|
||||
tau(0) = 1.0;
|
||||
|
||||
// define nodes and specify in reverse topological sort (i.e. parents last)
|
||||
GaussianConditionalOrdered::shared_ptr Px_y(new GaussianConditionalOrdered(_x_, d1, R11, _y_, S12, tau));
|
||||
GaussianConditionalOrdered::shared_ptr Py(new GaussianConditionalOrdered(_y_, d2, R22, tau));
|
||||
GaussianBayesNetOrdered cbn;
|
||||
cbn.push_back(Px_y);
|
||||
cbn.push_back(Py);
|
||||
|
||||
return cbn;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// Some nonlinear functions to optimize
|
||||
/* ************************************************************************* */
|
||||
namespace smallOptimize {
|
||||
|
||||
Point2 h(const Point2& v) {
|
||||
return Point2(cos(v.x()), sin(v.y()));
|
||||
}
|
||||
|
||||
Matrix H(const Point2& v) {
|
||||
return Matrix_(2, 2,
|
||||
-sin(v.x()), 0.0,
|
||||
0.0, cos(v.y()));
|
||||
}
|
||||
|
||||
struct UnaryFactor: public gtsam::NoiseModelFactor1<Point2> {
|
||||
|
||||
Point2 z_;
|
||||
|
||||
UnaryFactor(const Point2& z, const SharedNoiseModel& model, Key key) :
|
||||
gtsam::NoiseModelFactor1<Point2>(model, key), z_(z) {
|
||||
}
|
||||
|
||||
Vector evaluateError(const Point2& x, boost::optional<Matrix&> A = boost::none) const {
|
||||
if (A) *A = H(x);
|
||||
return (h(x) - z_).vector();
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
boost::shared_ptr<const Graph> sharedReallyNonlinearFactorGraph() {
|
||||
using symbol_shorthand::X;
|
||||
using symbol_shorthand::L;
|
||||
boost::shared_ptr<Graph> fg(new Graph);
|
||||
Vector z = Vector_(2, 1.0, 0.0);
|
||||
double sigma = 0.1;
|
||||
boost::shared_ptr<smallOptimize::UnaryFactor> factor(
|
||||
new smallOptimize::UnaryFactor(z, noiseModel::Isotropic::Sigma(2,sigma), X(1)));
|
||||
fg->push_back(factor);
|
||||
return fg;
|
||||
}
|
||||
|
||||
Graph createReallyNonlinearFactorGraph() {
|
||||
return *sharedReallyNonlinearFactorGraph();
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
std::pair<Graph, Values> createNonlinearSmoother(int T) {
|
||||
using namespace impl;
|
||||
using symbol_shorthand::X;
|
||||
using symbol_shorthand::L;
|
||||
|
||||
// Create
|
||||
Graph nlfg;
|
||||
Values poses;
|
||||
|
||||
// prior on x1
|
||||
Point2 x1(1.0, 0.0);
|
||||
shared_nlf prior(new simulated2D::Prior(x1, sigma1_0, X(1)));
|
||||
nlfg.push_back(prior);
|
||||
poses.insert(X(1), x1);
|
||||
|
||||
for (int t = 2; t <= T; t++) {
|
||||
// odometry between x_t and x_{t-1}
|
||||
Point2 odo(1.0, 0.0);
|
||||
shared_nlf odometry(new simulated2D::Odometry(odo, sigma1_0, X(t - 1), X(t)));
|
||||
nlfg.push_back(odometry);
|
||||
|
||||
// measurement on x_t is like perfect GPS
|
||||
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