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gtsam/cpp/testSQP.cpp

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/*
* @file testSQP.cpp
* @brief demos of SQP using existing gtsam components
* @author Alex Cunningham
*/
#include <iostream>
#include <cmath>
#include <boost/assign/std/list.hpp> // for operator +=
#include <boost/assign/std/map.hpp> // for insert
#include <boost/foreach.hpp>
#include <boost/shared_ptr.hpp>
#include <CppUnitLite/TestHarness.h>
// TODO: DANGEROUS, create shared pointers
#define GTSAM_MAGIC_GAUSSIAN 2
#define GTSAM_MAGIC_KEY
#include <Pose3.h>
#include <GaussianFactorGraph.h>
#include <NonlinearOptimizer.h>
#include <SQPOptimizer.h>
#include <simulated2D.h>
#include <Ordering.h>
#include <visualSLAM.h>
// templated implementations
#include <NonlinearFactorGraph-inl.h>
#include <NonlinearConstraint-inl.h>
#include <NonlinearOptimizer-inl.h>
#include <SQPOptimizer-inl.h>
using namespace std;
using namespace gtsam;
using namespace boost;
using namespace boost::assign;
// trick from some reading group
#define FOREACH_PAIR( KEY, VAL, COL) BOOST_FOREACH (boost::tie(KEY,VAL),COL)
/* *********************************************************************
* This example uses a nonlinear objective function and
* nonlinear equality constraint. The formulation is actually
* the Cholesky form that creates the full Hessian explicitly,
* which should really be avoided with our QR-based machinery.
*
* Note: the update equation used here has a fixed step size
* and gain that is rather arbitrarily chosen, and as such,
* will take a silly number of iterations.
*/
TEST (SQP, problem1_cholesky ) {
bool verbose = false;
// use a nonlinear function of f(x) = x^2+y^2
// nonlinear equality constraint: g(x) = x^2-5-y=0
// Lagrangian: f(x) + \lambda*g(x)
// state structure: [x y \lambda]
VectorConfig init, state;
init.insert("x", Vector_(1, 1.0));
init.insert("y", Vector_(1, 1.0));
init.insert("L", Vector_(1, 1.0));
state = init;
if (verbose) init.print("Initial State");
// loop until convergence
int maxIt = 10;
for (int i = 0; i<maxIt; ++i) {
if (verbose) cout << "\n******************************\nIteration: " << i+1 << endl;
// extract the states
double x, y, lambda;
x = state["x"](0);
y = state["y"](0);
lambda = state["L"](0);
// calculate the components
Matrix H1, H2, gradG;
Vector gradL, gx;
// hessian of lagrangian function, in two columns:
H1 = Matrix_(2,1,
2.0+2.0*lambda,
0.0);
H2 = Matrix_(2,1,
0.0,
2.0);
// deriviative of lagrangian function
gradL = Vector_(2,
2.0*x*(1+lambda),
2.0*y-lambda);
// constraint derivatives
gradG = Matrix_(2,1,
2.0*x,
0.0);
// constraint value
gx = Vector_(1,
x*x-5-y);
// create a factor for the states
GaussianFactor::shared_ptr f1(new
GaussianFactor("x", H1, "y", H2, "L", gradG, gradL, 1.0));
// create a factor for the lagrange multiplier
GaussianFactor::shared_ptr f2(new
GaussianFactor("x", -sub(gradG, 0, 1, 0, 1),
"y", -sub(gradG, 1, 2, 0, 1), -gx, 0.0));
// construct graph
GaussianFactorGraph fg;
fg.push_back(f1);
fg.push_back(f2);
if (verbose) fg.print("Graph");
// solve
Ordering ord;
ord += "x", "y", "L";
VectorConfig delta = fg.optimize(ord).scale(-1.0);
if (verbose) delta.print("Delta");
// update initial estimate
VectorConfig newState = expmap(state, delta);
state = newState;
if (verbose) state.print("Updated State");
}
// verify that it converges to the nearest optimal point
VectorConfig expected;
expected.insert("L", Vector_(1, -1.0));
expected.insert("x", Vector_(1, 2.12));
expected.insert("y", Vector_(1, -0.5));
CHECK(assert_equal(expected,state, 1e-2));
}
/* *********************************************************************
* This example uses a nonlinear objective function and
* nonlinear equality constraint. This formulation splits
* the constraint into a factor and a linear constraint.
*
* This example uses the same silly number of iterations as the
* previous example.
*/
TEST (SQP, problem1_sqp ) {
bool verbose = false;
// use a nonlinear function of f(x) = x^2+y^2
// nonlinear equality constraint: g(x) = x^2-5-y=0
// Lagrangian: f(x) + \lambda*g(x)
// state structure: [x y \lambda]
VectorConfig init, state;
init.insert("x", Vector_(1, 1.0));
init.insert("y", Vector_(1, 1.0));
init.insert("L", Vector_(1, 1.0));
state = init;
if (verbose) init.print("Initial State");
// loop until convergence
int maxIt = 5;
for (int i = 0; i<maxIt; ++i) {
if (verbose) cout << "\n******************************\nIteration: " << i+1 << endl;
// extract the states
double x, y, lambda;
x = state["x"](0);
y = state["y"](0);
lambda = state["L"](0);
/** create the linear factor
* ||h(x)-z||^2 => ||Ax-b||^2
* where:
* h(x) simply returns the inputs
* z zeros(2)
* A identity
* b linearization point
*/
Matrix A = eye(2);
Vector b = Vector_(2, x, y);
GaussianFactor::shared_ptr f1(
new GaussianFactor("x", sub(A, 0,2, 0,1), // A(:,1)
"y", sub(A, 0,2, 1,2), // A(:,2)
b, // rhs of f(x)
1.0)); // arbitrary sigma
/** create the constraint-linear factor
* Provides a mechanism to use variable gain to force the constraint
* \lambda*gradG*dx + d\lambda = zero
* formulated in matrix form as:
* [\lambda*gradG eye(1)] [dx; d\lambda] = zero
*/
Matrix gradG = Matrix_(1, 2,2*x, -1.0);
GaussianFactor::shared_ptr f2(
new GaussianFactor("x", lambda*sub(gradG, 0,1, 0,1), // scaled gradG(:,1)
"y", lambda*sub(gradG, 0,1, 1,2), // scaled gradG(:,2)
"L", eye(1), // dlambda term
Vector_(1, 0.0), // rhs is zero
1.0)); // arbitrary sigma
// create the actual constraint
// [gradG] [x; y] - g = 0
Vector g = Vector_(1,x*x-y-5);
GaussianFactor::shared_ptr c1(
new GaussianFactor("x", sub(gradG, 0,1, 0,1), // slice first part of gradG
"y", sub(gradG, 0,1, 1,2), // slice second part of gradG
g, // value of constraint function
0.0)); // force to constraint
// construct graph
GaussianFactorGraph fg;
fg.push_back(f1);
fg.push_back(f2);
fg.push_back(c1);
if (verbose) fg.print("Graph");
// solve
Ordering ord;
ord += "x", "y", "L";
VectorConfig delta = fg.optimize(ord);
if (verbose) delta.print("Delta");
// update initial estimate
VectorConfig newState = expmap(state, delta.scale(-1.0));
// set the state to the updated state
state = newState;
if (verbose) state.print("Updated State");
}
// verify that it converges to the nearest optimal point
VectorConfig expected;
expected.insert("x", Vector_(1, 2.12));
expected.insert("y", Vector_(1, -0.5));
CHECK(assert_equal(state["x"], expected["x"], 1e-2));
CHECK(assert_equal(state["y"], expected["y"], 1e-2));
}
/* ********************************************************************* */
typedef simulated2D::Config Config2D;
typedef NonlinearFactorGraph<Config2D> NLGraph;
typedef NonlinearEquality<Config2D, simulated2D::PoseKey, Point2> NLE;
typedef boost::shared_ptr<simulated2D::Measurement > shared;
typedef NonlinearOptimizer<NLGraph, Config2D> Optimizer;
typedef TypedSymbol<Vector, 'L'> LamKey;
/*
* Determining a ground truth linear system
* with two poses seeing one landmark, with each pose
* constrained to a particular value
*/
TEST (SQP, two_pose_truth ) {
bool verbose = false;
// create a graph
shared_ptr<NLGraph> graph(new NLGraph);
// add the constraints on the ends
// position (1, 1) constraint for x1
// position (5, 6) constraint for x2
simulated2D::PoseKey x1(1), x2(2);
simulated2D::PointKey l1(1);
Point2 pt_x1(1.0, 1.0),
pt_x2(5.0, 6.0);
shared_ptr<NLE> ef1(new NLE(x1, pt_x1)),
ef2(new NLE(x2, pt_x2));
graph->push_back(ef1);
graph->push_back(ef2);
// measurement from x1 to l1
Point2 z1(0.0, 5.0);
sharedGaussian sigma(noiseModel::Isotropic::Sigma(2, 0.1));
shared f1(new simulated2D::Measurement(z1, sigma, x1,l1));
graph->push_back(f1);
// measurement from x2 to l1
Point2 z2(-4.0, 0.0);
shared f2(new simulated2D::Measurement(z2, sigma, x2,l1));
graph->push_back(f2);
// create an initial estimate
Point2 pt_l1(
1.0, 6.0 // ground truth
//1.2, 5.6 // small error
);
shared_ptr<Config2D> initialEstimate(new Config2D);
initialEstimate->insert(l1, pt_l1);
initialEstimate->insert(x1, pt_x1);
initialEstimate->insert(x2, pt_x2);
// optimize the graph
shared_ptr<Ordering> ordering(new Ordering());
*ordering += "x1", "x2", "l1";
Optimizer optimizer(graph, ordering, initialEstimate);
// display solution
double relativeThreshold = 1e-5;
double absoluteThreshold = 1e-5;
Optimizer act_opt = optimizer.gaussNewton(relativeThreshold, absoluteThreshold);
boost::shared_ptr<const Config2D> actual = act_opt.config();
if (verbose) actual->print("Configuration after optimization");
// verify
Config2D expected;
expected.insert(x1, pt_x1);
expected.insert(x2, pt_x2);
expected.insert(l1, Point2(1.0, 6.0));
CHECK(assert_equal(expected, *actual, 1e-5));
}
/* ********************************************************************* */
namespace sqp_test1 {
// binary constraint between landmarks
/** g(x) = x-y = 0 */
Vector g(const Config2D& config, const list<Symbol>& keys) {
Point2 pt1, pt2;
pt1 = config[simulated2D::PointKey(keys.front().index())];
pt2 = config[simulated2D::PointKey(keys.back().index())];
return Vector_(2, pt1.x() - pt2.x(), pt1.y() - pt2.y());
}
/** jacobian at l1 */
Matrix G1(const Config2D& config, const list<Symbol>& keys) {
return eye(2);
}
/** jacobian at l2 */
Matrix G2(const Config2D& config, const list<Symbol>& keys) {
return -1 * eye(2);
}
} // \namespace sqp_test1
//namespace sqp_test2 {
//
// // Unary Constraint on x1
// /** g(x) = x -[1;1] = 0 */
// Vector g(const Config2D& config, const list<Symbol>& keys) {
// return config[keys.front()] - Vector_(2, 1.0, 1.0);
// }
//
// /** jacobian at x1 */
// Matrix G(const Config2D& config, const list<Symbol>& keys) {
// return eye(2);
// }
//
//} // \namespace sqp_test2
typedef NonlinearConstraint2<
Config2D, simulated2D::PointKey, Point2, simulated2D::PointKey, Point2> NLC2;
/* *********************************************************************
* Version that actually uses nonlinear equality constraints
* to to perform optimization. Same as above, but no
* equality constraint on x2, and two landmarks that
* should be the same. Note that this is a linear system,
* so it will converge in one step.
*/
TEST (SQP, two_pose ) {
bool verbose = false;
// create the graph
shared_ptr<NLGraph> graph(new NLGraph);
// add the constraints on the ends
// position (1, 1) constraint for x1
// position (5, 6) constraint for x2
simulated2D::PoseKey x1(1), x2(2);
simulated2D::PointKey l1(1), l2(2);
Point2 pt_x1(1.0, 1.0),
pt_x2(5.0, 6.0);
shared_ptr<NLE> ef1(new NLE(x1, pt_x1));
graph->push_back(ef1);
// measurement from x1 to l1
Point2 z1(0.0, 5.0);
sharedGaussian sigma(noiseModel::Isotropic::Sigma(2, 0.1));
shared f1(new simulated2D::Measurement(z1, sigma, x1,l1));
graph->push_back(f1);
// measurement from x2 to l2
Point2 z2(-4.0, 0.0);
shared f2(new simulated2D::Measurement(z2, sigma, x2,l2));
graph->push_back(f2);
// equality constraint between l1 and l2
list<Symbol> keys2; keys2 += "l1", "l2";
boost::shared_ptr<NLC2 > c2(new NLC2(
boost::bind(sqp_test1::g, _1, keys2),
l1, boost::bind(sqp_test1::G1, _1, keys2),
l2, boost::bind(sqp_test1::G2, _1, keys2),
2, "L1"));
graph->push_back(c2);
// create an initial estimate
shared_ptr<Config2D> initialEstimate(new Config2D);
initialEstimate->insert(x1, pt_x1);
initialEstimate->insert(x2, Point2());
initialEstimate->insert(l1, Point2(1.0, 6.0)); // ground truth
initialEstimate->insert(l2, Point2(-4.0, 0.0)); // starting with a separate reference frame
// create an initial estimate for the lagrange multiplier
shared_ptr<VectorConfig> initLagrange(new VectorConfig);
initLagrange->insert(LamKey(1), Vector_(2, 1.0, 1.0)); // connect the landmarks
// create state config variables and initialize them
Config2D state(*initialEstimate);
VectorConfig state_lambda(*initLagrange);
// optimization loop
int maxIt = 1;
for (int i = 0; i<maxIt; ++i) {
// linearize the graph
GaussianFactorGraph fg;
typedef FactorGraph<NonlinearFactor<Config2D> >::const_iterator const_iterator;
// iterate over all factors
for (const_iterator factor = graph->begin(); factor < graph->end(); factor++) {
const shared_ptr<NLC2> constraint = boost::shared_dynamic_cast<NLC2>(*factor);
if (constraint == NULL) {
// if a regular factor, linearize using the default linearization
GaussianFactor::shared_ptr f = (*factor)->linearize(state);
fg.push_back(f);
} else {
// if a constraint, linearize using the constraint method (2 configs)
GaussianFactor::shared_ptr f, c;
boost::tie(f,c) = constraint->linearize(state, state_lambda);
fg.push_back(f);
fg.push_back(c);
}
}
if (verbose) fg.print("Linearized graph");
// create an ordering
Ordering ordering;
ordering += "x1", "x2", "l1", "l2", "L1";
// optimize linear graph to get full delta config
VectorConfig delta = fg.optimize(ordering);
if (verbose) delta.print("Delta Config");
// update both state variables
state = expmap(state, delta);
if (verbose) state.print("newState");
state_lambda = expmap(state_lambda, delta);
if (verbose) state_lambda.print("newStateLam");
}
// verify
Config2D expected;
expected.insert(x1, pt_x1);
expected.insert(l1, Point2(1.0, 6.0));
expected.insert(l2, Point2(1.0, 6.0));
expected.insert(x2, Point2(5.0, 6.0));
CHECK(assert_equal(expected, state, 1e-5));
}
/* ********************************************************************* */
// VSLAM Examples
/* ********************************************************************* */
// make a realistic calibration matrix
double fov = 60; // degrees
size_t w=640,h=480;
Cal3_S2 K(fov,w,h);
boost::shared_ptr<Cal3_S2> shK(new Cal3_S2(K));
using namespace gtsam::visualSLAM;
using namespace boost;
// typedefs for visual SLAM example
typedef visualSLAM::Config VConfig;
typedef visualSLAM::Graph VGraph;
typedef boost::shared_ptr<ProjectionFactor> shared_vf;
typedef NonlinearOptimizer<VGraph,VConfig> VOptimizer;
typedef SQPOptimizer<VGraph, VConfig> VSOptimizer;
typedef NonlinearConstraint2<
VConfig, visualSLAM::PointKey, Pose3, visualSLAM::PointKey, Pose3> VNLC2;
/**
* Ground truth for a visual SLAM example with stereo vision
*/
TEST (SQP, stereo_truth ) {
bool verbose = false;
// create initial estimates
Rot3 faceDownY(Matrix_(3,3,
1.0, 0.0, 0.0,
0.0, 0.0, 1.0,
0.0, 1.0, 0.0));
Pose3 pose1(faceDownY, Point3()); // origin, left camera
SimpleCamera camera1(K, pose1);
Pose3 pose2(faceDownY, Point3(2.0, 0.0, 0.0)); // 2 units to the left
SimpleCamera camera2(K, pose2);
Point3 landmark(1.0, 5.0, 0.0); //centered between the cameras, 5 units away
Point3 landmarkNoisy(1.0, 6.0, 0.0);
// create truth config
boost::shared_ptr<VConfig> truthConfig(new Config);
truthConfig->insert(1, camera1.pose());
truthConfig->insert(2, camera2.pose());
truthConfig->insert(1, landmark);
// create graph
shared_ptr<VGraph> graph(new VGraph());
// create equality constraints for poses
graph->push_back(shared_ptr<PoseConstraint>(new PoseConstraint(1, camera1.pose())));
graph->push_back(shared_ptr<PoseConstraint>(new PoseConstraint(2, camera2.pose())));
// create VSLAM factors
Point2 z1 = camera1.project(landmark);
if (verbose) z1.print("z1");
shared_vf vf1(new ProjectionFactor(z1, 1.0, 1, 1, shK));
graph->push_back(vf1);
Point2 z2 = camera2.project(landmark);
if (verbose) z2.print("z2");
shared_vf vf2(new ProjectionFactor(z2, 1.0, 2, 1, shK));
graph->push_back(vf2);
if (verbose) graph->print("Graph after construction");
// create ordering
shared_ptr<Ordering> ord(new Ordering());
*ord += "x1", "x2", "l1";
// create optimizer
VOptimizer optimizer(graph, ord, truthConfig);
// optimize
VOptimizer afterOneIteration = optimizer.iterate();
// verify
DOUBLES_EQUAL(0.0, optimizer.error(), 1e-9);
// check if correct
if (verbose) afterOneIteration.config()->print("After iteration");
CHECK(assert_equal(*truthConfig,*(afterOneIteration.config())));
}
/* *********************************************************************
* Ground truth for a visual SLAM example with stereo vision
* with some noise injected into the initial config
*/
TEST (SQP, stereo_truth_noisy ) {
bool verbose = false;
// setting to determine how far away the noisy landmark is,
// given that the ground truth is 5m in front of the cameras
double noisyDist = 7.6;
// create initial estimates
Rot3 faceDownY(Matrix_(3,3,
1.0, 0.0, 0.0,
0.0, 0.0, 1.0,
0.0, 1.0, 0.0));
Pose3 pose1(faceDownY, Point3()); // origin, left camera
SimpleCamera camera1(K, pose1);
Pose3 pose2(faceDownY, Point3(2.0, 0.0, 0.0)); // 2 units to the left
SimpleCamera camera2(K, pose2);
Point3 landmark(1.0, 5.0, 0.0); //centered between the cameras, 5 units away
Point3 landmarkNoisy(1.0, noisyDist, 0.0); // initial point is too far out
// create truth config
boost::shared_ptr<Config> truthConfig(new Config);
truthConfig->insert(1, camera1.pose());
truthConfig->insert(2, camera2.pose());
truthConfig->insert(1, landmark);
// create config
boost::shared_ptr<Config> noisyConfig(new Config);
noisyConfig->insert(1, camera1.pose());
noisyConfig->insert(2, camera2.pose());
noisyConfig->insert(1, landmarkNoisy);
// create graph
shared_ptr<Graph> graph(new Graph());
// create equality constraints for poses
graph->push_back(shared_ptr<PoseConstraint>(new PoseConstraint(1, camera1.pose())));
graph->push_back(shared_ptr<PoseConstraint>(new PoseConstraint(2, camera2.pose())));
// create VSLAM factors
Point2 z1 = camera1.project(landmark);
if (verbose) z1.print("z1");
shared_vf vf1(new ProjectionFactor(z1, 1.0, 1, 1, shK));
graph->push_back(vf1);
Point2 z2 = camera2.project(landmark);
if (verbose) z2.print("z2");
shared_vf vf2(new ProjectionFactor(z2, 1.0, 2, 1, shK));
graph->push_back(vf2);
if (verbose) {
graph->print("Graph after construction");
noisyConfig->print("Initial config");
}
// create ordering
shared_ptr<Ordering> ord(new Ordering());
*ord += "x1", "x2", "l1";
// create optimizer
VOptimizer optimizer0(graph, ord, noisyConfig);
if (verbose)
cout << "Initial Error: " << optimizer0.error() << endl;
// use Levenberg-Marquardt optimization
double relThresh = 1e-5, absThresh = 1e-5;
VOptimizer optimizer(optimizer0.levenbergMarquardt(relThresh, absThresh, VOptimizer::SILENT));
// verify
DOUBLES_EQUAL(0.0, optimizer.error(), 1e-9);
// check if correct
if (verbose) {
optimizer.config()->print("After iteration");
cout << "Final error: " << optimizer.error() << endl;
}
CHECK(assert_equal(*truthConfig,*(optimizer.config())));
}
/* ********************************************************************* */
namespace sqp_stereo {
// binary constraint between landmarks
/** g(x) = x-y = 0 */
Vector g(const Config& config, const list<Symbol>& keys) {
return config[PointKey(keys.front().index())].vector()
- config[PointKey(keys.back().index())].vector();
}
/** jacobian at l1 */
Matrix G1(const Config& config, const list<Symbol>& keys) {
return eye(3);
}
/** jacobian at l2 */
Matrix G2(const Config& config, const list<Symbol>& keys) {
return -1.0 * eye(3);
}
} // \namespace sqp_stereo
/* ********************************************************************* */
VGraph stereoExampleGraph() {
// create initial estimates
Rot3 faceDownY(Matrix_(3,3,
1.0, 0.0, 0.0,
0.0, 0.0, 1.0,
0.0, 1.0, 0.0));
Pose3 pose1(faceDownY, Point3()); // origin, left camera
SimpleCamera camera1(K, pose1);
Pose3 pose2(faceDownY, Point3(2.0, 0.0, 0.0)); // 2 units to the left
SimpleCamera camera2(K, pose2);
Point3 landmark1(1.0, 5.0, 0.0); //centered between the cameras, 5 units away
Point3 landmark2(1.0, 5.0, 0.0);
// create graph
VGraph graph;
// create equality constraints for poses
graph.push_back(shared_ptr<PoseConstraint>(new PoseConstraint(1, camera1.pose())));
graph.push_back(shared_ptr<PoseConstraint>(new PoseConstraint(2, camera2.pose())));
// create factors
Point2 z1 = camera1.project(landmark1);
shared_vf vf1(new ProjectionFactor(z1, 1.0, 1, 1, shK));
graph.push_back(vf1);
Point2 z2 = camera2.project(landmark2);
shared_vf vf2(new ProjectionFactor(z2, 1.0, 2, 2, shK));
graph.push_back(vf2);
// create the binary equality constraint between the landmarks
// NOTE: this is really just a linear constraint that is exactly the same
// as the previous examples
list<Symbol> keys; keys += "l1", "l2";
visualSLAM::PointKey l1(1), l2(2);
shared_ptr<VNLC2> c2(
new VNLC2(boost::bind(sqp_stereo::g, _1, keys),
l1, boost::bind(sqp_stereo::G1, _1, keys),
l2, boost::bind(sqp_stereo::G2, _1, keys),
3, "L12"));
graph.push_back(c2);
return graph;
}
/* ********************************************************************* */
boost::shared_ptr<VConfig> stereoExampleTruthConfig() {
// create initial estimates
Rot3 faceDownY(Matrix_(3,3,
1.0, 0.0, 0.0,
0.0, 0.0, 1.0,
0.0, 1.0, 0.0));
Pose3 pose1(faceDownY, Point3()); // origin, left camera
SimpleCamera camera1(K, pose1);
Pose3 pose2(faceDownY, Point3(2.0, 0.0, 0.0)); // 2 units to the left
SimpleCamera camera2(K, pose2);
Point3 landmark1(1.0, 5.0, 0.0); //centered between the cameras, 5 units away
Point3 landmark2(1.0, 5.0, 0.0);
// create config
boost::shared_ptr<Config> truthConfig(new Config);
truthConfig->insert(1, camera1.pose());
truthConfig->insert(2, camera2.pose());
truthConfig->insert(1, landmark1);
truthConfig->insert(2, landmark2); // create two landmarks in same place
return truthConfig;
}
/* *********************************************************************
* SQP version of the above stereo example,
* with the initial case as the ground truth
*/
TEST (SQP, stereo_sqp ) {
bool verbose = false;
// get a graph
VGraph graph = stereoExampleGraph();
if (verbose) graph.print("Graph after construction");
// get the truth config
boost::shared_ptr<VConfig> truthConfig = stereoExampleTruthConfig();
// create ordering
Ordering ord;
ord += "x1", "x2", "l1", "l2";
// create optimizer
VSOptimizer optimizer(graph, ord, truthConfig);
// optimize
VSOptimizer afterOneIteration = optimizer.iterate();
// check if correct
CHECK(assert_equal(*truthConfig,*(afterOneIteration.config())));
}
/* *********************************************************************
* SQP version of the above stereo example,
* with noise in the initial estimate
*/
TEST (SQP, stereo_sqp_noisy ) {
bool verbose = false;
// get a graph
Graph graph = stereoExampleGraph();
// create initial data
Rot3 faceDownY(Matrix_(3,3,
1.0, 0.0, 0.0,
0.0, 0.0, 1.0,
0.0, 1.0, 0.0));
Pose3 pose1(faceDownY, Point3()); // origin, left camera
Pose3 pose2(faceDownY, Point3(2.0, 0.0, 0.0)); // 2 units to the left
Point3 landmark1(0.5, 5.0, 0.0); //centered between the cameras, 5 units away
Point3 landmark2(1.5, 5.0, 0.0);
// noisy config
boost::shared_ptr<Config> initConfig(new Config);
initConfig->insert(1, pose1);
initConfig->insert(2, pose2);
initConfig->insert(1, landmark1);
initConfig->insert(2, landmark2); // create two landmarks in same place
// create ordering
Ordering ord;
ord += "x1", "x2", "l1", "l2";
// create optimizer
VSOptimizer optimizer(graph, ord, initConfig);
// optimize
double start_error = optimizer.error();
int maxIt = 2;
for (int i=0; i<maxIt; ++i) {
if (verbose) cout << "\n ************************** \n"
<< " Iteration: " << i << endl;
//if (verbose) optimizer.graph()->print();
if (verbose) optimizer.config()->print();
if (verbose)
optimizer = optimizer.iterate(VSOptimizer::FULL);
else
optimizer = optimizer.iterate(VSOptimizer::SILENT);
}
if (verbose) cout << "Initial Error: " << start_error << "\n"
<< "Final Error: " << optimizer.error() << endl;
// get the truth config
boost::shared_ptr<Config> truthConfig = stereoExampleTruthConfig();
if (verbose) {
initConfig->print("Initial Config");
truthConfig->print("Truth Config");
optimizer.config()->print("After optimization");
}
// check if correct
CHECK(assert_equal(*truthConfig,*(optimizer.config())));
}
/* *********************************************************************
* SQP version of the above stereo example,
* with noise in the initial estimate and manually specified
* lagrange multipliers
*/
TEST (SQP, stereo_sqp_noisy_manualLagrange ) {
bool verbose = false;
// get a graph
Graph graph = stereoExampleGraph();
// create initial data
Rot3 faceDownY(Matrix_(3,3,
1.0, 0.0, 0.0,
0.0, 0.0, 1.0,
0.0, 1.0, 0.0));
Pose3 pose1(faceDownY, Point3()); // origin, left camera
Pose3 pose2(faceDownY, Point3(2.0, 0.0, 0.0)); // 2 units to the left
Point3 landmark1(0.5, 5.0, 0.0); //centered between the cameras, 5 units away
Point3 landmark2(1.5, 5.0, 0.0);
// noisy config
boost::shared_ptr<Config> initConfig(new Config);
initConfig->insert(1, pose1);
initConfig->insert(2, pose2);
initConfig->insert(1, landmark1);
initConfig->insert(2, landmark2); // create two landmarks in same place
// create ordering with lagrange multiplier included
Ordering ord;
ord += "x1", "x2", "l1", "l2", "L12";
// create lagrange multipliers
VSOptimizer::shared_vconfig initLagrangeConfig(new VectorConfig);
initLagrangeConfig->insert("L12", Vector_(3, 0.0, 0.0, 0.0));
// create optimizer
VSOptimizer optimizer(graph, ord, initConfig, initLagrangeConfig);
// optimize
double start_error = optimizer.error();
int maxIt = 5;
for (int i=0; i<maxIt; ++i) {
if (verbose) {
cout << "\n ************************** \n"
<< " Iteration: " << i << endl;
optimizer.config()->print("Config Before Iteration");
optimizer.configLagrange()->print("Lagrange Before Iteration");
optimizer = optimizer.iterate(VSOptimizer::FULL);
}
else
optimizer = optimizer.iterate(VSOptimizer::SILENT);
}
if (verbose) cout << "Initial Error: " << start_error << "\n"
<< "Final Error: " << optimizer.error() << endl;
// get the truth config
boost::shared_ptr<Config> truthConfig = stereoExampleTruthConfig();
if (verbose) {
initConfig->print("Initial Config");
truthConfig->print("Truth Config");
optimizer.config()->print("After optimization");
}
// check if correct
CHECK(assert_equal(*truthConfig,*(optimizer.config())));
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
/* ************************************************************************* */
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