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gtsam/tests/testMarginals.cpp

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/* ----------------------------------------------------------------------------
* 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 testMarginals.cpp
* @brief
* @author Richard Roberts
* @date May 14, 2012
*/
#include <CppUnitLite/TestHarness.h>
// for all nonlinear keys
#include <gtsam/inference/Symbol.h>
// for points and poses
#include <gtsam/geometry/Point2.h>
#include <gtsam/geometry/Pose2.h>
// for modeling measurement uncertainty - all models included here
#include <gtsam/linear/NoiseModel.h>
// add in headers for specific factors
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/BearingRangeFactor.h>
#include <gtsam/nonlinear/Marginals.h>
using namespace std;
using namespace gtsam;
TEST(Marginals, planarSLAMmarginals) {
// Taken from PlanarSLAMSelfContained_advanced
// create keys for variables
Symbol x1('x',1), x2('x',2), x3('x',3);
Symbol l1('l',1), l2('l',2);
// create graph container and add factors to it
NonlinearFactorGraph graph;
/* add prior */
// gaussian for prior
SharedDiagonal priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
graph += PriorFactor<Pose2>(x1, priorMean, priorNoise); // add the factor to the graph
/* add odometry */
// general noisemodel for odometry
SharedDiagonal odometryNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
Pose2 odometry(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case)
// create between factors to represent odometry
graph += BetweenFactor<Pose2>(x1, x2, odometry, odometryNoise);
graph += BetweenFactor<Pose2>(x2, x3, odometry, odometryNoise);
/* add measurements */
// general noisemodel for measurements
SharedDiagonal measurementNoise = noiseModel::Diagonal::Sigmas(Vector2(0.1, 0.2));
// create the measurement values - indices are (pose id, landmark id)
Rot2 bearing11 = Rot2::fromDegrees(45),
bearing21 = Rot2::fromDegrees(90),
bearing32 = Rot2::fromDegrees(90);
double range11 = sqrt(4.0+4.0),
range21 = 2.0,
range32 = 2.0;
// create bearing/range factors
graph += BearingRangeFactor<Pose2, Point2>(x1, l1, bearing11, range11, measurementNoise);
graph += BearingRangeFactor<Pose2, Point2>(x2, l1, bearing21, range21, measurementNoise);
graph += BearingRangeFactor<Pose2, Point2>(x3, l2, bearing32, range32, measurementNoise);
// linearization point for marginals
Values soln;
soln.insert(x1, Pose2(0.0, 0.0, 0.0));
soln.insert(x2, Pose2(2.0, 0.0, 0.0));
soln.insert(x3, Pose2(4.0, 0.0, 0.0));
soln.insert(l1, Point2(2.0, 2.0));
soln.insert(l2, Point2(4.0, 2.0));
Matrix expectedx1(3,3);
expectedx1 <<
0.09, -7.1942452e-18, -1.27897692e-17,
-7.1942452e-18, 0.09, 1.27897692e-17,
-1.27897692e-17, 1.27897692e-17, 0.01;
Matrix expectedx2(3,3);
expectedx2 <<
0.120967742, -0.00129032258, 0.00451612903,
-0.00129032258, 0.158387097, 0.0206451613,
0.00451612903, 0.0206451613, 0.0177419355;
Matrix expectedx3(3,3);
expectedx3 <<
0.160967742, 0.00774193548, 0.00451612903,
0.00774193548, 0.351935484, 0.0561290323,
0.00451612903, 0.0561290323, 0.0277419355;
Matrix expectedl1(2,2);
expectedl1 <<
0.168709677, -0.0477419355,
-0.0477419355, 0.163548387;
Matrix expectedl2(2,2);
expectedl2 <<
0.293870968, -0.104516129,
-0.104516129, 0.391935484;
// Check marginals covariances for all variables (Cholesky mode)
Marginals marginals(graph, soln, Marginals::CHOLESKY);
EXPECT(assert_equal(expectedx1, marginals.marginalCovariance(x1), 1e-8));
EXPECT(assert_equal(expectedx2, marginals.marginalCovariance(x2), 1e-8));
EXPECT(assert_equal(expectedx3, marginals.marginalCovariance(x3), 1e-8));
EXPECT(assert_equal(expectedl1, marginals.marginalCovariance(l1), 1e-8));
EXPECT(assert_equal(expectedl2, marginals.marginalCovariance(l2), 1e-8));
// Check marginals covariances for all variables (QR mode)
marginals = Marginals(graph, soln, Marginals::QR);
EXPECT(assert_equal(expectedx1, marginals.marginalCovariance(x1), 1e-8));
EXPECT(assert_equal(expectedx2, marginals.marginalCovariance(x2), 1e-8));
EXPECT(assert_equal(expectedx3, marginals.marginalCovariance(x3), 1e-8));
EXPECT(assert_equal(expectedl1, marginals.marginalCovariance(l1), 1e-8));
EXPECT(assert_equal(expectedl2, marginals.marginalCovariance(l2), 1e-8));
// Check joint marginals for 3 variables
Matrix expected_l2x1x3(8,8);
expected_l2x1x3 <<
0.293871159514111, -0.104516127560770, 0.090000180000270, -0.000000000000000, -0.020000000000000, 0.151935669757191, -0.104516127560770, -0.050967744878460,
-0.104516127560770, 0.391935664055174, 0.000000000000000, 0.090000180000270, 0.040000000000000, 0.007741936219615, 0.351935664055174, 0.056129031890193,
0.090000180000270, 0.000000000000000, 0.090000180000270, -0.000000000000000, 0.000000000000000, 0.090000180000270, 0.000000000000000, 0.000000000000000,
-0.000000000000000, 0.090000180000270, -0.000000000000000, 0.090000180000270, 0.000000000000000, -0.000000000000000, 0.090000180000270, 0.000000000000000,
-0.020000000000000, 0.040000000000000, 0.000000000000000, 0.000000000000000, 0.010000000000000, 0.000000000000000, 0.040000000000000, 0.010000000000000,
0.151935669757191, 0.007741936219615, 0.090000180000270, -0.000000000000000, 0.000000000000000, 0.160967924878730, 0.007741936219615, 0.004516127560770,
-0.104516127560770, 0.351935664055174, 0.000000000000000, 0.090000180000270, 0.040000000000000, 0.007741936219615, 0.351935664055174, 0.056129031890193,
-0.050967744878460, 0.056129031890193, 0.000000000000000, 0.000000000000000, 0.010000000000000, 0.004516127560770, 0.056129031890193, 0.027741936219615;
vector<Key> variables(3);
variables[0] = x1;
variables[1] = l2;
variables[2] = x3;
JointMarginal joint_l2x1x3 = marginals.jointMarginalCovariance(variables);
EXPECT(assert_equal(Matrix(expected_l2x1x3.block(0,0,2,2)), Matrix(joint_l2x1x3(l2,l2)), 1e-6));
EXPECT(assert_equal(Matrix(expected_l2x1x3.block(2,0,3,2)), Matrix(joint_l2x1x3(x1,l2)), 1e-6));
EXPECT(assert_equal(Matrix(expected_l2x1x3.block(5,0,3,2)), Matrix(joint_l2x1x3(x3,l2)), 1e-6));
EXPECT(assert_equal(Matrix(expected_l2x1x3.block(0,2,2,3)), Matrix(joint_l2x1x3(l2,x1)), 1e-6));
EXPECT(assert_equal(Matrix(expected_l2x1x3.block(2,2,3,3)), Matrix(joint_l2x1x3(x1,x1)), 1e-6));
EXPECT(assert_equal(Matrix(expected_l2x1x3.block(5,2,3,3)), Matrix(joint_l2x1x3(x3,x1)), 1e-6));
EXPECT(assert_equal(Matrix(expected_l2x1x3.block(0,5,2,3)), Matrix(joint_l2x1x3(l2,x3)), 1e-6));
EXPECT(assert_equal(Matrix(expected_l2x1x3.block(2,5,3,3)), Matrix(joint_l2x1x3(x1,x3)), 1e-6));
EXPECT(assert_equal(Matrix(expected_l2x1x3.block(5,5,3,3)), Matrix(joint_l2x1x3(x3,x3)), 1e-6));
// Check joint marginals for 2 variables (different code path than >2 variable case above)
Matrix expected_l2x1(5,5);
expected_l2x1 <<
0.293871159514111, -0.104516127560770, 0.090000180000270, -0.000000000000000, -0.020000000000000,
-0.104516127560770, 0.391935664055174, 0.000000000000000, 0.090000180000270, 0.040000000000000,
0.090000180000270, 0.000000000000000, 0.090000180000270, -0.000000000000000, 0.000000000000000,
-0.000000000000000, 0.090000180000270, -0.000000000000000, 0.090000180000270, 0.000000000000000,
-0.020000000000000, 0.040000000000000, 0.000000000000000, 0.000000000000000, 0.010000000000000;
variables.resize(2);
variables[0] = l2;
variables[1] = x1;
JointMarginal joint_l2x1 = marginals.jointMarginalCovariance(variables);
EXPECT(assert_equal(Matrix(expected_l2x1.block(0,0,2,2)), Matrix(joint_l2x1(l2,l2)), 1e-6));
EXPECT(assert_equal(Matrix(expected_l2x1.block(2,0,3,2)), Matrix(joint_l2x1(x1,l2)), 1e-6));
EXPECT(assert_equal(Matrix(expected_l2x1.block(0,2,2,3)), Matrix(joint_l2x1(l2,x1)), 1e-6));
EXPECT(assert_equal(Matrix(expected_l2x1.block(2,2,3,3)), Matrix(joint_l2x1(x1,x1)), 1e-6));
// Check joint marginal for 1 variable (different code path than >1 variable cases above)
variables.resize(1);
variables[0] = x1;
JointMarginal joint_x1 = marginals.jointMarginalCovariance(variables);
EXPECT(assert_equal(expectedx1, Matrix(joint_l2x1(x1,x1)), 1e-6));
}
/* ************************************************************************* */
TEST(Marginals, order) {
NonlinearFactorGraph fg;
fg += PriorFactor<Pose2>(0, Pose2(), noiseModel::Unit::Create(3));
fg += BetweenFactor<Pose2>(0, 1, Pose2(1,0,0), noiseModel::Unit::Create(3));
fg += BetweenFactor<Pose2>(1, 2, Pose2(1,0,0), noiseModel::Unit::Create(3));
fg += BetweenFactor<Pose2>(2, 3, Pose2(1,0,0), noiseModel::Unit::Create(3));
Values vals;
vals.insert(0, Pose2());
vals.insert(1, Pose2(1,0,0));
vals.insert(2, Pose2(2,0,0));
vals.insert(3, Pose2(3,0,0));
vals.insert(100, Point2(0,1));
vals.insert(101, Point2(1,1));
fg += BearingRangeFactor<Pose2,Point2>(0, 100,
vals.at<Pose2>(0).bearing(vals.at<Point2>(100)),
vals.at<Pose2>(0).range(vals.at<Point2>(100)), noiseModel::Unit::Create(2));
fg += BearingRangeFactor<Pose2,Point2>(0, 101,
vals.at<Pose2>(0).bearing(vals.at<Point2>(101)),
vals.at<Pose2>(0).range(vals.at<Point2>(101)), noiseModel::Unit::Create(2));
fg += BearingRangeFactor<Pose2,Point2>(1, 100,
vals.at<Pose2>(1).bearing(vals.at<Point2>(100)),
vals.at<Pose2>(1).range(vals.at<Point2>(100)), noiseModel::Unit::Create(2));
fg += BearingRangeFactor<Pose2,Point2>(1, 101,
vals.at<Pose2>(1).bearing(vals.at<Point2>(101)),
vals.at<Pose2>(1).range(vals.at<Point2>(101)), noiseModel::Unit::Create(2));
fg += BearingRangeFactor<Pose2,Point2>(2, 100,
vals.at<Pose2>(2).bearing(vals.at<Point2>(100)),
vals.at<Pose2>(2).range(vals.at<Point2>(100)), noiseModel::Unit::Create(2));
fg += BearingRangeFactor<Pose2,Point2>(2, 101,
vals.at<Pose2>(2).bearing(vals.at<Point2>(101)),
vals.at<Pose2>(2).range(vals.at<Point2>(101)), noiseModel::Unit::Create(2));
fg += BearingRangeFactor<Pose2,Point2>(3, 100,
vals.at<Pose2>(3).bearing(vals.at<Point2>(100)),
vals.at<Pose2>(3).range(vals.at<Point2>(100)), noiseModel::Unit::Create(2));
fg += BearingRangeFactor<Pose2,Point2>(3, 101,
vals.at<Pose2>(3).bearing(vals.at<Point2>(101)),
vals.at<Pose2>(3).range(vals.at<Point2>(101)), noiseModel::Unit::Create(2));
Marginals marginals(fg, vals);
FastVector<Key> keys(fg.keys());
JointMarginal joint = marginals.jointMarginalCovariance(keys);
LONGS_EQUAL(3, (long)joint(0,0).rows());
LONGS_EQUAL(3, (long)joint(1,1).rows());
LONGS_EQUAL(3, (long)joint(2,2).rows());
LONGS_EQUAL(3, (long)joint(3,3).rows());
LONGS_EQUAL(2, (long)joint(100,100).rows());
LONGS_EQUAL(2, (long)joint(101,101).rows());
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
/* ************************************************************************* */
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