137 lines
4.2 KiB
C++
137 lines
4.2 KiB
C++
/**
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* @file testPose2Factor.cpp
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* @brief Unit tests for Pose2Factor Class
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* @authors Frank Dellaert, Viorela Ila
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**/
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#include <CppUnitLite/TestHarness.h>
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#include "numericalDerivative.h"
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#include "pose2SLAM.h"
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using namespace std;
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using namespace gtsam;
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// Common measurement covariance
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static double sx=0.5, sy=0.5,st=0.1;
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static Matrix covariance = Matrix_(3,3,
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sx*sx, 0.0, 0.0,
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0.0, sy*sy, 0.0,
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0.0, 0.0, st*st
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);
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/* ************************************************************************* */
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// Very simple test establishing Ax-b \approx z-h(x)
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TEST( Pose2Factor, error )
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{
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// Choose a linearization point
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Pose2 p1; // robot at origin
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Pose2 p2(1, 0, 0); // robot at (1,0)
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Pose2Config x0;
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x0.insert(1, p1);
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x0.insert(2, p2);
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// Create factor
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Pose2 z = between(p1,p2);
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Pose2Factor factor(1, 2, z, covariance);
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// Actual linearization
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boost::shared_ptr<GaussianFactor> linear = factor.linearize(x0);
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// Check error at x0, i.e. delta = zero !
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VectorConfig delta;
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delta.insert("x1", zero(3));
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delta.insert("x2", zero(3));
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Vector error_at_zero = Vector_(3,0.0,0.0,0.0);
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CHECK(assert_equal(error_at_zero,factor.error_vector(x0)));
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CHECK(assert_equal(-error_at_zero,linear->error_vector(delta)));
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// Check error after increasing p2
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VectorConfig plus = delta + VectorConfig("x2", Vector_(3, 0.1, 0.0, 0.0));
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Pose2Config x1 = expmap(x0, plus);
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Vector error_at_plus = Vector_(3,0.1/sx,0.0,0.0); // h(x)-z = 0.1 !
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CHECK(assert_equal(error_at_plus,factor.error_vector(x1)));
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CHECK(assert_equal(error_at_plus,linear->error_vector(plus)));
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}
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/* ************************************************************************* */
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// common Pose2Factor for tests below
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static Pose2 measured(2,2,M_PI_2);
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static Pose2Factor factor(1,2,measured, covariance);
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/* ************************************************************************* */
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TEST( Pose2Factor, rhs )
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{
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// Choose a linearization point
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Pose2 p1(1.1,2,M_PI_2); // robot at (1.1,2) looking towards y (ground truth is at 1,2, see testPose2)
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Pose2 p2(-1,4.1,M_PI); // robot at (-1,4.1) looking at negative (ground truth is at -1,4)
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Pose2Config x0;
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x0.insert(1,p1);
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x0.insert(2,p2);
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// Actual linearization
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boost::shared_ptr<GaussianFactor> linear = factor.linearize(x0);
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// Check RHS
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Pose2 hx0 = between(p1,p2);
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CHECK(assert_equal(Pose2(2.1, 2.1, M_PI_2),hx0));
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Vector expected_b = Vector_(3, -0.1/sx, 0.1/sy, 0.0);
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CHECK(assert_equal(expected_b,-factor.error_vector(x0)));
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CHECK(assert_equal(expected_b,linear->get_b()));
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}
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/* ************************************************************************* */
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// The error |A*dx-b| approximates (h(x0+dx)-z) = -error_vector
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// Hence i.e., b = approximates z-h(x0) = error_vector(x0)
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Vector h(const Pose2& p1,const Pose2& p2) {
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return factor.evaluateError(p1,p2);
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}
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/* ************************************************************************* */
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TEST( Pose2Factor, linearize )
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{
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// Choose a linearization point at ground truth
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Pose2 p1(1,2,M_PI_2);
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Pose2 p2(-1,4,M_PI);
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Pose2Config x0;
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x0.insert(1,p1);
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x0.insert(2,p2);
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// expected linearization
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Matrix square_root_inverse_covariance = Matrix_(3,3,
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2.0, 0.0, 0.0,
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0.0, 2.0, 0.0,
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0.0, 0.0, 10.0
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);
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Matrix expectedH1 = square_root_inverse_covariance*Matrix_(3,3,
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0.0,-1.0,-2.0,
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1.0, 0.0,-2.0,
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0.0, 0.0,-1.0
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);
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Matrix expectedH2 = square_root_inverse_covariance*Matrix_(3,3,
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1.0, 0.0, 0.0,
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0.0, 1.0, 0.0,
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0.0, 0.0, 1.0
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);
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Vector expected_b = Vector_(3, 0.0, 0.0, 0.0);
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// expected linear factor
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GaussianFactor expected("x1", expectedH1, "x2", expectedH2, expected_b, 1.0);
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// Actual linearization
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boost::shared_ptr<GaussianFactor> actual = factor.linearize(x0);
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CHECK(assert_equal(expected,*actual));
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// Numerical do not work out because BetweenFactor is approximate ?
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Matrix numericalH1 = numericalDerivative21(h, p1, p2, 1e-5);
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CHECK(assert_equal(expectedH1,numericalH1));
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Matrix numericalH2 = numericalDerivative22(h, p1, p2, 1e-5);
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CHECK(assert_equal(expectedH2,numericalH2));
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}
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/* ************************************************************************* */
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int main() {
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TestResult tr;
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return TestRegistry::runAllTests(tr);
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}
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/* ************************************************************************* */
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