gtsam/cpp/testPose2Factor.cpp

118 lines
3.5 KiB
C++

/**
* @file testPose2Constraint.cpp
* @brief Unit tests for Pose2Factor Class
* @authors Frank Dellaert, Viorela Ila
**/
/*STL/C++*/
#include <iostream>
#include <boost/shared_ptr.hpp>
#include <boost/assign/std/list.hpp>
using namespace boost::assign;
#include <CppUnitLite/TestHarness.h>
#include "NonlinearOptimizer-inl.h"
#include "NonlinearEquality.h"
#include "Pose2Factor.h"
#include "Pose2Graph.h"
using namespace std;
using namespace gtsam;
/* ************************************************************************* */
TEST( Pose2Factor, linearize )
{
// create a factor between unknown poses p1 and p2
Pose2 measured(2,2,M_PI_2);
Matrix measurement_covariance = Matrix_(3,3,
0.25, 0.0, 0.0,
0.0, 0.25, 0.0,
0.0, 0.0, 0.01
);
Pose2Factor constraint("p1","p2",measured, measurement_covariance);
// Choose a linearization point
Pose2 p1(1.1,2,M_PI_2); // robot at (1.1,2) looking towards y (ground truth is at 1,2, see testPose2)
Pose2 p2(-1,4.1,M_PI); // robot at (-1,4.1) looking at negative (ground truth is at 4.1,2)
Pose2Config config;
config.insert("p1",p1);
config.insert("p2",p2);
// expected linearization
// we need the minus signs below as "inverse_square_root"
// uses SVD and the sign is simply arbitrary (but still a square root!)
Matrix square_root_inverse_covariance = Matrix_(3,3,
-2.0, 0.0, 0.0,
0.0, -2.0, 0.0,
0.0, 0.0, -10.0
);
Matrix expectedH1 = Matrix_(3,3,
0.0,-1.0,-2.1,
1.0, 0.0,-2.1,
0.0, 0.0,-1.0
);
Matrix expectedH2 = Matrix_(3,3,
1.0, 0.0, 0.0,
0.0, 1.0, 0.0,
0.0, 0.0, 1.0
);
GaussianFactor expected(
"p1", square_root_inverse_covariance*expectedH1,
"p2", square_root_inverse_covariance*expectedH2,
Vector_(3, 0.1, -0.1, 0.0), 1.0);
// Actual linearization
boost::shared_ptr<GaussianFactor> actual = constraint.linearize(config);
CHECK(assert_equal(expected,*actual));
}
/* ************************************************************************* */
bool poseCompare(const std::string& key,
const gtsam::Pose2Config& feasible,
const gtsam::Pose2Config& input) {
return feasible.get(key).equals(input.get(key));
}
/* ************************************************************************* */
TEST(Pose2Factor, optimize) {
// create a Pose graph with one equality constraint and one measurement
Pose2Graph fg;
Pose2Config feasible;
feasible.insert("p0", Pose2(0,0,0));
fg.push_back(Pose2Graph::sharedFactor(
new NonlinearEquality<Pose2Config>("p0", feasible, Pose2().dim(), poseCompare)));
fg.push_back(Pose2Graph::sharedFactor(
new Pose2Factor("p0", "p1", Pose2(1,2,M_PI_2), Matrix_(3,3,
0.5, 0.0, 0.0,
0.0, 0.5, 0.0,
0.0, 0.0, 0.5))));
// Create initial config
boost::shared_ptr<Pose2Config> initial =
boost::shared_ptr<Pose2Config>(new Pose2Config());
initial->insert("p0", Pose2(0,0,0));
initial->insert("p1", Pose2(0,0,0));
// Choose an ordering and optimize
Ordering ordering;
ordering += "p0","p1";
NonlinearOptimizer<Pose2Graph, Pose2Config> optimizer(fg, ordering, initial);
optimizer = optimizer.levenbergMarquardt(1e-15, 1e-15);
// Check with expected config
Pose2Config expected;
expected.insert("p0", Pose2(0,0,0));
expected.insert("p1", Pose2(1,2,M_PI_2));
CHECK(assert_equal(expected, *optimizer.config()));
}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */