249 lines
8.1 KiB
C++
249 lines
8.1 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/*
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* @file testNonlinearEquality.cpp
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* @author Alex Cunningham
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*/
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#include <CppUnitLite/TestHarness.h>
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#include <gtsam/geometry/Pose2.h>
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/nonlinear/NonlinearEquality.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/NonlinearOptimizer-inl.h>
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#include <gtsam/nonlinear/LieValues-inl.h>
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using namespace std;
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using namespace gtsam;
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typedef TypedSymbol<Pose2, 'x'> PoseKey;
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typedef LieValues<PoseKey> PoseValues;
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typedef PriorFactor<PoseValues, PoseKey> PosePrior;
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typedef NonlinearEquality<PoseValues, PoseKey> PoseNLE;
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typedef boost::shared_ptr<PoseNLE> shared_poseNLE;
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typedef NonlinearFactorGraph<PoseValues> PoseGraph;
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typedef NonlinearOptimizer<PoseGraph,PoseValues> PoseOptimizer;
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PoseKey key(1);
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/* ************************************************************************* */
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TEST ( NonlinearEquality, linearization ) {
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Pose2 value = Pose2(2.1, 1.0, 2.0);
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PoseValues linearize;
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linearize.insert(key, value);
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// create a nonlinear equality constraint
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shared_poseNLE nle(new PoseNLE(key, value));
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// check linearize
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SharedDiagonal constraintModel = noiseModel::Constrained::All(3);
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GaussianFactor expLF(0, eye(3), zero(3), constraintModel);
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GaussianFactor::shared_ptr actualLF = nle->linearize(linearize, *linearize.orderingArbitrary());
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EXPECT(assert_equal(*actualLF, expLF));
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}
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/* ********************************************************************** */
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TEST ( NonlinearEquality, linearization_pose ) {
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PoseKey key(1);
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Pose2 value;
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PoseValues config;
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config.insert(key, value);
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// create a nonlinear equality constraint
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shared_poseNLE nle(new PoseNLE(key, value));
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GaussianFactor::shared_ptr actualLF = nle->linearize(config, *config.orderingArbitrary());
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EXPECT(true);
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}
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/* ********************************************************************** */
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TEST ( NonlinearEquality, linearization_fail ) {
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Pose2 value = Pose2(2.1, 1.0, 2.0);
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Pose2 wrong = Pose2(2.1, 3.0, 4.0);
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PoseValues bad_linearize;
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bad_linearize.insert(key, wrong);
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// create a nonlinear equality constraint
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shared_poseNLE nle(new PoseNLE(key, value));
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// check linearize to ensure that it fails for bad linearization points
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CHECK_EXCEPTION(nle->linearize(bad_linearize, *bad_linearize.orderingArbitrary()), std::invalid_argument);
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}
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/* ********************************************************************** */
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TEST ( NonlinearEquality, linearization_fail_pose ) {
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PoseKey key(1);
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Pose2 value(2.0, 1.0, 2.0),
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wrong(2.0, 3.0, 4.0);
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PoseValues bad_linearize;
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bad_linearize.insert(key, wrong);
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// create a nonlinear equality constraint
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shared_poseNLE nle(new PoseNLE(key, value));
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// check linearize to ensure that it fails for bad linearization points
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CHECK_EXCEPTION(nle->linearize(bad_linearize, *bad_linearize.orderingArbitrary()), std::invalid_argument);
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}
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/* ********************************************************************** */
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TEST ( NonlinearEquality, linearization_fail_pose_origin ) {
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PoseKey key(1);
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Pose2 value,
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wrong(2.0, 3.0, 4.0);
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PoseValues bad_linearize;
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bad_linearize.insert(key, wrong);
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// create a nonlinear equality constraint
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shared_poseNLE nle(new PoseNLE(key, value));
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// check linearize to ensure that it fails for bad linearization points
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CHECK_EXCEPTION(nle->linearize(bad_linearize, *bad_linearize.orderingArbitrary()), std::invalid_argument);
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}
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/* ************************************************************************* */
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TEST ( NonlinearEquality, error ) {
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Pose2 value = Pose2(2.1, 1.0, 2.0);
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Pose2 wrong = Pose2(2.1, 3.0, 4.0);
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PoseValues feasible, bad_linearize;
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feasible.insert(key, value);
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bad_linearize.insert(key, wrong);
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// create a nonlinear equality constraint
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shared_poseNLE nle(new PoseNLE(key, value));
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// check error function outputs
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Vector actual = nle->unwhitenedError(feasible);
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EXPECT(assert_equal(actual, zero(3)));
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actual = nle->unwhitenedError(bad_linearize);
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EXPECT(assert_equal(actual, repeat(3, std::numeric_limits<double>::infinity())));
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}
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/* ************************************************************************* */
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TEST ( NonlinearEquality, equals ) {
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Pose2 value1 = Pose2(2.1, 1.0, 2.0);
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Pose2 value2 = Pose2(2.1, 3.0, 4.0);
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// create some constraints to compare
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shared_poseNLE nle1(new PoseNLE(key, value1));
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shared_poseNLE nle2(new PoseNLE(key, value1));
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shared_poseNLE nle3(new PoseNLE(key, value2));
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// verify
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EXPECT(nle1->equals(*nle2)); // basic equality = true
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EXPECT(nle2->equals(*nle1)); // test symmetry of equals()
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EXPECT(!nle1->equals(*nle3)); // test config
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}
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/* ************************************************************************* */
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TEST ( NonlinearEquality, allow_error_pose ) {
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PoseKey key1(1);
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Pose2 feasible1(1.0, 2.0, 3.0);
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double error_gain = 500.0;
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PoseNLE nle(key1, feasible1, error_gain);
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// the unwhitened error should provide logmap to the feasible state
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Pose2 badPoint1(0.0, 2.0, 3.0);
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Vector actVec = nle.evaluateError(badPoint1);
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Vector expVec = Vector_(3, -0.989992, -0.14112, 0.0);
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EXPECT(assert_equal(expVec, actVec, 1e-5));
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// the actual error should have a gain on it
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PoseValues config;
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config.insert(key1, badPoint1);
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double actError = nle.error(config);
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DOUBLES_EQUAL(500.0, actError, 1e-9);
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// check linearization
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GaussianFactor::shared_ptr actLinFactor = nle.linearize(config, *config.orderingArbitrary());
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Matrix A1 = eye(3,3);
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Vector b = expVec;
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SharedDiagonal model = noiseModel::Constrained::All(3);
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GaussianFactor::shared_ptr expLinFactor(new GaussianFactor(0, A1, b, model));
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EXPECT(assert_equal(*expLinFactor, *actLinFactor, 1e-5));
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}
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/* ************************************************************************* */
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TEST ( NonlinearEquality, allow_error_optimize ) {
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PoseKey key1(1);
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Pose2 feasible1(1.0, 2.0, 3.0);
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double error_gain = 500.0;
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PoseNLE nle(key1, feasible1, error_gain);
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// add to a graph
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boost::shared_ptr<PoseGraph> graph(new PoseGraph());
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graph->add(nle);
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// initialize away from the ideal
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Pose2 initPose(0.0, 2.0, 3.0);
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boost::shared_ptr<PoseValues> init(new PoseValues());
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init->insert(key1, initPose);
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// optimize
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boost::shared_ptr<Ordering> ord(new Ordering());
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ord->push_back(key1);
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NonlinearOptimizationParameters::sharedThis params = NonlinearOptimizationParameters::newDrecreaseThresholds(1e-5, 1e-5);
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PoseOptimizer optimizer(graph, init, ord, params);
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PoseOptimizer result = optimizer.levenbergMarquardt();
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// verify
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PoseValues expected;
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expected.insert(key1, feasible1);
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EXPECT(assert_equal(expected, *result.values()));
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}
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/* ************************************************************************* */
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TEST ( NonlinearEquality, allow_error_optimize_with_factors ) {
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// create a hard constraint
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PoseKey key1(1);
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Pose2 feasible1(1.0, 2.0, 3.0);
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// initialize away from the ideal
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boost::shared_ptr<PoseValues> init(new PoseValues());
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Pose2 initPose(0.0, 2.0, 3.0);
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init->insert(key1, initPose);
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double error_gain = 500.0;
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PoseNLE nle(key1, feasible1, error_gain);
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// create a soft prior that conflicts
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PosePrior prior(key1, initPose, noiseModel::Isotropic::Sigma(3, 0.1));
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// add to a graph
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boost::shared_ptr<PoseGraph> graph(new PoseGraph());
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graph->add(nle);
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graph->add(prior);
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// optimize
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boost::shared_ptr<Ordering> ord(new Ordering());
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ord->push_back(key1);
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NonlinearOptimizationParameters::sharedThis params = NonlinearOptimizationParameters::newDrecreaseThresholds(1e-5, 1e-5);
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PoseOptimizer optimizer(graph, init, ord, params);
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PoseOptimizer result = optimizer.levenbergMarquardt();
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// verify
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PoseValues expected;
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expected.insert(key1, feasible1);
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EXPECT(assert_equal(expected, *result.values()));
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}
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
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/* ************************************************************************* */
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