269 lines
10 KiB
C++
269 lines
10 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 testOrientedPlane3Factor.cpp
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* @date Dec 19, 2012
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* @author Alex Trevor
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* @brief Tests the OrientedPlane3Factor class
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*/
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#include <gtsam/slam/OrientedPlane3Factor.h>
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#include <gtsam/base/numericalDerivative.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
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#include <gtsam/nonlinear/ISAM2.h>
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#include <CppUnitLite/TestHarness.h>
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#include <boost/assign/std/vector.hpp>
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#include <boost/assign/std.hpp>
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#include <boost/bind.hpp>
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using namespace boost::assign;
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using namespace gtsam;
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using namespace std;
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GTSAM_CONCEPT_TESTABLE_INST(OrientedPlane3)
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GTSAM_CONCEPT_MANIFOLD_INST(OrientedPlane3)
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using symbol_shorthand::P; //< Planes
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using symbol_shorthand::X; //< Pose3
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// *************************************************************************
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TEST(OrientedPlane3Factor, lm_translation_error) {
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// Tests one pose, two measurements of the landmark that differ in range only.
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// Normal along -x, 3m away
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OrientedPlane3 test_lm0(-1.0, 0.0, 0.0, 3.0);
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NonlinearFactorGraph graph;
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// Init pose and prior. Pose Prior is needed since a single plane measurement
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// does not fully constrain the pose
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Pose3 init_pose(Rot3::Ypr(0.0, 0.0, 0.0), Point3(0.0, 0.0, 0.0));
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Vector6 sigmas;
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sigmas << 0.001, 0.001, 0.001, 0.001, 0.001, 0.001;
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graph.addPrior(X(0), init_pose, noiseModel::Diagonal::Sigmas(sigmas));
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// Add two landmark measurements, differing in range
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Vector3 sigmas3 {0.1, 0.1, 0.1};
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Vector4 measurement0 {-1.0, 0.0, 0.0, 3.0};
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OrientedPlane3Factor factor0(
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measurement0, noiseModel::Diagonal::Sigmas(sigmas3), X(0), P(0));
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graph.add(factor0);
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Vector4 measurement1 {-1.0, 0.0, 0.0, 1.0};
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OrientedPlane3Factor factor1(
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measurement1, noiseModel::Diagonal::Sigmas(sigmas3), X(0), P(0));
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graph.add(factor1);
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// Initial Estimate
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Values values;
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values.insert(X(0), init_pose);
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values.insert(P(0), test_lm0);
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// Optimize
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ISAM2 isam2;
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ISAM2Result result = isam2.update(graph, values);
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Values result_values = isam2.calculateEstimate();
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OrientedPlane3 optimized_plane_landmark =
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result_values.at<OrientedPlane3>(P(0));
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// Given two noisy measurements of equal weight, expect result between the two
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OrientedPlane3 expected_plane_landmark(-1.0, 0.0, 0.0, 2.0);
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EXPECT(assert_equal(optimized_plane_landmark, expected_plane_landmark));
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}
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// *************************************************************************
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// TODO As described in PR #564 after correcting the derivatives in
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// OrientedPlane3Factor this test fails. It should be debugged and re-enabled.
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/*
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TEST (OrientedPlane3Factor, lm_rotation_error) {
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// Tests one pose, two measurements of the landmark that differ in angle only.
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// Normal along -x, 3m away
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OrientedPlane3 test_lm0(-1.0/sqrt(1.01), 0.1/sqrt(1.01), 0.0, 3.0);
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NonlinearFactorGraph graph;
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// Init pose and prior. Pose Prior is needed since a single plane measurement does not fully constrain the pose
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Pose3 init_pose(Rot3::Ypr(0.0, 0.0, 0.0), Point3(0.0, 0.0, 0.0));
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graph.addPrior(X(0), init_pose,
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noiseModel::Diagonal::Sigmas(
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(Vector(6) << 0.001, 0.001, 0.001, 0.001, 0.001, 0.001).finished()));
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// Add two landmark measurements, differing in angle
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Vector4 measurement0 {-1.0, 0.0, 0.0, 3.0};
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OrientedPlane3Factor factor0(measurement0,
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noiseModel::Diagonal::Sigmas(Vector3(0.1, 0.1, 0.1)), X(0), P(0));
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graph.add(factor0);
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Vector4 measurement1 {0.0, -1.0, 0.0, 3.0};
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OrientedPlane3Factor factor1(measurement1,
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noiseModel::Diagonal::Sigmas(Vector3(0.1, 0.1, 0.1)), X(0), P(0));
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graph.add(factor1);
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// Initial Estimate
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Values values;
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values.insert(X(0), init_pose);
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values.insert(P(0), test_lm0);
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// Optimize
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ISAM2 isam2;
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ISAM2Result result = isam2.update(graph, values);
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Values result_values = isam2.calculateEstimate();
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auto optimized_plane_landmark = result_values.at<OrientedPlane3>(P(0));
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// Given two noisy measurements of equal weight, expect result between the two
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OrientedPlane3 expected_plane_landmark(-sqrt(2.0) / 2.0, -sqrt(2.0) / 2.0,
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0.0, 3.0);
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EXPECT(assert_equal(optimized_plane_landmark, expected_plane_landmark));
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}
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*/
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// *************************************************************************
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TEST( OrientedPlane3Factor, Derivatives ) {
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// Measurement
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OrientedPlane3 p(sqrt(2)/2, -sqrt(2)/2, 0, 5);
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// Linearisation point
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OrientedPlane3 pLin(sqrt(3)/3, -sqrt(3)/3, sqrt(3)/3, 7);
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gtsam::Point3 pointLin = gtsam::Point3(1, 2, -4);
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gtsam::Rot3 rotationLin = gtsam::Rot3::RzRyRx(0.5*M_PI, -0.3*M_PI, 1.4*M_PI);
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Pose3 poseLin(rotationLin, pointLin);
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// Factor
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Key planeKey(1), poseKey(2);
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SharedGaussian noise = noiseModel::Diagonal::Sigmas(Vector3(0.1, 0.1, 0.1));
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OrientedPlane3Factor factor(p.planeCoefficients(), noise, poseKey, planeKey);
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// Calculate numerical derivatives
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boost::function<Vector(const Pose3&, const OrientedPlane3&)> f = boost::bind(
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&OrientedPlane3Factor::evaluateError, factor, _1, _2, boost::none, boost::none);
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Matrix numericalH1 = numericalDerivative21<Vector, Pose3, OrientedPlane3>(f, poseLin, pLin);
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Matrix numericalH2 = numericalDerivative22<Vector, Pose3, OrientedPlane3>(f, poseLin, pLin);
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// Use the factor to calculate the derivative
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Matrix actualH1, actualH2;
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factor.evaluateError(poseLin, pLin, actualH1, actualH2);
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// Verify we get the expected error
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EXPECT(assert_equal(numericalH1, actualH1, 1e-8));
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EXPECT(assert_equal(numericalH2, actualH2, 1e-8));
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}
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// *************************************************************************
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TEST( OrientedPlane3DirectionPrior, Constructor ) {
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// Example: pitch and roll of aircraft in an ENU Cartesian frame.
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// If pitch and roll are zero for an aerospace frame,
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// that means Z is pointing down, i.e., direction of Z = (0,0,-1)
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Vector4 planeOrientation = (Vector(4) << 0.0, 0.0, -1.0, 10.0).finished(); // all vertical planes directly facing the origin
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// Factor
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Key key(1);
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SharedGaussian model = noiseModel::Diagonal::Sigmas(Vector3(0.1, 0.1, 10.0));
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OrientedPlane3DirectionPrior factor(key, planeOrientation, model);
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// Create a linearization point at the zero-error point
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Vector4 theta1 {0.0, 0.02, -1.2, 10.0};
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Vector4 theta2 {0.0, 0.1, -0.8, 10.0};
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Vector4 theta3 {0.0, 0.2, -0.9, 10.0};
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OrientedPlane3 T1(theta1);
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OrientedPlane3 T2(theta2);
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OrientedPlane3 T3(theta3);
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// Calculate numerical derivatives
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Matrix expectedH1 = numericalDerivative11<Vector, OrientedPlane3>(
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boost::bind(&OrientedPlane3DirectionPrior::evaluateError, &factor, _1,
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boost::none), T1);
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Matrix expectedH2 = numericalDerivative11<Vector, OrientedPlane3>(
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boost::bind(&OrientedPlane3DirectionPrior::evaluateError, &factor, _1,
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boost::none), T2);
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Matrix expectedH3 = numericalDerivative11<Vector, OrientedPlane3>(
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boost::bind(&OrientedPlane3DirectionPrior::evaluateError, &factor, _1,
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boost::none), T3);
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// Use the factor to calculate the derivative
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Matrix actualH1, actualH2, actualH3;
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factor.evaluateError(T1, actualH1);
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factor.evaluateError(T2, actualH2);
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factor.evaluateError(T3, actualH3);
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// Verify we get the expected error
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EXPECT(assert_equal(expectedH1, actualH1, 1e-8));
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EXPECT(assert_equal(expectedH2, actualH2, 1e-8));
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EXPECT(assert_equal(expectedH3, actualH3, 1e-8));
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}
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/* ************************************************************************* */
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// Simplified version of the test by Marco Camurri to debug issue #561
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TEST(OrientedPlane3Factor, Issue561Simplified) {
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// Typedefs
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using Plane = OrientedPlane3;
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NonlinearFactorGraph graph;
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// Setup prior factors
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// Note: If x0 is too far away from the origin (e.g. x=100) this test can fail.
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Pose3 x0(Rot3::identity(), Vector3(10, -1, 1));
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auto x0_noise = noiseModel::Isotropic::Sigma(6, 0.01);
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graph.addPrior<Pose3>(X(0), x0, x0_noise);
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// Two horizontal planes with different heights, in the world frame.
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const Plane p1(0,0,1,1), p2(0,0,1,2);
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auto p1_noise = noiseModel::Diagonal::Sigmas(Vector3{1, 1, 5});
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auto p2_noise = noiseModel::Diagonal::Sigmas(Vector3{1, 1, 5});
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graph.addPrior<Plane>(P(1), p1, p1_noise);
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graph.addPrior<Plane>(P(2), p2, p2_noise);
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// Plane factors
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auto p1_measured_from_x0 = p1.transform(x0); // transform p1 to pose x0 as a measurement
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auto p2_measured_from_x0 = p2.transform(x0); // transform p2 to pose x0 as a measurement
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const auto x0_p1_noise = noiseModel::Isotropic::Sigma(3, 0.05);
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const auto x0_p2_noise = noiseModel::Isotropic::Sigma(3, 0.05);
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graph.emplace_shared<OrientedPlane3Factor>(
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p1_measured_from_x0.planeCoefficients(), x0_p1_noise, X(0), P(1));
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graph.emplace_shared<OrientedPlane3Factor>(
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p2_measured_from_x0.planeCoefficients(), x0_p2_noise, X(0), P(2));
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// Initial values
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// Just offset the initial pose by 1m. This is what we are trying to optimize.
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Values initialEstimate;
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Pose3 x0_initial = x0.compose(Pose3(Rot3::identity(), Vector3(1,0,0)));
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initialEstimate.insert(P(1), p1);
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initialEstimate.insert(P(2), p2);
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initialEstimate.insert(X(0), x0_initial);
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// Optimize
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try {
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GaussNewtonOptimizer optimizer(graph, initialEstimate);
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Values result = optimizer.optimize();
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EXPECT_DOUBLES_EQUAL(0, graph.error(result), 0.1);
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EXPECT(x0.equals(result.at<Pose3>(X(0))));
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EXPECT(p1.equals(result.at<Plane>(P(1))));
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EXPECT(p2.equals(result.at<Plane>(P(2))));
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} catch (const IndeterminantLinearSystemException &e) {
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std::cerr << "CAPTURED THE EXCEPTION: " << DefaultKeyFormatter(e.nearbyVariable()) << std::endl;
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EXPECT(false); // fail if this happens
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}
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}
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/* ************************************************************************* */
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int main() {
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srand(time(nullptr));
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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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