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gtsam/gtsam/slam/tests/testSmartProjectionFactorP.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 testSmartProjectionFactorP.cpp
* @brief Unit tests for SmartProjectionFactorP Class
* @author Chris Beall
* @author Luca Carlone
* @author Zsolt Kira
* @author Frank Dellaert
* @date August 2021
*/
#include "smartFactorScenarios.h"
#include <gtsam/slam/PoseTranslationPrior.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/base/numericalDerivative.h>
#include <gtsam/base/serializationTestHelpers.h>
#include <CppUnitLite/TestHarness.h>
#include <boost/assign/std/map.hpp>
#include <iostream>
using namespace boost::assign;
using namespace std::placeholders;
static const double rankTol = 1.0;
// Create a noise model for the pixel error
static const double sigma = 0.1;
static SharedIsotropic model(noiseModel::Isotropic::Sigma(2, sigma));
// Convenience for named keys
using symbol_shorthand::X;
using symbol_shorthand::L;
// tests data
static Symbol x1('X', 1);
static Symbol x2('X', 2);
static Symbol x3('X', 3);
static Point2 measurement1(323.0, 240.0);
LevenbergMarquardtParams lmParams;
// Make more verbose like so (in tests):
// params.verbosityLM = LevenbergMarquardtParams::SUMMARY;
/* ************************************************************************* */
TEST( SmartProjectionFactorP, Constructor) {
using namespace vanillaPose;
SmartFactorP::shared_ptr factor1(new SmartFactorP(model));
}
/* ************************************************************************* */
TEST( SmartProjectionFactorP, Constructor2) {
using namespace vanillaPose;
SmartProjectionParams params;
params.setRankTolerance(rankTol);
SmartFactorP factor1(model, params);
}
/* ************************************************************************* */
TEST( SmartProjectionFactorP, Constructor3) {
using namespace vanillaPose;
SmartFactorP::shared_ptr factor1(new SmartFactorP(model));
factor1->add(measurement1, x1, sharedK);
}
/* ************************************************************************* */
TEST( SmartProjectionFactorP, Constructor4) {
using namespace vanillaPose;
SmartProjectionParams params;
params.setRankTolerance(rankTol);
SmartFactorP factor1(model, params);
factor1.add(measurement1, x1, sharedK);
}
/* ************************************************************************* */
TEST( SmartProjectionFactorP, Equals ) {
using namespace vanillaPose;
SmartFactorP::shared_ptr factor1(new SmartFactorP(model));
factor1->add(measurement1, x1, sharedK);
SmartFactorP::shared_ptr factor2(new SmartFactorP(model));
factor2->add(measurement1, x1, sharedK);
CHECK(assert_equal(*factor1, *factor2));
}
/* *************************************************************************/
TEST( SmartProjectionFactorP, noiseless ) {
using namespace vanillaPose;
// Project two landmarks into two cameras
Point2 level_uv = level_camera.project(landmark1);
Point2 level_uv_right = level_camera_right.project(landmark1);
SmartFactorP factor(model);
factor.add(level_uv, x1, sharedK);
factor.add(level_uv_right, x2, sharedK);
Values values; // it's a pose factor, hence these are poses
values.insert(x1, cam1.pose());
values.insert(x2, cam2.pose());
double actualError = factor.error(values);
double expectedError = 0.0;
EXPECT_DOUBLES_EQUAL(expectedError, actualError, 1e-7);
// SmartFactorP::Cameras cameras = factor.cameras(values);
// double actualError2 = factor.totalReprojectionError(cameras);
// EXPECT_DOUBLES_EQUAL(expectedError, actualError2, 1e-7);
//
// // Calculate expected derivative for point (easiest to check)
// std::function<Vector(Point3)> f = //
// std::bind(&SmartFactorP::whitenedError<Point3>, factor, cameras, std::placeholders::_1);
//
// // Calculate using computeEP
// Matrix actualE;
// factor.triangulateAndComputeE(actualE, values);
//
// // get point
// boost::optional<Point3> point = factor.point();
// CHECK(point);
//
// // calculate numerical derivative with triangulated point
// Matrix expectedE = sigma * numericalDerivative11<Vector, Point3>(f, *point);
// EXPECT(assert_equal(expectedE, actualE, 1e-7));
//
// // Calculate using reprojectionError
// SmartFactorP::Cameras::FBlocks F;
// Matrix E;
// Vector actualErrors = factor.unwhitenedError(cameras, *point, F, E);
// EXPECT(assert_equal(expectedE, E, 1e-7));
//
// EXPECT(assert_equal(Z_4x1, actualErrors, 1e-7));
//
// // Calculate using computeJacobians
// Vector b;
// SmartFactorP::FBlocks Fs;
// factor.computeJacobians(Fs, E, b, cameras, *point);
// double actualError3 = b.squaredNorm();
// EXPECT(assert_equal(expectedE, E, 1e-7));
// EXPECT_DOUBLES_EQUAL(expectedError, actualError3, 1e-6);
}
///* *************************************************************************/
//TEST( SmartProjectionFactorP, noisy ) {
//
// using namespace vanillaPose;
//
// // Project two landmarks into two cameras
// Point2 pixelError(0.2, 0.2);
// Point2 level_uv = level_camera.project(landmark1) + pixelError;
// Point2 level_uv_right = level_camera_right.project(landmark1);
//
// Values values;
// values.insert(x1, cam1.pose());
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 10, 0., -M_PI / 10),
// Point3(0.5, 0.1, 0.3));
// values.insert(x2, pose_right.compose(noise_pose));
//
// SmartFactorP::shared_ptr factor(new SmartFactorP(model, sharedK));
// factor->add(level_uv, x1);
// factor->add(level_uv_right, x2);
//
// double actualError1 = factor->error(values);
//
// SmartFactorP::shared_ptr factor2(new SmartFactorP(model, sharedK));
// Point2Vector measurements;
// measurements.push_back(level_uv);
// measurements.push_back(level_uv_right);
//
// KeyVector views {x1, x2};
//
// factor2->add(measurements, views);
// double actualError2 = factor2->error(values);
// DOUBLES_EQUAL(actualError1, actualError2, 1e-7);
//}
//
///* *************************************************************************/
//TEST(SmartProjectionFactorP, smartFactorWithSensorBodyTransform) {
// using namespace vanillaPose;
//
// // create arbitrary body_T_sensor (transforms from sensor to body)
// Pose3 body_T_sensor = Pose3(Rot3::Ypr(-M_PI / 2, 0., -M_PI / 2), Point3(1, 1, 1));
//
// // These are the poses we want to estimate, from camera measurements
// const Pose3 sensor_T_body = body_T_sensor.inverse();
// Pose3 wTb1 = cam1.pose() * sensor_T_body;
// Pose3 wTb2 = cam2.pose() * sensor_T_body;
// Pose3 wTb3 = cam3.pose() * sensor_T_body;
//
// // three landmarks ~5 meters infront of camera
// Point3 landmark1(5, 0.5, 1.2), landmark2(5, -0.5, 1.2), landmark3(5, 0, 3.0);
//
// Point2Vector measurements_cam1, measurements_cam2, measurements_cam3;
//
// // Project three landmarks into three cameras
// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_cam1);
// projectToMultipleCameras(cam1, cam2, cam3, landmark2, measurements_cam2);
// projectToMultipleCameras(cam1, cam2, cam3, landmark3, measurements_cam3);
//
// // Create smart factors
// KeyVector views {x1, x2, x3};
//
// SmartProjectionParams params;
// params.setRankTolerance(1.0);
// params.setDegeneracyMode(IGNORE_DEGENERACY);
// params.setEnableEPI(false);
//
// SmartFactorP smartFactor1(model, sharedK, body_T_sensor, params);
// smartFactor1.add(measurements_cam1, views);
//
// SmartFactorP smartFactor2(model, sharedK, body_T_sensor, params);
// smartFactor2.add(measurements_cam2, views);
//
// SmartFactorP smartFactor3(model, sharedK, body_T_sensor, params);
// smartFactor3.add(measurements_cam3, views);
//
// const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
//
// // Put all factors in factor graph, adding priors
// NonlinearFactorGraph graph;
// graph.push_back(smartFactor1);
// graph.push_back(smartFactor2);
// graph.push_back(smartFactor3);
// graph.addPrior(x1, wTb1, noisePrior);
// graph.addPrior(x2, wTb2, noisePrior);
//
// // Check errors at ground truth poses
// Values gtValues;
// gtValues.insert(x1, wTb1);
// gtValues.insert(x2, wTb2);
// gtValues.insert(x3, wTb3);
// double actualError = graph.error(gtValues);
// double expectedError = 0.0;
// DOUBLES_EQUAL(expectedError, actualError, 1e-7)
//
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
// Point3(0.1, 0.1, 0.1));
// Values values;
// values.insert(x1, wTb1);
// values.insert(x2, wTb2);
// // initialize third pose with some noise, we expect it to move back to
// // original pose3
// values.insert(x3, wTb3 * noise_pose);
//
// LevenbergMarquardtParams lmParams;
// Values result;
// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
// result = optimizer.optimize();
// EXPECT(assert_equal(wTb3, result.at<Pose3>(x3)));
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, 3poses_smart_projection_factor ) {
//
// using namespace vanillaPose2;
// Point2Vector measurements_cam1, measurements_cam2, measurements_cam3;
//
// // Project three landmarks into three cameras
// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_cam1);
// projectToMultipleCameras(cam1, cam2, cam3, landmark2, measurements_cam2);
// projectToMultipleCameras(cam1, cam2, cam3, landmark3, measurements_cam3);
//
// KeyVector views;
// views.push_back(x1);
// views.push_back(x2);
// views.push_back(x3);
//
// SmartFactorP::shared_ptr smartFactor1(new SmartFactorP(model, sharedK2));
// smartFactor1->add(measurements_cam1, views);
//
// SmartFactorP::shared_ptr smartFactor2(new SmartFactorP(model, sharedK2));
// smartFactor2->add(measurements_cam2, views);
//
// SmartFactorP::shared_ptr smartFactor3(new SmartFactorP(model, sharedK2));
// smartFactor3->add(measurements_cam3, views);
//
// const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
//
// NonlinearFactorGraph graph;
// graph.push_back(smartFactor1);
// graph.push_back(smartFactor2);
// graph.push_back(smartFactor3);
// graph.addPrior(x1, cam1.pose(), noisePrior);
// graph.addPrior(x2, cam2.pose(), noisePrior);
//
// Values groundTruth;
// groundTruth.insert(x1, cam1.pose());
// groundTruth.insert(x2, cam2.pose());
// groundTruth.insert(x3, cam3.pose());
// DOUBLES_EQUAL(0, graph.error(groundTruth), 1e-9);
//
// // Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10), Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
// Point3(0.1, 0.1, 0.1)); // smaller noise
// Values values;
// values.insert(x1, cam1.pose());
// values.insert(x2, cam2.pose());
// // initialize third pose with some noise, we expect it to move back to original pose_above
// values.insert(x3, pose_above * noise_pose);
// EXPECT(
// assert_equal(
// Pose3(
// Rot3(0, -0.0314107591, 0.99950656, -0.99950656, -0.0313952598,
// -0.000986635786, 0.0314107591, -0.999013364, -0.0313952598),
// Point3(0.1, -0.1, 1.9)), values.at<Pose3>(x3)));
//
// Values result;
// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
// result = optimizer.optimize();
// EXPECT(assert_equal(pose_above, result.at<Pose3>(x3), 1e-6));
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, Factors ) {
//
// using namespace vanillaPose;
//
// // Default cameras for simple derivatives
// Rot3 R;
// static Cal3_S2::shared_ptr sharedK(new Cal3_S2(100, 100, 0, 0, 0));
// Camera cam1(Pose3(R, Point3(0, 0, 0)), sharedK), cam2(
// Pose3(R, Point3(1, 0, 0)), sharedK);
//
// // one landmarks 1m in front of camera
// Point3 landmark1(0, 0, 10);
//
// Point2Vector measurements_cam1;
//
// // Project 2 landmarks into 2 cameras
// measurements_cam1.push_back(cam1.project(landmark1));
// measurements_cam1.push_back(cam2.project(landmark1));
//
// // Create smart factors
// KeyVector views {x1, x2};
//
// SmartFactorP::shared_ptr smartFactor1 = boost::make_shared<SmartFactorP>(model, sharedK);
// smartFactor1->add(measurements_cam1, views);
//
// SmartFactorP::Cameras cameras;
// cameras.push_back(cam1);
// cameras.push_back(cam2);
//
// // Make sure triangulation works
// CHECK(smartFactor1->triangulateSafe(cameras));
// CHECK(!smartFactor1->isDegenerate());
// CHECK(!smartFactor1->isPointBehindCamera());
// boost::optional<Point3> p = smartFactor1->point();
// CHECK(p);
// EXPECT(assert_equal(landmark1, *p));
//
// VectorValues zeroDelta;
// Vector6 delta;
// delta.setZero();
// zeroDelta.insert(x1, delta);
// zeroDelta.insert(x2, delta);
//
// VectorValues perturbedDelta;
// delta.setOnes();
// perturbedDelta.insert(x1, delta);
// perturbedDelta.insert(x2, delta);
// double expectedError = 2500;
//
// // After eliminating the point, A1 and A2 contain 2-rank information on cameras:
// Matrix16 A1, A2;
// A1 << -10, 0, 0, 0, 1, 0;
// A2 << 10, 0, 1, 0, -1, 0;
// A1 *= 10. / sigma;
// A2 *= 10. / sigma;
// Matrix expectedInformation; // filled below
// {
// // createHessianFactor
// Matrix66 G11 = 0.5 * A1.transpose() * A1;
// Matrix66 G12 = 0.5 * A1.transpose() * A2;
// Matrix66 G22 = 0.5 * A2.transpose() * A2;
//
// Vector6 g1;
// g1.setZero();
// Vector6 g2;
// g2.setZero();
//
// double f = 0;
//
// RegularHessianFactor<6> expected(x1, x2, G11, G12, g1, G22, g2, f);
// expectedInformation = expected.information();
//
// boost::shared_ptr<RegularHessianFactor<6> > actual =
// smartFactor1->createHessianFactor(cameras, 0.0);
// EXPECT(assert_equal(expectedInformation, actual->information(), 1e-6));
// EXPECT(assert_equal(expected, *actual, 1e-6));
// EXPECT_DOUBLES_EQUAL(0, actual->error(zeroDelta), 1e-6);
// EXPECT_DOUBLES_EQUAL(expectedError, actual->error(perturbedDelta), 1e-6);
// }
//
// {
// Matrix26 F1;
// F1.setZero();
// F1(0, 1) = -100;
// F1(0, 3) = -10;
// F1(1, 0) = 100;
// F1(1, 4) = -10;
// Matrix26 F2;
// F2.setZero();
// F2(0, 1) = -101;
// F2(0, 3) = -10;
// F2(0, 5) = -1;
// F2(1, 0) = 100;
// F2(1, 2) = 10;
// F2(1, 4) = -10;
// Matrix E(4, 3);
// E.setZero();
// E(0, 0) = 10;
// E(1, 1) = 10;
// E(2, 0) = 10;
// E(2, 2) = 1;
// E(3, 1) = 10;
// SmartFactorP::FBlocks Fs = list_of<Matrix>(F1)(F2);
// Vector b(4);
// b.setZero();
//
// // Create smart factors
// KeyVector keys;
// keys.push_back(x1);
// keys.push_back(x2);
//
// // createJacobianQFactor
// SharedIsotropic n = noiseModel::Isotropic::Sigma(4, sigma);
// Matrix3 P = (E.transpose() * E).inverse();
// JacobianFactorQ<6, 2> expectedQ(keys, Fs, E, P, b, n);
// EXPECT(assert_equal(expectedInformation, expectedQ.information(), 1e-6));
//
// boost::shared_ptr<JacobianFactorQ<6, 2> > actualQ =
// smartFactor1->createJacobianQFactor(cameras, 0.0);
// CHECK(actualQ);
// EXPECT(assert_equal(expectedInformation, actualQ->information(), 1e-6));
// EXPECT(assert_equal(expectedQ, *actualQ));
// EXPECT_DOUBLES_EQUAL(0, actualQ->error(zeroDelta), 1e-6);
// EXPECT_DOUBLES_EQUAL(expectedError, actualQ->error(perturbedDelta), 1e-6);
//
// // Whiten for RegularImplicitSchurFactor (does not have noise model)
// model->WhitenSystem(E, b);
// Matrix3 whiteP = (E.transpose() * E).inverse();
// Fs[0] = model->Whiten(Fs[0]);
// Fs[1] = model->Whiten(Fs[1]);
//
// // createRegularImplicitSchurFactor
// RegularImplicitSchurFactor<Camera> expected(keys, Fs, E, whiteP, b);
//
// boost::shared_ptr<RegularImplicitSchurFactor<Camera> > actual =
// smartFactor1->createRegularImplicitSchurFactor(cameras, 0.0);
// CHECK(actual);
// EXPECT(assert_equal(expectedInformation, expected.information(), 1e-6));
// EXPECT(assert_equal(expectedInformation, actual->information(), 1e-6));
// EXPECT(assert_equal(expected, *actual));
// EXPECT_DOUBLES_EQUAL(0, actual->error(zeroDelta), 1e-6);
// EXPECT_DOUBLES_EQUAL(expectedError, actual->error(perturbedDelta), 1e-6);
// }
//
// {
// // createJacobianSVDFactor
// Vector1 b;
// b.setZero();
// double s = sigma * sin(M_PI_4);
// SharedIsotropic n = noiseModel::Isotropic::Sigma(4 - 3, sigma);
// JacobianFactor expected(x1, s * A1, x2, s * A2, b, n);
// EXPECT(assert_equal(expectedInformation, expected.information(), 1e-6));
//
// boost::shared_ptr<JacobianFactor> actual =
// smartFactor1->createJacobianSVDFactor(cameras, 0.0);
// CHECK(actual);
// EXPECT(assert_equal(expectedInformation, actual->information(), 1e-6));
// EXPECT(assert_equal(expected, *actual));
// EXPECT_DOUBLES_EQUAL(0, actual->error(zeroDelta), 1e-6);
// EXPECT_DOUBLES_EQUAL(expectedError, actual->error(perturbedDelta), 1e-6);
// }
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, 3poses_iterative_smart_projection_factor ) {
//
// using namespace vanillaPose;
//
// KeyVector views {x1, x2, x3};
//
// Point2Vector measurements_cam1, measurements_cam2, measurements_cam3;
//
// // Project three landmarks into three cameras
// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_cam1);
// projectToMultipleCameras(cam1, cam2, cam3, landmark2, measurements_cam2);
// projectToMultipleCameras(cam1, cam2, cam3, landmark3, measurements_cam3);
//
// SmartFactorP::shared_ptr smartFactor1(new SmartFactorP(model, sharedK));
// smartFactor1->add(measurements_cam1, views);
//
// SmartFactorP::shared_ptr smartFactor2(new SmartFactorP(model, sharedK));
// smartFactor2->add(measurements_cam2, views);
//
// SmartFactorP::shared_ptr smartFactor3(new SmartFactorP(model, sharedK));
// smartFactor3->add(measurements_cam3, views);
//
// const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
//
// NonlinearFactorGraph graph;
// graph.push_back(smartFactor1);
// graph.push_back(smartFactor2);
// graph.push_back(smartFactor3);
// graph.addPrior(x1, cam1.pose(), noisePrior);
// graph.addPrior(x2, cam2.pose(), noisePrior);
//
// // Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10), Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
// Point3(0.1, 0.1, 0.1)); // smaller noise
// Values values;
// values.insert(x1, cam1.pose());
// values.insert(x2, cam2.pose());
// // initialize third pose with some noise, we expect it to move back to original pose_above
// values.insert(x3, pose_above * noise_pose);
// EXPECT(
// assert_equal(
// Pose3(
// Rot3(1.11022302e-16, -0.0314107591, 0.99950656, -0.99950656,
// -0.0313952598, -0.000986635786, 0.0314107591, -0.999013364,
// -0.0313952598), Point3(0.1, -0.1, 1.9)),
// values.at<Pose3>(x3)));
//
// Values result;
// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
// result = optimizer.optimize();
// EXPECT(assert_equal(pose_above, result.at<Pose3>(x3), 1e-7));
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, jacobianSVD ) {
//
// using namespace vanillaPose;
//
// KeyVector views {x1, x2, x3};
//
// Point2Vector measurements_cam1, measurements_cam2, measurements_cam3;
//
// // Project three landmarks into three cameras
// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_cam1);
// projectToMultipleCameras(cam1, cam2, cam3, landmark2, measurements_cam2);
// projectToMultipleCameras(cam1, cam2, cam3, landmark3, measurements_cam3);
//
// SmartProjectionParams params;
// params.setRankTolerance(1.0);
// params.setLinearizationMode(gtsam::JACOBIAN_SVD);
// params.setDegeneracyMode(gtsam::IGNORE_DEGENERACY);
// params.setEnableEPI(false);
// SmartFactorP factor1(model, sharedK, params);
//
// SmartFactorP::shared_ptr smartFactor1(
// new SmartFactorP(model, sharedK, params));
// smartFactor1->add(measurements_cam1, views);
//
// SmartFactorP::shared_ptr smartFactor2(
// new SmartFactorP(model, sharedK, params));
// smartFactor2->add(measurements_cam2, views);
//
// SmartFactorP::shared_ptr smartFactor3(
// new SmartFactorP(model, sharedK, params));
// smartFactor3->add(measurements_cam3, views);
//
// const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
//
// NonlinearFactorGraph graph;
// graph.push_back(smartFactor1);
// graph.push_back(smartFactor2);
// graph.push_back(smartFactor3);
// graph.addPrior(x1, cam1.pose(), noisePrior);
// graph.addPrior(x2, cam2.pose(), noisePrior);
//
// // Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10), Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
// Point3(0.1, 0.1, 0.1)); // smaller noise
// Values values;
// values.insert(x1, cam1.pose());
// values.insert(x2, cam2.pose());
// values.insert(x3, pose_above * noise_pose);
//
// Values result;
// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
// result = optimizer.optimize();
// EXPECT(assert_equal(pose_above, result.at<Pose3>(x3), 1e-6));
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, landmarkDistance ) {
//
// using namespace vanillaPose;
//
// double excludeLandmarksFutherThanDist = 2;
//
// KeyVector views {x1, x2, x3};
//
// Point2Vector measurements_cam1, measurements_cam2, measurements_cam3;
//
// // Project three landmarks into three cameras
// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_cam1);
// projectToMultipleCameras(cam1, cam2, cam3, landmark2, measurements_cam2);
// projectToMultipleCameras(cam1, cam2, cam3, landmark3, measurements_cam3);
//
// SmartProjectionParams params;
// params.setRankTolerance(1.0);
// params.setLinearizationMode(gtsam::JACOBIAN_SVD);
// params.setDegeneracyMode(gtsam::IGNORE_DEGENERACY);
// params.setLandmarkDistanceThreshold(excludeLandmarksFutherThanDist);
// params.setEnableEPI(false);
//
// SmartFactorP::shared_ptr smartFactor1(
// new SmartFactorP(model, sharedK, params));
// smartFactor1->add(measurements_cam1, views);
//
// SmartFactorP::shared_ptr smartFactor2(
// new SmartFactorP(model, sharedK, params));
// smartFactor2->add(measurements_cam2, views);
//
// SmartFactorP::shared_ptr smartFactor3(
// new SmartFactorP(model, sharedK, params));
// smartFactor3->add(measurements_cam3, views);
//
// const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
//
// NonlinearFactorGraph graph;
// graph.push_back(smartFactor1);
// graph.push_back(smartFactor2);
// graph.push_back(smartFactor3);
// graph.addPrior(x1, cam1.pose(), noisePrior);
// graph.addPrior(x2, cam2.pose(), noisePrior);
//
// // Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10), Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
// Point3(0.1, 0.1, 0.1)); // smaller noise
// Values values;
// values.insert(x1, cam1.pose());
// values.insert(x2, cam2.pose());
// values.insert(x3, pose_above * noise_pose);
//
// // All factors are disabled and pose should remain where it is
// Values result;
// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
// result = optimizer.optimize();
// EXPECT(assert_equal(values.at<Pose3>(x3), result.at<Pose3>(x3)));
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, dynamicOutlierRejection ) {
//
// using namespace vanillaPose;
//
// double excludeLandmarksFutherThanDist = 1e10;
// double dynamicOutlierRejectionThreshold = 1; // max 1 pixel of average reprojection error
//
// KeyVector views {x1, x2, x3};
//
// // add fourth landmark
// Point3 landmark4(5, -0.5, 1);
//
// Point2Vector measurements_cam1, measurements_cam2, measurements_cam3,
// measurements_cam4;
//
// // Project 4 landmarks into three cameras
// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_cam1);
// projectToMultipleCameras(cam1, cam2, cam3, landmark2, measurements_cam2);
// projectToMultipleCameras(cam1, cam2, cam3, landmark3, measurements_cam3);
// projectToMultipleCameras(cam1, cam2, cam3, landmark4, measurements_cam4);
// measurements_cam4.at(0) = measurements_cam4.at(0) + Point2(10, 10); // add outlier
//
// SmartProjectionParams params;
// params.setLinearizationMode(gtsam::JACOBIAN_SVD);
// params.setLandmarkDistanceThreshold(excludeLandmarksFutherThanDist);
// params.setDynamicOutlierRejectionThreshold(dynamicOutlierRejectionThreshold);
//
// SmartFactorP::shared_ptr smartFactor1(
// new SmartFactorP(model, sharedK, params));
// smartFactor1->add(measurements_cam1, views);
//
// SmartFactorP::shared_ptr smartFactor2(
// new SmartFactorP(model, sharedK, params));
// smartFactor2->add(measurements_cam2, views);
//
// SmartFactorP::shared_ptr smartFactor3(
// new SmartFactorP(model, sharedK, params));
// smartFactor3->add(measurements_cam3, views);
//
// SmartFactorP::shared_ptr smartFactor4(
// new SmartFactorP(model, sharedK, params));
// smartFactor4->add(measurements_cam4, views);
//
// const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
//
// NonlinearFactorGraph graph;
// graph.push_back(smartFactor1);
// graph.push_back(smartFactor2);
// graph.push_back(smartFactor3);
// graph.push_back(smartFactor4);
// graph.addPrior(x1, cam1.pose(), noisePrior);
// graph.addPrior(x2, cam2.pose(), noisePrior);
//
// Values values;
// values.insert(x1, cam1.pose());
// values.insert(x2, cam2.pose());
// values.insert(x3, cam3.pose());
//
// // All factors are disabled and pose should remain where it is
// Values result;
// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
// result = optimizer.optimize();
// EXPECT(assert_equal(cam3.pose(), result.at<Pose3>(x3)));
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, jacobianQ ) {
//
// using namespace vanillaPose;
//
// KeyVector views {x1, x2, x3};
//
// Point2Vector measurements_cam1, measurements_cam2, measurements_cam3;
//
// // Project three landmarks into three cameras
// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_cam1);
// projectToMultipleCameras(cam1, cam2, cam3, landmark2, measurements_cam2);
// projectToMultipleCameras(cam1, cam2, cam3, landmark3, measurements_cam3);
//
// SmartProjectionParams params;
// params.setLinearizationMode(gtsam::JACOBIAN_Q);
//
// SmartFactorP::shared_ptr smartFactor1(
// new SmartFactorP(model, sharedK, params));
// smartFactor1->add(measurements_cam1, views);
//
// SmartFactorP::shared_ptr smartFactor2(
// new SmartFactorP(model, sharedK, params));
// smartFactor2->add(measurements_cam2, views);
//
// SmartFactorP::shared_ptr smartFactor3(
// new SmartFactorP(model, sharedK, params));
// smartFactor3->add(measurements_cam3, views);
//
// const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
//
// NonlinearFactorGraph graph;
// graph.push_back(smartFactor1);
// graph.push_back(smartFactor2);
// graph.push_back(smartFactor3);
// graph.addPrior(x1, cam1.pose(), noisePrior);
// graph.addPrior(x2, cam2.pose(), noisePrior);
//
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
// Point3(0.1, 0.1, 0.1)); // smaller noise
// Values values;
// values.insert(x1, cam1.pose());
// values.insert(x2, cam2.pose());
// values.insert(x3, pose_above * noise_pose);
//
// Values result;
// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
// result = optimizer.optimize();
// EXPECT(assert_equal(pose_above, result.at<Pose3>(x3), 1e-6));
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, 3poses_projection_factor ) {
//
// using namespace vanillaPose2;
//
// KeyVector views {x1, x2, x3};
//
// typedef GenericProjectionFactor<Pose3, Point3> ProjectionFactor;
// NonlinearFactorGraph graph;
//
// // Project three landmarks into three cameras
// graph.emplace_shared<ProjectionFactor>(cam1.project(landmark1), model, x1, L(1), sharedK2);
// graph.emplace_shared<ProjectionFactor>(cam2.project(landmark1), model, x2, L(1), sharedK2);
// graph.emplace_shared<ProjectionFactor>(cam3.project(landmark1), model, x3, L(1), sharedK2);
//
// graph.emplace_shared<ProjectionFactor>(cam1.project(landmark2), model, x1, L(2), sharedK2);
// graph.emplace_shared<ProjectionFactor>(cam2.project(landmark2), model, x2, L(2), sharedK2);
// graph.emplace_shared<ProjectionFactor>(cam3.project(landmark2), model, x3, L(2), sharedK2);
//
// graph.emplace_shared<ProjectionFactor>(cam1.project(landmark3), model, x1, L(3), sharedK2);
// graph.emplace_shared<ProjectionFactor>(cam2.project(landmark3), model, x2, L(3), sharedK2);
// graph.emplace_shared<ProjectionFactor>(cam3.project(landmark3), model, x3, L(3), sharedK2);
//
// const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
// graph.addPrior(x1, level_pose, noisePrior);
// graph.addPrior(x2, pose_right, noisePrior);
//
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 10, 0., -M_PI / 10),
// Point3(0.5, 0.1, 0.3));
// Values values;
// values.insert(x1, level_pose);
// values.insert(x2, pose_right);
// values.insert(x3, pose_above * noise_pose);
// values.insert(L(1), landmark1);
// values.insert(L(2), landmark2);
// values.insert(L(3), landmark3);
//
// DOUBLES_EQUAL(48406055, graph.error(values), 1);
//
// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
// Values result = optimizer.optimize();
//
// DOUBLES_EQUAL(0, graph.error(result), 1e-9);
//
// EXPECT(assert_equal(pose_above, result.at<Pose3>(x3), 1e-7));
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, CheckHessian) {
//
// KeyVector views {x1, x2, x3};
//
// using namespace vanillaPose;
//
// // Two slightly different cameras
// Pose3 pose2 = level_pose
// * Pose3(Rot3::RzRyRx(-0.05, 0.0, -0.05), Point3(0, 0, 0));
// Pose3 pose3 = pose2 * Pose3(Rot3::RzRyRx(-0.05, 0.0, -0.05), Point3(0, 0, 0));
// Camera cam2(pose2, sharedK);
// Camera cam3(pose3, sharedK);
//
// Point2Vector measurements_cam1, measurements_cam2, measurements_cam3;
//
// // Project three landmarks into three cameras
// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_cam1);
// projectToMultipleCameras(cam1, cam2, cam3, landmark2, measurements_cam2);
// projectToMultipleCameras(cam1, cam2, cam3, landmark3, measurements_cam3);
//
// SmartProjectionParams params;
// params.setRankTolerance(10);
//
// SmartFactorP::shared_ptr smartFactor1(
// new SmartFactorP(model, sharedK, params)); // HESSIAN, by default
// smartFactor1->add(measurements_cam1, views);
//
// SmartFactorP::shared_ptr smartFactor2(
// new SmartFactorP(model, sharedK, params)); // HESSIAN, by default
// smartFactor2->add(measurements_cam2, views);
//
// SmartFactorP::shared_ptr smartFactor3(
// new SmartFactorP(model, sharedK, params)); // HESSIAN, by default
// smartFactor3->add(measurements_cam3, views);
//
// NonlinearFactorGraph graph;
// graph.push_back(smartFactor1);
// graph.push_back(smartFactor2);
// graph.push_back(smartFactor3);
//
// // Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10), Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
// Point3(0.1, 0.1, 0.1)); // smaller noise
// Values values;
// values.insert(x1, cam1.pose());
// values.insert(x2, cam2.pose());
// // initialize third pose with some noise, we expect it to move back to original pose_above
// values.insert(x3, pose3 * noise_pose);
// EXPECT(
// assert_equal(
// Pose3(
// Rot3(0.00563056869, -0.130848107, 0.991386438, -0.991390265,
// -0.130426831, -0.0115837907, 0.130819108, -0.98278564,
// -0.130455917),
// Point3(0.0897734171, -0.110201006, 0.901022872)),
// values.at<Pose3>(x3)));
//
// boost::shared_ptr<GaussianFactor> factor1 = smartFactor1->linearize(values);
// boost::shared_ptr<GaussianFactor> factor2 = smartFactor2->linearize(values);
// boost::shared_ptr<GaussianFactor> factor3 = smartFactor3->linearize(values);
//
// Matrix CumulativeInformation = factor1->information() + factor2->information()
// + factor3->information();
//
// boost::shared_ptr<GaussianFactorGraph> GaussianGraph = graph.linearize(
// values);
// Matrix GraphInformation = GaussianGraph->hessian().first;
//
// // Check Hessian
// EXPECT(assert_equal(GraphInformation, CumulativeInformation, 1e-6));
//
// Matrix AugInformationMatrix = factor1->augmentedInformation()
// + factor2->augmentedInformation() + factor3->augmentedInformation();
//
// // Check Information vector
// Vector InfoVector = AugInformationMatrix.block(0, 18, 18, 1); // 18x18 Hessian + information vector
//
// // Check Hessian
// EXPECT(assert_equal(InfoVector, GaussianGraph->hessian().second, 1e-6));
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, 3poses_2land_rotation_only_smart_projection_factor ) {
// using namespace vanillaPose2;
//
// KeyVector views {x1, x2, x3};
//
// // Two different cameras, at the same position, but different rotations
// Pose3 pose2 = level_pose * Pose3(Rot3::RzRyRx(-0.05, 0.0, -0.05), Point3(0,0,0));
// Pose3 pose3 = pose2 * Pose3(Rot3::RzRyRx(-0.05, 0.0, -0.05), Point3(0,0,0));
// Camera cam2(pose2, sharedK2);
// Camera cam3(pose3, sharedK2);
//
// Point2Vector measurements_cam1, measurements_cam2;
//
// // Project three landmarks into three cameras
// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_cam1);
// projectToMultipleCameras(cam1, cam2, cam3, landmark2, measurements_cam2);
//
// SmartProjectionParams params;
// params.setRankTolerance(50);
// params.setDegeneracyMode(gtsam::HANDLE_INFINITY);
//
// SmartFactorP::shared_ptr smartFactor1(
// new SmartFactorP(model, sharedK2, params));
// smartFactor1->add(measurements_cam1, views);
//
// SmartFactorP::shared_ptr smartFactor2(
// new SmartFactorP(model, sharedK2, params));
// smartFactor2->add(measurements_cam2, views);
//
// const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
// const SharedDiagonal noisePriorTranslation = noiseModel::Isotropic::Sigma(3, 0.10);
// Point3 positionPrior = Point3(0, 0, 1);
//
// NonlinearFactorGraph graph;
// graph.push_back(smartFactor1);
// graph.push_back(smartFactor2);
// graph.addPrior(x1, cam1.pose(), noisePrior);
// graph.emplace_shared<PoseTranslationPrior<Pose3> >(x2, positionPrior, noisePriorTranslation);
// graph.emplace_shared<PoseTranslationPrior<Pose3> >(x3, positionPrior, noisePriorTranslation);
//
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
// Point3(0.1, 0.1, 0.1)); // smaller noise
// Values values;
// values.insert(x1, cam1.pose());
// values.insert(x2, pose2 * noise_pose);
// values.insert(x3, pose3 * noise_pose);
//
// // params.verbosityLM = LevenbergMarquardtParams::SUMMARY;
// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
// Values result = optimizer.optimize();
// EXPECT(assert_equal(pose3, result.at<Pose3>(x3)));
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, 3poses_rotation_only_smart_projection_factor ) {
//
// // this test considers a condition in which the cheirality constraint is triggered
// using namespace vanillaPose;
//
// KeyVector views {x1, x2, x3};
//
// // Two different cameras, at the same position, but different rotations
// Pose3 pose2 = level_pose
// * Pose3(Rot3::RzRyRx(-0.05, 0.0, -0.05), Point3(0, 0, 0));
// Pose3 pose3 = pose2 * Pose3(Rot3::RzRyRx(-0.05, 0.0, -0.05), Point3(0, 0, 0));
// Camera cam2(pose2, sharedK);
// Camera cam3(pose3, sharedK);
//
// Point2Vector measurements_cam1, measurements_cam2, measurements_cam3;
//
// // Project three landmarks into three cameras
// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_cam1);
// projectToMultipleCameras(cam1, cam2, cam3, landmark2, measurements_cam2);
// projectToMultipleCameras(cam1, cam2, cam3, landmark3, measurements_cam3);
//
// SmartProjectionParams params;
// params.setRankTolerance(10);
// params.setDegeneracyMode(gtsam::ZERO_ON_DEGENERACY);
//
// SmartFactorP::shared_ptr smartFactor1(
// new SmartFactorP(model, sharedK, params));
// smartFactor1->add(measurements_cam1, views);
//
// SmartFactorP::shared_ptr smartFactor2(
// new SmartFactorP(model, sharedK, params));
// smartFactor2->add(measurements_cam2, views);
//
// SmartFactorP::shared_ptr smartFactor3(
// new SmartFactorP(model, sharedK, params));
// smartFactor3->add(measurements_cam3, views);
//
// const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
// const SharedDiagonal noisePriorTranslation = noiseModel::Isotropic::Sigma(3,
// 0.10);
// Point3 positionPrior = Point3(0, 0, 1);
//
// NonlinearFactorGraph graph;
// graph.push_back(smartFactor1);
// graph.push_back(smartFactor2);
// graph.push_back(smartFactor3);
// graph.addPrior(x1, cam1.pose(), noisePrior);
// graph.emplace_shared<PoseTranslationPrior<Pose3> >(x2, positionPrior, noisePriorTranslation);
// graph.emplace_shared<PoseTranslationPrior<Pose3> >(x3, positionPrior, noisePriorTranslation);
//
// // Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10), Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
// Point3(0.1, 0.1, 0.1)); // smaller noise
// Values values;
// values.insert(x1, cam1.pose());
// values.insert(x2, cam2.pose());
// values.insert(x3, pose3 * noise_pose);
// EXPECT(
// assert_equal(
// Pose3(
// Rot3(0.00563056869, -0.130848107, 0.991386438, -0.991390265,
// -0.130426831, -0.0115837907, 0.130819108, -0.98278564,
// -0.130455917),
// Point3(0.0897734171, -0.110201006, 0.901022872)),
// values.at<Pose3>(x3)));
//
// Values result;
// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
// result = optimizer.optimize();
//
// // Since we do not do anything on degenerate instances (ZERO_ON_DEGENERACY)
// // rotation remains the same as the initial guess, but position is fixed by PoseTranslationPrior
//#ifdef GTSAM_THROW_CHEIRALITY_EXCEPTION
// EXPECT(assert_equal(Pose3(values.at<Pose3>(x3).rotation(),
// Point3(0,0,1)), result.at<Pose3>(x3)));
//#else
// // if the check is disabled, no cheirality exception if thrown and the pose converges to the right rotation
// // with modest accuracy since the configuration is essentially degenerate without the translation due to noise (noise_pose)
// EXPECT(assert_equal(pose3, result.at<Pose3>(x3),1e-3));
//#endif
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, Hessian ) {
//
// using namespace vanillaPose2;
//
// KeyVector views {x1, x2};
//
// // Project three landmarks into 2 cameras
// Point2 cam1_uv1 = cam1.project(landmark1);
// Point2 cam2_uv1 = cam2.project(landmark1);
// Point2Vector measurements_cam1;
// measurements_cam1.push_back(cam1_uv1);
// measurements_cam1.push_back(cam2_uv1);
//
// SmartFactorP::shared_ptr smartFactor1(new SmartFactorP(model, sharedK2));
// smartFactor1->add(measurements_cam1, views);
//
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 10, 0., -M_PI / 10),
// Point3(0.5, 0.1, 0.3));
// Values values;
// values.insert(x1, cam1.pose());
// values.insert(x2, cam2.pose());
//
// boost::shared_ptr<GaussianFactor> factor = smartFactor1->linearize(values);
//
// // compute triangulation from linearization point
// // compute reprojection errors (sum squared)
// // compare with factor.info(): the bottom right element is the squared sum of the reprojection errors (normalized by the covariance)
// // check that it is correctly scaled when using noiseProjection = [1/4 0; 0 1/4]
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, HessianWithRotation ) {
// // cout << " ************************ SmartProjectionFactorP: rotated Hessian **********************" << endl;
//
// using namespace vanillaPose;
//
// KeyVector views {x1, x2, x3};
//
// Point2Vector measurements_cam1, measurements_cam2, measurements_cam3;
//
// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_cam1);
//
// SmartFactorP::shared_ptr smartFactorInstance(new SmartFactorP(model, sharedK));
// smartFactorInstance->add(measurements_cam1, views);
//
// Values values;
// values.insert(x1, cam1.pose());
// values.insert(x2, cam2.pose());
// values.insert(x3, cam3.pose());
//
// boost::shared_ptr<GaussianFactor> factor = smartFactorInstance->linearize(
// values);
//
// Pose3 poseDrift = Pose3(Rot3::Ypr(-M_PI / 2, 0., -M_PI / 2), Point3(0, 0, 0));
//
// Values rotValues;
// rotValues.insert(x1, poseDrift.compose(level_pose));
// rotValues.insert(x2, poseDrift.compose(pose_right));
// rotValues.insert(x3, poseDrift.compose(pose_above));
//
// boost::shared_ptr<GaussianFactor> factorRot = smartFactorInstance->linearize(
// rotValues);
//
// // Hessian is invariant to rotations in the nondegenerate case
// EXPECT(assert_equal(factor->information(), factorRot->information(), 1e-7));
//
// Pose3 poseDrift2 = Pose3(Rot3::Ypr(-M_PI / 2, -M_PI / 3, -M_PI / 2),
// Point3(10, -4, 5));
//
// Values tranValues;
// tranValues.insert(x1, poseDrift2.compose(level_pose));
// tranValues.insert(x2, poseDrift2.compose(pose_right));
// tranValues.insert(x3, poseDrift2.compose(pose_above));
//
// boost::shared_ptr<GaussianFactor> factorRotTran =
// smartFactorInstance->linearize(tranValues);
//
// // Hessian is invariant to rotations and translations in the nondegenerate case
// EXPECT(assert_equal(factor->information(), factorRotTran->information(), 1e-7));
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, HessianWithRotationDegenerate ) {
//
// using namespace vanillaPose2;
//
// KeyVector views {x1, x2, x3};
//
// // All cameras have the same pose so will be degenerate !
// Camera cam2(level_pose, sharedK2);
// Camera cam3(level_pose, sharedK2);
//
// Point2Vector measurements_cam1;
// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_cam1);
//
// SmartFactorP::shared_ptr smartFactor(new SmartFactorP(model, sharedK2));
// smartFactor->add(measurements_cam1, views);
//
// Values values;
// values.insert(x1, cam1.pose());
// values.insert(x2, cam2.pose());
// values.insert(x3, cam3.pose());
//
// boost::shared_ptr<GaussianFactor> factor = smartFactor->linearize(values);
//
// Pose3 poseDrift = Pose3(Rot3::Ypr(-M_PI / 2, 0., -M_PI / 2), Point3(0, 0, 0));
//
// Values rotValues;
// rotValues.insert(x1, poseDrift.compose(level_pose));
// rotValues.insert(x2, poseDrift.compose(level_pose));
// rotValues.insert(x3, poseDrift.compose(level_pose));
//
// boost::shared_ptr<GaussianFactor> factorRot = //
// smartFactor->linearize(rotValues);
//
// // Hessian is invariant to rotations in the nondegenerate case
// EXPECT(assert_equal(factor->information(), factorRot->information(), 1e-7));
//
// Pose3 poseDrift2 = Pose3(Rot3::Ypr(-M_PI / 2, -M_PI / 3, -M_PI / 2),
// Point3(10, -4, 5));
//
// Values tranValues;
// tranValues.insert(x1, poseDrift2.compose(level_pose));
// tranValues.insert(x2, poseDrift2.compose(level_pose));
// tranValues.insert(x3, poseDrift2.compose(level_pose));
//
// boost::shared_ptr<GaussianFactor> factorRotTran = smartFactor->linearize(
// tranValues);
//
// // Hessian is invariant to rotations and translations in the nondegenerate case
// EXPECT(assert_equal(factor->information(), factorRotTran->information(), 1e-7));
//}
//
///* ************************************************************************* */
//TEST( SmartProjectionFactorP, ConstructorWithCal3Bundler) {
// using namespace bundlerPose;
// SmartProjectionParams params;
// params.setDegeneracyMode(gtsam::ZERO_ON_DEGENERACY);
// SmartFactorP factor(model, sharedBundlerK, params);
// factor.add(measurement1, x1);
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, Cal3Bundler ) {
//
// using namespace bundlerPose;
//
// // three landmarks ~5 meters in front of camera
// Point3 landmark3(3, 0, 3.0);
//
// Point2Vector measurements_cam1, measurements_cam2, measurements_cam3;
//
// // Project three landmarks into three cameras
// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_cam1);
// projectToMultipleCameras(cam1, cam2, cam3, landmark2, measurements_cam2);
// projectToMultipleCameras(cam1, cam2, cam3, landmark3, measurements_cam3);
//
// KeyVector views {x1, x2, x3};
//
// SmartFactorP::shared_ptr smartFactor1(new SmartFactorP(model, sharedBundlerK));
// smartFactor1->add(measurements_cam1, views);
//
// SmartFactorP::shared_ptr smartFactor2(new SmartFactorP(model, sharedBundlerK));
// smartFactor2->add(measurements_cam2, views);
//
// SmartFactorP::shared_ptr smartFactor3(new SmartFactorP(model, sharedBundlerK));
// smartFactor3->add(measurements_cam3, views);
//
// const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
//
// NonlinearFactorGraph graph;
// graph.push_back(smartFactor1);
// graph.push_back(smartFactor2);
// graph.push_back(smartFactor3);
// graph.addPrior(x1, cam1.pose(), noisePrior);
// graph.addPrior(x2, cam2.pose(), noisePrior);
//
// // Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10), Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
// Point3(0.1, 0.1, 0.1)); // smaller noise
// Values values;
// values.insert(x1, cam1.pose());
// values.insert(x2, cam2.pose());
// // initialize third pose with some noise, we expect it to move back to original pose_above
// values.insert(x3, pose_above * noise_pose);
// EXPECT(
// assert_equal(
// Pose3(
// Rot3(0, -0.0314107591, 0.99950656, -0.99950656, -0.0313952598,
// -0.000986635786, 0.0314107591, -0.999013364, -0.0313952598),
// Point3(0.1, -0.1, 1.9)), values.at<Pose3>(x3)));
//
// Values result;
// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
// result = optimizer.optimize();
// EXPECT(assert_equal(cam3.pose(), result.at<Pose3>(x3), 1e-6));
//}
//
///* *************************************************************************/
//TEST( SmartProjectionFactorP, Cal3BundlerRotationOnly ) {
//
// using namespace bundlerPose;
//
// KeyVector views {x1, x2, x3};
//
// // Two different cameras
// Pose3 pose2 = level_pose
// * Pose3(Rot3::RzRyRx(-0.05, 0.0, -0.05), Point3(0, 0, 0));
// Pose3 pose3 = pose2 * Pose3(Rot3::RzRyRx(-0.05, 0.0, -0.05), Point3(0, 0, 0));
// Camera cam2(pose2, sharedBundlerK);
// Camera cam3(pose3, sharedBundlerK);
//
// // landmark3 at 3 meters now
// Point3 landmark3(3, 0, 3.0);
//
// Point2Vector measurements_cam1, measurements_cam2, measurements_cam3;
//
// // Project three landmarks into three cameras
// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_cam1);
// projectToMultipleCameras(cam1, cam2, cam3, landmark2, measurements_cam2);
// projectToMultipleCameras(cam1, cam2, cam3, landmark3, measurements_cam3);
//
// SmartProjectionParams params;
// params.setRankTolerance(10);
// params.setDegeneracyMode(gtsam::ZERO_ON_DEGENERACY);
//
// SmartFactorP::shared_ptr smartFactor1(
// new SmartFactorP(model, sharedBundlerK, params));
// smartFactor1->add(measurements_cam1, views);
//
// SmartFactorP::shared_ptr smartFactor2(
// new SmartFactorP(model, sharedBundlerK, params));
// smartFactor2->add(measurements_cam2, views);
//
// SmartFactorP::shared_ptr smartFactor3(
// new SmartFactorP(model, sharedBundlerK, params));
// smartFactor3->add(measurements_cam3, views);
//
// const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
// const SharedDiagonal noisePriorTranslation = noiseModel::Isotropic::Sigma(3,
// 0.10);
// Point3 positionPrior = Point3(0, 0, 1);
//
// NonlinearFactorGraph graph;
// graph.push_back(smartFactor1);
// graph.push_back(smartFactor2);
// graph.push_back(smartFactor3);
// graph.addPrior(x1, cam1.pose(), noisePrior);
// graph.emplace_shared<PoseTranslationPrior<Pose3> >(x2, positionPrior, noisePriorTranslation);
// graph.emplace_shared<PoseTranslationPrior<Pose3> >(x3, positionPrior, noisePriorTranslation);
//
// // Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10), Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
// Point3(0.1, 0.1, 0.1)); // smaller noise
// Values values;
// values.insert(x1, cam1.pose());
// values.insert(x2, cam2.pose());
// // initialize third pose with some noise, we expect it to move back to original pose_above
// values.insert(x3, pose3 * noise_pose);
// EXPECT(
// assert_equal(
// Pose3(
// Rot3(0.00563056869, -0.130848107, 0.991386438, -0.991390265,
// -0.130426831, -0.0115837907, 0.130819108, -0.98278564,
// -0.130455917),
// Point3(0.0897734171, -0.110201006, 0.901022872)),
// values.at<Pose3>(x3)));
//
// Values result;
// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
// result = optimizer.optimize();
//
// EXPECT(
// assert_equal(
// Pose3(
// Rot3(0.00563056869, -0.130848107, 0.991386438, -0.991390265,
// -0.130426831, -0.0115837907, 0.130819108, -0.98278564,
// -0.130455917),
// Point3(0.0897734171, -0.110201006, 0.901022872)),
// values.at<Pose3>(x3)));
//}
//
///* ************************************************************************* */
//BOOST_CLASS_EXPORT_GUID(gtsam::noiseModel::Constrained, "gtsam_noiseModel_Constrained");
//BOOST_CLASS_EXPORT_GUID(gtsam::noiseModel::Diagonal, "gtsam_noiseModel_Diagonal");
//BOOST_CLASS_EXPORT_GUID(gtsam::noiseModel::Gaussian, "gtsam_noiseModel_Gaussian");
//BOOST_CLASS_EXPORT_GUID(gtsam::noiseModel::Unit, "gtsam_noiseModel_Unit");
//BOOST_CLASS_EXPORT_GUID(gtsam::noiseModel::Isotropic, "gtsam_noiseModel_Isotropic");
//BOOST_CLASS_EXPORT_GUID(gtsam::SharedNoiseModel, "gtsam_SharedNoiseModel");
//BOOST_CLASS_EXPORT_GUID(gtsam::SharedDiagonal, "gtsam_SharedDiagonal");
//
//TEST(SmartProjectionFactorP, serialize) {
// using namespace vanillaPose;
// using namespace gtsam::serializationTestHelpers;
// SmartProjectionParams params;
// params.setRankTolerance(rankTol);
// SmartFactorP factor(model, sharedK, params);
//
// EXPECT(equalsObj(factor));
// EXPECT(equalsXML(factor));
// EXPECT(equalsBinary(factor));
//}
//
//TEST(SmartProjectionFactorP, serialize2) {
// using namespace vanillaPose;
// using namespace gtsam::serializationTestHelpers;
// SmartProjectionParams params;
// params.setRankTolerance(rankTol);
// Pose3 bts;
// SmartFactorP factor(model, sharedK, bts, params);
//
// // insert some measurments
// KeyVector key_view;
// Point2Vector meas_view;
// key_view.push_back(Symbol('x', 1));
// meas_view.push_back(Point2(10, 10));
// factor.add(meas_view, key_view);
//
// EXPECT(equalsObj(factor));
// EXPECT(equalsXML(factor));
// EXPECT(equalsBinary(factor));
//}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */
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