all tests are passing!
lcarlone
parent
4fd6c2cb5d
commit
0797eae9a8
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@ -724,429 +724,425 @@ TEST(SmartProjectionPoseFactorRollingShutter,
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EXPECT(assert_equal(pose_above, result.at<Pose3>(x3), 1e-6));
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}
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///* *************************************************************************/
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//TEST(SmartProjectionPoseFactorRollingShutter,
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// hessianComparedToProjFactorsRollingShutter) {
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// using namespace vanillaPoseRS;
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// Point2Vector measurements_lmk1;
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//
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// // Project three landmarks into three cameras
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// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_lmk1);
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//
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// // create inputs
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// std::vector<std::pair<Key, Key>> key_pairs;
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// key_pairs.push_back(std::make_pair(x1, x2));
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// key_pairs.push_back(std::make_pair(x2, x3));
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// key_pairs.push_back(std::make_pair(x3, x1));
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//
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// std::vector<double> interp_factors;
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// interp_factors.push_back(interp_factor1);
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// interp_factors.push_back(interp_factor2);
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// interp_factors.push_back(interp_factor3);
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//
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// SmartFactorRS::shared_ptr smartFactor1(new SmartFactorRS(model));
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// smartFactor1->add(measurements_lmk1, key_pairs, interp_factors, sharedK);
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//
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// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
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// Point3(0.1, 0.1, 0.1)); // smaller noise
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// Values values;
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// values.insert(x1, level_pose);
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// values.insert(x2, pose_right);
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// // initialize third pose with some noise to get a nontrivial linearization
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// // point
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// values.insert(x3, pose_above * noise_pose);
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// EXPECT( // check that the pose is actually noisy
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// assert_equal(Pose3(Rot3(0, -0.0314107591, 0.99950656, -0.99950656,
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// -0.0313952598, -0.000986635786, 0.0314107591,
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// -0.999013364, -0.0313952598),
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// Point3(0.1, -0.1, 1.9)),
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// values.at<Pose3>(x3)));
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//
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// // linearization point for the poses
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// Pose3 pose1 = level_pose;
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// Pose3 pose2 = pose_right;
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// Pose3 pose3 = pose_above * noise_pose;
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//
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// // ==== check Hessian of smartFactor1 =====
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// // -- compute actual Hessian
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// boost::shared_ptr<GaussianFactor> linearfactor1 =
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// smartFactor1->linearize(values);
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// Matrix actualHessian = linearfactor1->information();
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//
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// // -- compute expected Hessian from manual Schur complement from Jacobians
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// // linearization point for the 3D point
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// smartFactor1->triangulateSafe(smartFactor1->cameras(values));
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// TriangulationResult point = smartFactor1->point();
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// EXPECT(point.valid()); // check triangulated point is valid
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//
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// // Use the factor to calculate the Jacobians
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// Matrix F = Matrix::Zero(2 * 3, 6 * 3);
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// Matrix E = Matrix::Zero(2 * 3, 3);
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// Vector b = Vector::Zero(6);
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//
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// // create projection factors rolling shutter
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// ProjectionFactorRollingShutter factor11(measurements_lmk1[0], interp_factor1,
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// model, x1, x2, l0, sharedK);
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// Matrix H1Actual, H2Actual, H3Actual;
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// // note: b is minus the reprojection error, cf the smart factor jacobian
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// // computation
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// b.segment<2>(0) = -factor11.evaluateError(pose1, pose2, *point, H1Actual,
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// H2Actual, H3Actual);
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// F.block<2, 6>(0, 0) = H1Actual;
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// F.block<2, 6>(0, 6) = H2Actual;
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// E.block<2, 3>(0, 0) = H3Actual;
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//
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// ProjectionFactorRollingShutter factor12(measurements_lmk1[1], interp_factor2,
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// model, x2, x3, l0, sharedK);
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// b.segment<2>(2) = -factor12.evaluateError(pose2, pose3, *point, H1Actual,
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// H2Actual, H3Actual);
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// F.block<2, 6>(2, 6) = H1Actual;
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// F.block<2, 6>(2, 12) = H2Actual;
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// E.block<2, 3>(2, 0) = H3Actual;
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//
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// ProjectionFactorRollingShutter factor13(measurements_lmk1[2], interp_factor3,
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// model, x3, x1, l0, sharedK);
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// b.segment<2>(4) = -factor13.evaluateError(pose3, pose1, *point, H1Actual,
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// H2Actual, H3Actual);
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// F.block<2, 6>(4, 12) = H1Actual;
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// F.block<2, 6>(4, 0) = H2Actual;
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// E.block<2, 3>(4, 0) = H3Actual;
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//
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// // whiten
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// F = (1 / sigma) * F;
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// E = (1 / sigma) * E;
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// b = (1 / sigma) * b;
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// //* G = F' * F - F' * E * P * E' * F
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// Matrix P = (E.transpose() * E).inverse();
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// Matrix expectedHessian =
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// F.transpose() * F - (F.transpose() * E * P * E.transpose() * F);
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// EXPECT(assert_equal(expectedHessian, actualHessian, 1e-6));
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//
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// // ==== check Information vector of smartFactor1 =====
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// GaussianFactorGraph gfg;
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// gfg.add(linearfactor1);
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// Matrix actualHessian_v2 = gfg.hessian().first;
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// EXPECT(assert_equal(actualHessian_v2, actualHessian,
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// 1e-6)); // sanity check on hessian
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//
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// // -- compute actual information vector
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// Vector actualInfoVector = gfg.hessian().second;
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//
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// // -- compute expected information vector from manual Schur complement from
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// // Jacobians
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// //* g = F' * (b - E * P * E' * b)
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// Vector expectedInfoVector = F.transpose() * (b - E * P * E.transpose() * b);
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// EXPECT(assert_equal(expectedInfoVector, actualInfoVector, 1e-6));
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//
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// // ==== check error of smartFactor1 (again) =====
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// NonlinearFactorGraph nfg_projFactorsRS;
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// nfg_projFactorsRS.add(factor11);
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// nfg_projFactorsRS.add(factor12);
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// nfg_projFactorsRS.add(factor13);
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// values.insert(l0, *point);
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//
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// double actualError = smartFactor1->error(values);
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// double expectedError = nfg_projFactorsRS.error(values);
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// EXPECT_DOUBLES_EQUAL(expectedError, actualError, 1e-7);
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//}
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//
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///* *************************************************************************/
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//TEST(SmartProjectionPoseFactorRollingShutter,
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// hessianComparedToProjFactorsRollingShutter_measurementsFromSamePose) {
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// // in this test we make sure the fact works even if we have multiple pixel
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// // measurements of the same landmark at a single pose, a setup that occurs in
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// // multi-camera systems
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//
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// using namespace vanillaPoseRS;
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// Point2Vector measurements_lmk1;
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//
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// // Project three landmarks into three cameras
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// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_lmk1);
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//
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// // create redundant measurements:
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// Camera::MeasurementVector measurements_lmk1_redundant = measurements_lmk1;
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// measurements_lmk1_redundant.push_back(
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// measurements_lmk1.at(0)); // we readd the first measurement
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//
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// // create inputs
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// std::vector<std::pair<Key, Key>> key_pairs;
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// key_pairs.push_back(std::make_pair(x1, x2));
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// key_pairs.push_back(std::make_pair(x2, x3));
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// key_pairs.push_back(std::make_pair(x3, x1));
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// key_pairs.push_back(std::make_pair(x1, x2));
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//
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// std::vector<double> interp_factors;
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// interp_factors.push_back(interp_factor1);
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// interp_factors.push_back(interp_factor2);
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// interp_factors.push_back(interp_factor3);
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// interp_factors.push_back(interp_factor1);
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//
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// SmartFactorRS::shared_ptr smartFactor1(new SmartFactorRS(model));
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// smartFactor1->add(measurements_lmk1_redundant, key_pairs, interp_factors,
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// sharedK);
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//
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// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
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// Point3(0.1, 0.1, 0.1)); // smaller noise
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// Values values;
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// values.insert(x1, level_pose);
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// values.insert(x2, pose_right);
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// // initialize third pose with some noise to get a nontrivial linearization
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// // point
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// values.insert(x3, pose_above * noise_pose);
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// EXPECT( // check that the pose is actually noisy
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// assert_equal(Pose3(Rot3(0, -0.0314107591, 0.99950656, -0.99950656,
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// -0.0313952598, -0.000986635786, 0.0314107591,
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// -0.999013364, -0.0313952598),
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// Point3(0.1, -0.1, 1.9)),
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// values.at<Pose3>(x3)));
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//
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// // linearization point for the poses
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// Pose3 pose1 = level_pose;
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// Pose3 pose2 = pose_right;
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// Pose3 pose3 = pose_above * noise_pose;
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//
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// // ==== check Hessian of smartFactor1 =====
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// // -- compute actual Hessian
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// boost::shared_ptr<GaussianFactor> linearfactor1 =
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// smartFactor1->linearize(values);
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// Matrix actualHessian = linearfactor1->information();
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//
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// // -- compute expected Hessian from manual Schur complement from Jacobians
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// // linearization point for the 3D point
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// smartFactor1->triangulateSafe(smartFactor1->cameras(values));
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// TriangulationResult point = smartFactor1->point();
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// EXPECT(point.valid()); // check triangulated point is valid
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//
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// // Use standard ProjectionFactorRollingShutter factor to calculate the
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// // Jacobians
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// Matrix F = Matrix::Zero(2 * 4, 6 * 3);
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// Matrix E = Matrix::Zero(2 * 4, 3);
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// Vector b = Vector::Zero(8);
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//
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// // create projection factors rolling shutter
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// ProjectionFactorRollingShutter factor11(measurements_lmk1_redundant[0],
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// interp_factor1, model, x1, x2, l0,
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// sharedK);
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// Matrix H1Actual, H2Actual, H3Actual;
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// // note: b is minus the reprojection error, cf the smart factor jacobian
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// // computation
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// b.segment<2>(0) = -factor11.evaluateError(pose1, pose2, *point, H1Actual,
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// H2Actual, H3Actual);
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// F.block<2, 6>(0, 0) = H1Actual;
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// F.block<2, 6>(0, 6) = H2Actual;
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// E.block<2, 3>(0, 0) = H3Actual;
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//
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// ProjectionFactorRollingShutter factor12(measurements_lmk1_redundant[1],
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// interp_factor2, model, x2, x3, l0,
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// sharedK);
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// b.segment<2>(2) = -factor12.evaluateError(pose2, pose3, *point, H1Actual,
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// H2Actual, H3Actual);
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// F.block<2, 6>(2, 6) = H1Actual;
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// F.block<2, 6>(2, 12) = H2Actual;
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// E.block<2, 3>(2, 0) = H3Actual;
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//
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// ProjectionFactorRollingShutter factor13(measurements_lmk1_redundant[2],
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// interp_factor3, model, x3, x1, l0,
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// sharedK);
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// b.segment<2>(4) = -factor13.evaluateError(pose3, pose1, *point, H1Actual,
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// H2Actual, H3Actual);
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// F.block<2, 6>(4, 12) = H1Actual;
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// F.block<2, 6>(4, 0) = H2Actual;
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// E.block<2, 3>(4, 0) = H3Actual;
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//
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// ProjectionFactorRollingShutter factor14(measurements_lmk1_redundant[3],
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// interp_factor1, model, x1, x2, l0,
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// sharedK);
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// b.segment<2>(6) = -factor11.evaluateError(pose1, pose2, *point, H1Actual,
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// H2Actual, H3Actual);
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// F.block<2, 6>(6, 0) = H1Actual;
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// F.block<2, 6>(6, 6) = H2Actual;
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// E.block<2, 3>(6, 0) = H3Actual;
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//
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// // whiten
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// F = (1 / sigma) * F;
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// E = (1 / sigma) * E;
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// b = (1 / sigma) * b;
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// //* G = F' * F - F' * E * P * E' * F
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// Matrix P = (E.transpose() * E).inverse();
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// Matrix expectedHessian =
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// F.transpose() * F - (F.transpose() * E * P * E.transpose() * F);
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// EXPECT(assert_equal(expectedHessian, actualHessian, 1e-6));
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//
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// // ==== check Information vector of smartFactor1 =====
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// GaussianFactorGraph gfg;
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// gfg.add(linearfactor1);
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// Matrix actualHessian_v2 = gfg.hessian().first;
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// EXPECT(assert_equal(actualHessian_v2, actualHessian,
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// 1e-6)); // sanity check on hessian
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//
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// // -- compute actual information vector
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// Vector actualInfoVector = gfg.hessian().second;
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//
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// // -- compute expected information vector from manual Schur complement from
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// // Jacobians
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// //* g = F' * (b - E * P * E' * b)
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// Vector expectedInfoVector = F.transpose() * (b - E * P * E.transpose() * b);
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// EXPECT(assert_equal(expectedInfoVector, actualInfoVector, 1e-6));
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//
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// // ==== check error of smartFactor1 (again) =====
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// NonlinearFactorGraph nfg_projFactorsRS;
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// nfg_projFactorsRS.add(factor11);
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// nfg_projFactorsRS.add(factor12);
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// nfg_projFactorsRS.add(factor13);
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// nfg_projFactorsRS.add(factor14);
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// values.insert(l0, *point);
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//
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// double actualError = smartFactor1->error(values);
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// double expectedError = nfg_projFactorsRS.error(values);
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// EXPECT_DOUBLES_EQUAL(expectedError, actualError, 1e-7);
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//}
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//
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///* *************************************************************************/
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//TEST(SmartProjectionPoseFactorRollingShutter,
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// optimization_3poses_measurementsFromSamePose) {
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// using namespace vanillaPoseRS;
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// Point2Vector measurements_lmk1, measurements_lmk2, measurements_lmk3;
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//
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// // Project three landmarks into three cameras
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// projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_lmk1);
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// projectToMultipleCameras(cam1, cam2, cam3, landmark2, measurements_lmk2);
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// projectToMultipleCameras(cam1, cam2, cam3, landmark3, measurements_lmk3);
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//
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// // create inputs
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// std::vector<std::pair<Key, Key>> key_pairs;
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// key_pairs.push_back(std::make_pair(x1, x2));
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// key_pairs.push_back(std::make_pair(x2, x3));
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// key_pairs.push_back(std::make_pair(x3, x1));
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//
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// std::vector<double> interp_factors;
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// interp_factors.push_back(interp_factor1);
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// interp_factors.push_back(interp_factor2);
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// interp_factors.push_back(interp_factor3);
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//
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// // For first factor, we create redundant measurement (taken by the same keys
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// // as factor 1, to make sure the redundancy in the keys does not create
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// // problems)
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// Camera::MeasurementVector& measurements_lmk1_redundant = measurements_lmk1;
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// measurements_lmk1_redundant.push_back(
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// measurements_lmk1.at(0)); // we readd the first measurement
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// std::vector<std::pair<Key, Key>> key_pairs_redundant = key_pairs;
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// key_pairs_redundant.push_back(
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// key_pairs.at(0)); // we readd the first pair of keys
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// std::vector<double> interp_factors_redundant = interp_factors;
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// interp_factors_redundant.push_back(
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// interp_factors.at(0)); // we readd the first interp factor
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//
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// SmartFactorRS::shared_ptr smartFactor1(new SmartFactorRS(model));
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// smartFactor1->add(measurements_lmk1_redundant, key_pairs_redundant,
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// interp_factors_redundant, sharedK);
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//
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// SmartFactorRS::shared_ptr smartFactor2(new SmartFactorRS(model));
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// smartFactor2->add(measurements_lmk2, key_pairs, interp_factors, sharedK);
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//
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// SmartFactorRS::shared_ptr smartFactor3(new SmartFactorRS(model));
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// smartFactor3->add(measurements_lmk3, key_pairs, interp_factors, sharedK);
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//
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// const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
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//
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// NonlinearFactorGraph graph;
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// graph.push_back(smartFactor1);
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// graph.push_back(smartFactor2);
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// graph.push_back(smartFactor3);
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// graph.addPrior(x1, level_pose, noisePrior);
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// graph.addPrior(x2, pose_right, noisePrior);
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//
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// Values groundTruth;
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// groundTruth.insert(x1, level_pose);
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// groundTruth.insert(x2, pose_right);
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// groundTruth.insert(x3, pose_above);
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// DOUBLES_EQUAL(0, graph.error(groundTruth), 1e-9);
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//
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// // Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10),
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// // Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
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// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
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// Point3(0.1, 0.1, 0.1)); // smaller noise
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// Values values;
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// values.insert(x1, level_pose);
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// values.insert(x2, pose_right);
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// // initialize third pose with some noise, we expect it to move back to
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// // original pose_above
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// values.insert(x3, pose_above * noise_pose);
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// EXPECT( // check that the pose is actually noisy
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// assert_equal(Pose3(Rot3(0, -0.0314107591, 0.99950656, -0.99950656,
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// -0.0313952598, -0.000986635786, 0.0314107591,
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// -0.999013364, -0.0313952598),
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// Point3(0.1, -0.1, 1.9)),
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// values.at<Pose3>(x3)));
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//
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// Values result;
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// LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
|
||||
// result = optimizer.optimize();
|
||||
// EXPECT(assert_equal(pose_above, result.at<Pose3>(x3), 1e-5));
|
||||
//}
|
||||
//
|
||||
//#ifndef DISABLE_TIMING
|
||||
//#include <gtsam/base/timing.h>
|
||||
//// -Total: 0 CPU (0 times, 0 wall, 0.04 children, min: 0 max: 0)
|
||||
////| -SF RS LINEARIZE: 0.02 CPU (1000 times, 0.017244 wall, 0.02 children, min:
|
||||
//// 0 max: 0) | -RS LINEARIZE: 0.02 CPU (1000 times, 0.009035 wall, 0.02
|
||||
//// children, min: 0 max: 0)
|
||||
///* *************************************************************************/
|
||||
//TEST(SmartProjectionPoseFactorRollingShutter, timing) {
|
||||
// using namespace vanillaPose;
|
||||
//
|
||||
// // Default cameras for simple derivatives
|
||||
// static Cal3_S2::shared_ptr sharedKSimple(new Cal3_S2(100, 100, 0, 0, 0));
|
||||
//
|
||||
// Rot3 R = Rot3::identity();
|
||||
// Pose3 pose1 = Pose3(R, Point3(0, 0, 0));
|
||||
// Pose3 pose2 = Pose3(R, Point3(1, 0, 0));
|
||||
// Camera cam1(pose1, sharedKSimple), cam2(pose2, sharedKSimple);
|
||||
// Pose3 body_P_sensorId = Pose3::identity();
|
||||
//
|
||||
// // one landmarks 1m in front of camera
|
||||
// Point3 landmark1(0, 0, 10);
|
||||
//
|
||||
// Point2Vector measurements_lmk1;
|
||||
//
|
||||
// // Project 2 landmarks into 2 cameras
|
||||
// measurements_lmk1.push_back(cam1.project(landmark1));
|
||||
// measurements_lmk1.push_back(cam2.project(landmark1));
|
||||
//
|
||||
// size_t nrTests = 1000;
|
||||
//
|
||||
// for (size_t i = 0; i < nrTests; i++) {
|
||||
// SmartFactorRS::shared_ptr smartFactorRS(new SmartFactorRS(model));
|
||||
// double interp_factor = 0; // equivalent to measurement taken at pose 1
|
||||
// smartFactorRS->add(measurements_lmk1[0], x1, x2, interp_factor,
|
||||
// sharedKSimple, body_P_sensorId);
|
||||
// interp_factor = 1; // equivalent to measurement taken at pose 2
|
||||
// smartFactorRS->add(measurements_lmk1[1], x1, x2, interp_factor,
|
||||
// sharedKSimple, body_P_sensorId);
|
||||
//
|
||||
// Values values;
|
||||
// values.insert(x1, pose1);
|
||||
// values.insert(x2, pose2);
|
||||
// gttic_(SF_RS_LINEARIZE);
|
||||
// smartFactorRS->linearize(values);
|
||||
// gttoc_(SF_RS_LINEARIZE);
|
||||
// }
|
||||
//
|
||||
// for (size_t i = 0; i < nrTests; i++) {
|
||||
// SmartFactor::shared_ptr smartFactor(new SmartFactor(model, sharedKSimple));
|
||||
// smartFactor->add(measurements_lmk1[0], x1);
|
||||
// smartFactor->add(measurements_lmk1[1], x2);
|
||||
//
|
||||
// Values values;
|
||||
// values.insert(x1, pose1);
|
||||
// values.insert(x2, pose2);
|
||||
// gttic_(RS_LINEARIZE);
|
||||
// smartFactor->linearize(values);
|
||||
// gttoc_(RS_LINEARIZE);
|
||||
// }
|
||||
// tictoc_print_();
|
||||
//}
|
||||
//#endif
|
||||
/* *************************************************************************/
|
||||
TEST(SmartProjectionPoseFactorRollingShutter,
|
||||
hessianComparedToProjFactorsRollingShutter) {
|
||||
using namespace vanillaPoseRS;
|
||||
Point2Vector measurements_lmk1;
|
||||
|
||||
// Project three landmarks into three cameras
|
||||
projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_lmk1);
|
||||
|
||||
// create inputs
|
||||
std::vector<std::pair<Key, Key>> key_pairs;
|
||||
key_pairs.push_back(std::make_pair(x1, x2));
|
||||
key_pairs.push_back(std::make_pair(x2, x3));
|
||||
key_pairs.push_back(std::make_pair(x3, x1));
|
||||
|
||||
std::vector<double> interp_factors;
|
||||
interp_factors.push_back(interp_factor1);
|
||||
interp_factors.push_back(interp_factor2);
|
||||
interp_factors.push_back(interp_factor3);
|
||||
|
||||
SmartFactorRS::shared_ptr smartFactor1(new SmartFactorRS(model, Camera(Pose3::identity(),sharedK)));
|
||||
smartFactor1->add(measurements_lmk1, key_pairs, interp_factors);
|
||||
|
||||
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, level_pose);
|
||||
values.insert(x2, pose_right);
|
||||
// initialize third pose with some noise to get a nontrivial linearization
|
||||
// point
|
||||
values.insert(x3, pose_above * noise_pose);
|
||||
EXPECT( // check that the pose is actually noisy
|
||||
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)));
|
||||
|
||||
// linearization point for the poses
|
||||
Pose3 pose1 = level_pose;
|
||||
Pose3 pose2 = pose_right;
|
||||
Pose3 pose3 = pose_above * noise_pose;
|
||||
|
||||
// ==== check Hessian of smartFactor1 =====
|
||||
// -- compute actual Hessian
|
||||
boost::shared_ptr<GaussianFactor> linearfactor1 =
|
||||
smartFactor1->linearize(values);
|
||||
Matrix actualHessian = linearfactor1->information();
|
||||
|
||||
// -- compute expected Hessian from manual Schur complement from Jacobians
|
||||
// linearization point for the 3D point
|
||||
smartFactor1->triangulateSafe(smartFactor1->cameras(values));
|
||||
TriangulationResult point = smartFactor1->point();
|
||||
EXPECT(point.valid()); // check triangulated point is valid
|
||||
|
||||
// Use the factor to calculate the Jacobians
|
||||
Matrix F = Matrix::Zero(2 * 3, 6 * 3);
|
||||
Matrix E = Matrix::Zero(2 * 3, 3);
|
||||
Vector b = Vector::Zero(6);
|
||||
|
||||
// create projection factors rolling shutter
|
||||
ProjectionFactorRollingShutter factor11(measurements_lmk1[0], interp_factor1,
|
||||
model, x1, x2, l0, sharedK);
|
||||
Matrix H1Actual, H2Actual, H3Actual;
|
||||
// note: b is minus the reprojection error, cf the smart factor jacobian
|
||||
// computation
|
||||
b.segment<2>(0) = -factor11.evaluateError(pose1, pose2, *point, H1Actual,
|
||||
H2Actual, H3Actual);
|
||||
F.block<2, 6>(0, 0) = H1Actual;
|
||||
F.block<2, 6>(0, 6) = H2Actual;
|
||||
E.block<2, 3>(0, 0) = H3Actual;
|
||||
|
||||
ProjectionFactorRollingShutter factor12(measurements_lmk1[1], interp_factor2,
|
||||
model, x2, x3, l0, sharedK);
|
||||
b.segment<2>(2) = -factor12.evaluateError(pose2, pose3, *point, H1Actual,
|
||||
H2Actual, H3Actual);
|
||||
F.block<2, 6>(2, 6) = H1Actual;
|
||||
F.block<2, 6>(2, 12) = H2Actual;
|
||||
E.block<2, 3>(2, 0) = H3Actual;
|
||||
|
||||
ProjectionFactorRollingShutter factor13(measurements_lmk1[2], interp_factor3,
|
||||
model, x3, x1, l0, sharedK);
|
||||
b.segment<2>(4) = -factor13.evaluateError(pose3, pose1, *point, H1Actual,
|
||||
H2Actual, H3Actual);
|
||||
F.block<2, 6>(4, 12) = H1Actual;
|
||||
F.block<2, 6>(4, 0) = H2Actual;
|
||||
E.block<2, 3>(4, 0) = H3Actual;
|
||||
|
||||
// whiten
|
||||
F = (1 / sigma) * F;
|
||||
E = (1 / sigma) * E;
|
||||
b = (1 / sigma) * b;
|
||||
//* G = F' * F - F' * E * P * E' * F
|
||||
Matrix P = (E.transpose() * E).inverse();
|
||||
Matrix expectedHessian =
|
||||
F.transpose() * F - (F.transpose() * E * P * E.transpose() * F);
|
||||
EXPECT(assert_equal(expectedHessian, actualHessian, 1e-6));
|
||||
|
||||
// ==== check Information vector of smartFactor1 =====
|
||||
GaussianFactorGraph gfg;
|
||||
gfg.add(linearfactor1);
|
||||
Matrix actualHessian_v2 = gfg.hessian().first;
|
||||
EXPECT(assert_equal(actualHessian_v2, actualHessian,
|
||||
1e-6)); // sanity check on hessian
|
||||
|
||||
// -- compute actual information vector
|
||||
Vector actualInfoVector = gfg.hessian().second;
|
||||
|
||||
// -- compute expected information vector from manual Schur complement from
|
||||
// Jacobians
|
||||
//* g = F' * (b - E * P * E' * b)
|
||||
Vector expectedInfoVector = F.transpose() * (b - E * P * E.transpose() * b);
|
||||
EXPECT(assert_equal(expectedInfoVector, actualInfoVector, 1e-6));
|
||||
|
||||
// ==== check error of smartFactor1 (again) =====
|
||||
NonlinearFactorGraph nfg_projFactorsRS;
|
||||
nfg_projFactorsRS.add(factor11);
|
||||
nfg_projFactorsRS.add(factor12);
|
||||
nfg_projFactorsRS.add(factor13);
|
||||
values.insert(l0, *point);
|
||||
|
||||
double actualError = smartFactor1->error(values);
|
||||
double expectedError = nfg_projFactorsRS.error(values);
|
||||
EXPECT_DOUBLES_EQUAL(expectedError, actualError, 1e-7);
|
||||
}
|
||||
|
||||
/* *************************************************************************/
|
||||
TEST(SmartProjectionPoseFactorRollingShutter,
|
||||
hessianComparedToProjFactorsRollingShutter_measurementsFromSamePose) {
|
||||
// in this test we make sure the fact works even if we have multiple pixel
|
||||
// measurements of the same landmark at a single pose, a setup that occurs in
|
||||
// multi-camera systems
|
||||
|
||||
using namespace vanillaPoseRS;
|
||||
Point2Vector measurements_lmk1;
|
||||
|
||||
// Project three landmarks into three cameras
|
||||
projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_lmk1);
|
||||
|
||||
// create redundant measurements:
|
||||
Camera::MeasurementVector measurements_lmk1_redundant = measurements_lmk1;
|
||||
measurements_lmk1_redundant.push_back(
|
||||
measurements_lmk1.at(0)); // we readd the first measurement
|
||||
|
||||
// create inputs
|
||||
std::vector<std::pair<Key, Key>> key_pairs;
|
||||
key_pairs.push_back(std::make_pair(x1, x2));
|
||||
key_pairs.push_back(std::make_pair(x2, x3));
|
||||
key_pairs.push_back(std::make_pair(x3, x1));
|
||||
key_pairs.push_back(std::make_pair(x1, x2));
|
||||
|
||||
std::vector<double> interp_factors;
|
||||
interp_factors.push_back(interp_factor1);
|
||||
interp_factors.push_back(interp_factor2);
|
||||
interp_factors.push_back(interp_factor3);
|
||||
interp_factors.push_back(interp_factor1);
|
||||
|
||||
SmartFactorRS::shared_ptr smartFactor1(new SmartFactorRS(model, Camera(Pose3::identity(),sharedK)));
|
||||
smartFactor1->add(measurements_lmk1_redundant, key_pairs, interp_factors);
|
||||
|
||||
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, level_pose);
|
||||
values.insert(x2, pose_right);
|
||||
// initialize third pose with some noise to get a nontrivial linearization
|
||||
// point
|
||||
values.insert(x3, pose_above * noise_pose);
|
||||
EXPECT( // check that the pose is actually noisy
|
||||
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)));
|
||||
|
||||
// linearization point for the poses
|
||||
Pose3 pose1 = level_pose;
|
||||
Pose3 pose2 = pose_right;
|
||||
Pose3 pose3 = pose_above * noise_pose;
|
||||
|
||||
// ==== check Hessian of smartFactor1 =====
|
||||
// -- compute actual Hessian
|
||||
boost::shared_ptr<GaussianFactor> linearfactor1 =
|
||||
smartFactor1->linearize(values);
|
||||
Matrix actualHessian = linearfactor1->information();
|
||||
|
||||
// -- compute expected Hessian from manual Schur complement from Jacobians
|
||||
// linearization point for the 3D point
|
||||
smartFactor1->triangulateSafe(smartFactor1->cameras(values));
|
||||
TriangulationResult point = smartFactor1->point();
|
||||
EXPECT(point.valid()); // check triangulated point is valid
|
||||
|
||||
// Use standard ProjectionFactorRollingShutter factor to calculate the
|
||||
// Jacobians
|
||||
Matrix F = Matrix::Zero(2 * 4, 6 * 3);
|
||||
Matrix E = Matrix::Zero(2 * 4, 3);
|
||||
Vector b = Vector::Zero(8);
|
||||
|
||||
// create projection factors rolling shutter
|
||||
ProjectionFactorRollingShutter factor11(measurements_lmk1_redundant[0],
|
||||
interp_factor1, model, x1, x2, l0,
|
||||
sharedK);
|
||||
Matrix H1Actual, H2Actual, H3Actual;
|
||||
// note: b is minus the reprojection error, cf the smart factor jacobian
|
||||
// computation
|
||||
b.segment<2>(0) = -factor11.evaluateError(pose1, pose2, *point, H1Actual,
|
||||
H2Actual, H3Actual);
|
||||
F.block<2, 6>(0, 0) = H1Actual;
|
||||
F.block<2, 6>(0, 6) = H2Actual;
|
||||
E.block<2, 3>(0, 0) = H3Actual;
|
||||
|
||||
ProjectionFactorRollingShutter factor12(measurements_lmk1_redundant[1],
|
||||
interp_factor2, model, x2, x3, l0,
|
||||
sharedK);
|
||||
b.segment<2>(2) = -factor12.evaluateError(pose2, pose3, *point, H1Actual,
|
||||
H2Actual, H3Actual);
|
||||
F.block<2, 6>(2, 6) = H1Actual;
|
||||
F.block<2, 6>(2, 12) = H2Actual;
|
||||
E.block<2, 3>(2, 0) = H3Actual;
|
||||
|
||||
ProjectionFactorRollingShutter factor13(measurements_lmk1_redundant[2],
|
||||
interp_factor3, model, x3, x1, l0,
|
||||
sharedK);
|
||||
b.segment<2>(4) = -factor13.evaluateError(pose3, pose1, *point, H1Actual,
|
||||
H2Actual, H3Actual);
|
||||
F.block<2, 6>(4, 12) = H1Actual;
|
||||
F.block<2, 6>(4, 0) = H2Actual;
|
||||
E.block<2, 3>(4, 0) = H3Actual;
|
||||
|
||||
ProjectionFactorRollingShutter factor14(measurements_lmk1_redundant[3],
|
||||
interp_factor1, model, x1, x2, l0,
|
||||
sharedK);
|
||||
b.segment<2>(6) = -factor11.evaluateError(pose1, pose2, *point, H1Actual,
|
||||
H2Actual, H3Actual);
|
||||
F.block<2, 6>(6, 0) = H1Actual;
|
||||
F.block<2, 6>(6, 6) = H2Actual;
|
||||
E.block<2, 3>(6, 0) = H3Actual;
|
||||
|
||||
// whiten
|
||||
F = (1 / sigma) * F;
|
||||
E = (1 / sigma) * E;
|
||||
b = (1 / sigma) * b;
|
||||
//* G = F' * F - F' * E * P * E' * F
|
||||
Matrix P = (E.transpose() * E).inverse();
|
||||
Matrix expectedHessian =
|
||||
F.transpose() * F - (F.transpose() * E * P * E.transpose() * F);
|
||||
EXPECT(assert_equal(expectedHessian, actualHessian, 1e-6));
|
||||
|
||||
// ==== check Information vector of smartFactor1 =====
|
||||
GaussianFactorGraph gfg;
|
||||
gfg.add(linearfactor1);
|
||||
Matrix actualHessian_v2 = gfg.hessian().first;
|
||||
EXPECT(assert_equal(actualHessian_v2, actualHessian,
|
||||
1e-6)); // sanity check on hessian
|
||||
|
||||
// -- compute actual information vector
|
||||
Vector actualInfoVector = gfg.hessian().second;
|
||||
|
||||
// -- compute expected information vector from manual Schur complement from
|
||||
// Jacobians
|
||||
//* g = F' * (b - E * P * E' * b)
|
||||
Vector expectedInfoVector = F.transpose() * (b - E * P * E.transpose() * b);
|
||||
EXPECT(assert_equal(expectedInfoVector, actualInfoVector, 1e-6));
|
||||
|
||||
// ==== check error of smartFactor1 (again) =====
|
||||
NonlinearFactorGraph nfg_projFactorsRS;
|
||||
nfg_projFactorsRS.add(factor11);
|
||||
nfg_projFactorsRS.add(factor12);
|
||||
nfg_projFactorsRS.add(factor13);
|
||||
nfg_projFactorsRS.add(factor14);
|
||||
values.insert(l0, *point);
|
||||
|
||||
double actualError = smartFactor1->error(values);
|
||||
double expectedError = nfg_projFactorsRS.error(values);
|
||||
EXPECT_DOUBLES_EQUAL(expectedError, actualError, 1e-7);
|
||||
}
|
||||
|
||||
/* *************************************************************************/
|
||||
TEST(SmartProjectionPoseFactorRollingShutter,
|
||||
optimization_3poses_measurementsFromSamePose) {
|
||||
using namespace vanillaPoseRS;
|
||||
Point2Vector measurements_lmk1, measurements_lmk2, measurements_lmk3;
|
||||
|
||||
// Project three landmarks into three cameras
|
||||
projectToMultipleCameras(cam1, cam2, cam3, landmark1, measurements_lmk1);
|
||||
projectToMultipleCameras(cam1, cam2, cam3, landmark2, measurements_lmk2);
|
||||
projectToMultipleCameras(cam1, cam2, cam3, landmark3, measurements_lmk3);
|
||||
|
||||
// create inputs
|
||||
std::vector<std::pair<Key, Key>> key_pairs;
|
||||
key_pairs.push_back(std::make_pair(x1, x2));
|
||||
key_pairs.push_back(std::make_pair(x2, x3));
|
||||
key_pairs.push_back(std::make_pair(x3, x1));
|
||||
|
||||
std::vector<double> interp_factors;
|
||||
interp_factors.push_back(interp_factor1);
|
||||
interp_factors.push_back(interp_factor2);
|
||||
interp_factors.push_back(interp_factor3);
|
||||
|
||||
// For first factor, we create redundant measurement (taken by the same keys
|
||||
// as factor 1, to make sure the redundancy in the keys does not create
|
||||
// problems)
|
||||
Camera::MeasurementVector& measurements_lmk1_redundant = measurements_lmk1;
|
||||
measurements_lmk1_redundant.push_back(
|
||||
measurements_lmk1.at(0)); // we readd the first measurement
|
||||
std::vector<std::pair<Key, Key>> key_pairs_redundant = key_pairs;
|
||||
key_pairs_redundant.push_back(
|
||||
key_pairs.at(0)); // we readd the first pair of keys
|
||||
std::vector<double> interp_factors_redundant = interp_factors;
|
||||
interp_factors_redundant.push_back(
|
||||
interp_factors.at(0)); // we readd the first interp factor
|
||||
|
||||
SmartFactorRS::shared_ptr smartFactor1(new SmartFactorRS(model, Camera(Pose3::identity(),sharedK)));
|
||||
smartFactor1->add(measurements_lmk1_redundant, key_pairs_redundant,
|
||||
interp_factors_redundant);
|
||||
|
||||
SmartFactorRS::shared_ptr smartFactor2(new SmartFactorRS(model, Camera(Pose3::identity(),sharedK)));
|
||||
smartFactor2->add(measurements_lmk2, key_pairs, interp_factors);
|
||||
|
||||
SmartFactorRS::shared_ptr smartFactor3(new SmartFactorRS(model, Camera(Pose3::identity(),sharedK)));
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smartFactor3->add(measurements_lmk3, key_pairs, interp_factors);
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||||
|
||||
const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
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||||
|
||||
NonlinearFactorGraph graph;
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graph.push_back(smartFactor1);
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||||
graph.push_back(smartFactor2);
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||||
graph.push_back(smartFactor3);
|
||||
graph.addPrior(x1, level_pose, noisePrior);
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||||
graph.addPrior(x2, pose_right, noisePrior);
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||||
|
||||
Values groundTruth;
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||||
groundTruth.insert(x1, level_pose);
|
||||
groundTruth.insert(x2, pose_right);
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||||
groundTruth.insert(x3, pose_above);
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||||
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, level_pose);
|
||||
values.insert(x2, pose_right);
|
||||
// initialize third pose with some noise, we expect it to move back to
|
||||
// original pose_above
|
||||
values.insert(x3, pose_above * noise_pose);
|
||||
EXPECT( // check that the pose is actually noisy
|
||||
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-5));
|
||||
}
|
||||
|
||||
#ifndef DISABLE_TIMING
|
||||
#include <gtsam/base/timing.h>
|
||||
//-Total: 0 CPU (0 times, 0 wall, 0.21 children, min: 0 max: 0)
|
||||
//| -SF RS LINEARIZE: 0.15 CPU (10000 times, 0.125521 wall, 0.15 children, min: 0 max: 0)
|
||||
//| -RS LINEARIZE: 0.06 CPU (10000 times, 0.06311 wall, 0.06 children, min: 0 max: 0)
|
||||
/* *************************************************************************/
|
||||
TEST(SmartProjectionPoseFactorRollingShutter, timing) {
|
||||
using namespace vanillaPose;
|
||||
|
||||
// Default cameras for simple derivatives
|
||||
static Cal3_S2::shared_ptr sharedKSimple(new Cal3_S2(100, 100, 0, 0, 0));
|
||||
|
||||
Rot3 R = Rot3::identity();
|
||||
Pose3 pose1 = Pose3(R, Point3(0, 0, 0));
|
||||
Pose3 pose2 = Pose3(R, Point3(1, 0, 0));
|
||||
Camera cam1(pose1, sharedKSimple), cam2(pose2, sharedKSimple);
|
||||
Pose3 body_P_sensorId = Pose3::identity();
|
||||
|
||||
// one landmarks 1m in front of camera
|
||||
Point3 landmark1(0, 0, 10);
|
||||
|
||||
Point2Vector measurements_lmk1;
|
||||
|
||||
// Project 2 landmarks into 2 cameras
|
||||
measurements_lmk1.push_back(cam1.project(landmark1));
|
||||
measurements_lmk1.push_back(cam2.project(landmark1));
|
||||
|
||||
size_t nrTests = 10000;
|
||||
|
||||
for (size_t i = 0; i < nrTests; i++) {
|
||||
SmartFactorRS::shared_ptr smartFactorRS(new SmartFactorRS(model, Camera(body_P_sensorId,sharedKSimple)));
|
||||
double interp_factor = 0; // equivalent to measurement taken at pose 1
|
||||
smartFactorRS->add(measurements_lmk1[0], x1, x2, interp_factor);
|
||||
interp_factor = 1; // equivalent to measurement taken at pose 2
|
||||
smartFactorRS->add(measurements_lmk1[1], x1, x2, interp_factor);
|
||||
|
||||
Values values;
|
||||
values.insert(x1, pose1);
|
||||
values.insert(x2, pose2);
|
||||
gttic_(SF_RS_LINEARIZE);
|
||||
smartFactorRS->linearize(values);
|
||||
gttoc_(SF_RS_LINEARIZE);
|
||||
}
|
||||
|
||||
for (size_t i = 0; i < nrTests; i++) {
|
||||
SmartFactor::shared_ptr smartFactor(new SmartFactor(model, sharedKSimple));
|
||||
smartFactor->add(measurements_lmk1[0], x1);
|
||||
smartFactor->add(measurements_lmk1[1], x2);
|
||||
|
||||
Values values;
|
||||
values.insert(x1, pose1);
|
||||
values.insert(x2, pose2);
|
||||
gttic_(RS_LINEARIZE);
|
||||
smartFactor->linearize(values);
|
||||
gttoc_(RS_LINEARIZE);
|
||||
}
|
||||
tictoc_print_();
|
||||
}
|
||||
#endif
|
||||
|
||||
/* ************************************************************************* */
|
||||
int main() {
|
||||
|
|
|
|||
Loading…
Reference in New Issue