Merge branch 'feature/rollingShutterSmartFactors' into feature/cameraTemplateForAllSmartFactors
lcarlone
commit
8af633a991
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@ -37,9 +37,11 @@ namespace gtsam {
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* from which the pixel observation pose can be interpolated.
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* @addtogroup SLAM
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*/
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template<class CALIBRATION>
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class SmartProjectionPoseFactorRollingShutter : public SmartProjectionFactor<
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PinholePose<CALIBRATION> > {
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template<class CAMERA>
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class SmartProjectionPoseFactorRollingShutter : public SmartProjectionFactor<CAMERA> {
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public:
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typedef typename CAMERA::CalibrationType CALIBRATION;
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protected:
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/// shared pointer to calibration object (one for each observation)
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@ -213,8 +215,8 @@ PinholePose<CALIBRATION> > {
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/// equals
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bool equals(const NonlinearFactor& p, double tol = 1e-9) const override {
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const SmartProjectionPoseFactorRollingShutter<CALIBRATION>* e =
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dynamic_cast<const SmartProjectionPoseFactorRollingShutter<CALIBRATION>*>(&p);
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const SmartProjectionPoseFactorRollingShutter<CAMERA>* e =
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dynamic_cast<const SmartProjectionPoseFactorRollingShutter<CAMERA>*>(&p);
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double keyPairsEqual = true;
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if(this->world_P_body_key_pairs_.size() == e->world_P_body_key_pairs().size()){
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@ -430,9 +432,9 @@ PinholePose<CALIBRATION> > {
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// end of class declaration
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/// traits
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template<class CALIBRATION>
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struct traits<SmartProjectionPoseFactorRollingShutter<CALIBRATION> > :
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public Testable<SmartProjectionPoseFactorRollingShutter<CALIBRATION> > {
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template<class CAMERA>
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struct traits<SmartProjectionPoseFactorRollingShutter<CAMERA> > :
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public Testable<SmartProjectionPoseFactorRollingShutter<CAMERA> > {
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};
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} // namespace gtsam
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@ -73,7 +73,7 @@ Camera cam3(interp_pose3, sharedK);
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}
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LevenbergMarquardtParams lmParams;
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typedef SmartProjectionPoseFactorRollingShutter<Cal3_S2> SmartFactorRS;
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typedef SmartProjectionPoseFactorRollingShutter< PinholePose<Cal3_S2> > SmartFactorRS;
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/* ************************************************************************* */
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TEST( SmartProjectionPoseFactorRollingShutter, Constructor) {
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@ -770,6 +770,213 @@ TEST( SmartProjectionPoseFactorRollingShutter, hessianComparedToProjFactorsRolli
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EXPECT_DOUBLES_EQUAL(expectedError, actualError, 1e-7);
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}
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/* *************************************************************************/
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TEST( SmartProjectionPoseFactorRollingShutter, hessianComparedToProjFactorsRollingShutter_measurementsFromSamePose) {
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// in this test we make sure the fact works even if we have multiple pixel measurements of the same landmark
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// at a single pose, a setup that occurs in multi-camera systems
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using namespace vanillaPoseRS;
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Point2Vector measurements_lmk1;
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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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// create redundant measurements:
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Camera::MeasurementVector measurements_lmk1_redundant = measurements_lmk1;
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measurements_lmk1_redundant.push_back(measurements_lmk1.at(0)); // we readd the first measurement
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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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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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SmartFactorRS::shared_ptr smartFactor1(new SmartFactorRS(model));
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smartFactor1->add(measurements_lmk1_redundant, key_pairs, interp_factors, sharedK);
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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 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, -0.0313952598, -0.000986635786, 0.0314107591, -0.999013364, -0.0313952598), Point3(0.1, -0.1, 1.9)), values.at<Pose3>(x3)));
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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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// ==== check Hessian of smartFactor1 =====
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// -- compute actual Hessian
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boost::shared_ptr<GaussianFactor> linearfactor1 = smartFactor1->linearize(
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values);
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Matrix actualHessian = linearfactor1->information();
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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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// Use standard ProjectionFactorRollingShutter factor to calculate the 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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// create projection factors rolling shutter
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ProjectionFactorRollingShutter factor11(measurements_lmk1_redundant[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 computation
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b.segment<2>(0) = -factor11.evaluateError(pose1, pose2, *point, H1Actual, 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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ProjectionFactorRollingShutter factor12(measurements_lmk1_redundant[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, 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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ProjectionFactorRollingShutter factor13(measurements_lmk1_redundant[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, 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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ProjectionFactorRollingShutter factor14(measurements_lmk1_redundant[3], interp_factor1,
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model, x1, x2, l0, sharedK);
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b.segment<2>(6) = -factor11.evaluateError(pose1, pose2, *point, H1Actual, 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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// 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 = F.transpose() * F
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- (F.transpose() * E * P * E.transpose() * F);
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EXPECT(assert_equal(expectedHessian, actualHessian, 1e-6));
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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, 1e-6)); // sanity check on hessian
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// -- compute actual information vector
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Vector actualInfoVector = gfg.hessian().second;
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// -- compute expected information vector from manual Schur complement from 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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// ==== 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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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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TEST( SmartProjectionPoseFactorRollingShutter, 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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// 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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// 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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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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// For first factor, we create redundant measurement (taken by the same keys as factor 1, to
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// make sure the redundancy in the keys does not create problems)
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Camera::MeasurementVector& measurements_lmk1_redundant = measurements_lmk1;
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measurements_lmk1_redundant.push_back(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(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(interp_factors.at(0));// we readd the first interp factor
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SmartFactorRS::shared_ptr smartFactor1(new SmartFactorRS(model));
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smartFactor1->add(measurements_lmk1_redundant, key_pairs_redundant, interp_factors_redundant, sharedK);
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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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SmartFactorRS::shared_ptr smartFactor3(new SmartFactorRS(model));
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smartFactor3->add(measurements_lmk3, key_pairs, interp_factors, sharedK);
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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);
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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);
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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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// 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
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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 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(
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Pose3(
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Rot3(0, -0.0314107591, 0.99950656, -0.99950656, -0.0313952598,
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-0.000986635786, 0.0314107591, -0.999013364, -0.0313952598),
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Point3(0.1, -0.1, 1.9)), values.at<Pose3>(x3)));
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Values result;
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LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
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result = optimizer.optimize();
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EXPECT(assert_equal(pose_above, result.at<Pose3>(x3), 1e-5));
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}
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#ifndef DISABLE_TIMING
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#include <gtsam/base/timing.h>
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// -Total: 0 CPU (0 times, 0 wall, 0.04 children, min: 0 max: 0)
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