diff --git a/gtsam/slam/SmartProjectionFactorP.h b/gtsam/slam/SmartProjectionFactorP.h new file mode 100644 index 000000000..030ec65f2 --- /dev/null +++ b/gtsam/slam/SmartProjectionFactorP.h @@ -0,0 +1,216 @@ +/* ---------------------------------------------------------------------------- + + * 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 SmartProjectionFactorP.h + * @brief Smart factor on poses, assuming camera calibration is fixed. + * Same as SmartProjectionPoseFactor, except: + * - it is templated on CAMERA (i.e., it allows cameras beyond pinhole) + * - it admits a different calibration for each measurement (i.e., it can model a multi-camera system) + * @author Luca Carlone + * @author Chris Beall + * @author Zsolt Kira + */ + +#pragma once + +#include + +namespace gtsam { +/** + * + * @addtogroup SLAM + * + * If you are using the factor, please cite: + * L. Carlone, Z. Kira, C. Beall, V. Indelman, F. Dellaert, Eliminating conditionally + * independent sets in factor graphs: a unifying perspective based on smart factors, + * Int. Conf. on Robotics and Automation (ICRA), 2014. + * + */ + +/** + * This factor assumes that camera calibration is fixed (but each camera + * measurement can have a different extrinsic and intrinsic calibration). + * The factor only constrains poses (variable dimension is 6). + * This factor requires that values contains the involved poses (Pose3). + * If all measurements share the same calibration (i.e., are from the same camera), use SmartProjectionPoseFactor instead! + * If the calibration should be optimized, as well, use SmartProjectionFactor instead! + * @addtogroup SLAM + */ +template +class SmartProjectionFactorP: public SmartProjectionFactor { + +private: + typedef SmartProjectionFactor Base; + typedef SmartProjectionFactorP This; + typedef CAMERA Camera; + typedef typename CAMERA::CalibrationType CALIBRATION; + +protected: + + /// shared pointer to calibration object (one for each observation) + std::vector > K_all_; + + /// Pose of the camera in the body frame (one for each observation) + std::vector body_P_sensors_; + +public: + + /// shorthand for a smart pointer to a factor + typedef boost::shared_ptr shared_ptr; + + /// Default constructor, only for serialization + SmartProjectionFactorP() {} + + /** + * Constructor + * @param sharedNoiseModel isotropic noise model for the 2D feature measurements + * @param params parameters for the smart projection factors + */ + SmartProjectionFactorP( + const SharedNoiseModel& sharedNoiseModel, + const SmartProjectionParams& params = SmartProjectionParams()) + : Base(sharedNoiseModel, params) { + } + + /** Virtual destructor */ + ~SmartProjectionFactorP() override { + } + + /** + * add a new measurement, corresponding to an observation from pose "poseKey" whose camera + * has intrinsic calibration K and extrinsic calibration body_P_sensor. + * @param measured 2-dimensional location of the projection of a + * single landmark in a single view (the measurement) + * @param poseKey key corresponding to the body pose of the camera taking the measurement + * @param K (fixed) camera intrinsic calibration + * @param body_P_sensor (fixed) camera extrinsic calibration + */ + void add(const Point2& measured, const Key& poseKey, + const boost::shared_ptr& K, const Pose3 body_P_sensor = Pose3::identity()) { + // store measurement and key + this->measured_.push_back(measured); + this->keys_.push_back(key); + // store fixed intrinsic calibration + K_all_.push_back(K); + // store fixed extrinsics of the camera + body_P_sensors_.push_back(body_P_sensor); + } + + /** + * Variant of the previous "add" function in which we include multiple measurements + * @param measurements vector of the 2m dimensional location of the projection + * of a single landmark in the m views (the measurements) + * @param poseKeys keys corresponding to the body poses of the cameras taking the measurements + * @param Ks vector of (fixed) intrinsic calibration objects + * @param body_P_sensors vector of (fixed) extrinsic calibration objects + */ + void add(const Point2Vector& measurements, + const std::vector& poseKeys, + const std::vector>& Ks, + const std::vector body_P_sensors) { + assert(poseKeys.size() == measurements.size()); + assert(poseKeys.size() == Ks.size()); + assert(poseKeys.size() == body_P_sensors.size()); + for (size_t i = 0; i < measurements.size(); i++) { + add(measurements[i], poseKeys[i], Ks[i], body_P_sensors[i]); + } + } + + /// return the calibration object + inline std::vector> calibration() const { + return K_all_; + } + + /// return the extrinsic camera calibration body_P_sensors + const std::vector body_P_sensors() const { + return body_P_sensors_; + } + + /** + * print + * @param s optional string naming the factor + * @param keyFormatter optional formatter useful for printing Symbols + */ + void print(const std::string& s = "", const KeyFormatter& keyFormatter = + DefaultKeyFormatter) const override { + std::cout << s << "SmartProjectionFactorP: \n "; + for (size_t i = 0; i < K_all_.size(); i++) { + std::cout << "-- Measurement nr " << i << std::endl; + body_P_sensors_[i].print("extrinsic calibration:\n"); + K_all_[i]->print("intrinsic calibration = "); + } + Base::print("", keyFormatter); + } + + /// equals + bool equals(const NonlinearFactor& p, double tol = 1e-9) const override { + const This *e = dynamic_cast(&p); + double extrinsicCalibrationEqual = true; + if(this->body_P_sensors_.size() == e->body_P_sensors().size()){ + for(size_t i=0; i< this->body_P_sensors_.size(); i++){ + if (!body_P_sensors_[i].equals(e->body_P_sensors()[i])){ + extrinsicCalibrationEqual = false; break; + } + } + }else{ extrinsicCalibrationEqual = false; } + + return e && Base::equals(p, tol) && K_all_ == e->calibration() + && extrinsicCalibrationEqual; + } + + /** + * error calculates the error of the factor. + */ + double error(const Values& values) const override { + if (this->active(values)) { + return this->totalReprojectionError(cameras(values)); + } else { // else of active flag + return 0.0; + } + } + + /** + * Collect all cameras involved in this factor + * @param values Values structure which must contain camera poses corresponding + * to keys involved in this factor + * @return vector of cameras + */ + typename Base::Cameras cameras(const Values& values) const override { + typename Base::Cameras cameras; + for (const Key& k : this->keys_) { + const Pose3& body_P_cam = body_P_sensors_[i]; + const Pose3 world_P_sensor_k = values.at(k) * body_P_cam; + cameras.emplace_back(world_P_sensor_k, K_all_[i]); + } + return cameras; + } + + private: + + /// Serialization function + friend class boost::serialization::access; + template + void serialize(ARCHIVE & ar, const unsigned int /*version*/) { + ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base); + ar & BOOST_SERIALIZATION_NVP(K_); + } + +}; +// end of class declaration + +/// traits +template +struct traits > : public Testable< + SmartProjectionFactorP > { +}; + +} // \ namespace gtsam diff --git a/gtsam/slam/tests/testSmartProjectionFactorP.cpp b/gtsam/slam/tests/testSmartProjectionFactorP.cpp new file mode 100644 index 000000000..c7f220c10 --- /dev/null +++ b/gtsam/slam/tests/testSmartProjectionFactorP.cpp @@ -0,0 +1,1382 @@ +/* ---------------------------------------------------------------------------- + + * 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 testSmartProjectionPoseFactor.cpp + * @brief Unit tests for ProjectionFactor Class + * @author Chris Beall + * @author Luca Carlone + * @author Zsolt Kira + * @author Frank Dellaert + * @date Sept 2013 + */ + +#include "smartFactorScenarios.h" +#include +#include +#include +#include +#include +#include +#include +#include + +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( SmartProjectionPoseFactor, Constructor) { + using namespace vanillaPose; + SmartFactor::shared_ptr factor1(new SmartFactor(model, sharedK)); +} + +/* ************************************************************************* */ +TEST( SmartProjectionPoseFactor, Constructor2) { + using namespace vanillaPose; + SmartProjectionParams params; + params.setRankTolerance(rankTol); + SmartFactor factor1(model, sharedK, params); +} + +/* ************************************************************************* */ +TEST( SmartProjectionPoseFactor, Constructor3) { + using namespace vanillaPose; + SmartFactor::shared_ptr factor1(new SmartFactor(model, sharedK)); + factor1->add(measurement1, x1); +} + +/* ************************************************************************* */ +TEST( SmartProjectionPoseFactor, Constructor4) { + using namespace vanillaPose; + SmartProjectionParams params; + params.setRankTolerance(rankTol); + SmartFactor factor1(model, sharedK, params); + factor1.add(measurement1, x1); +} + +/* ************************************************************************* */ +TEST( SmartProjectionPoseFactor, params) { + using namespace vanillaPose; + SmartProjectionParams params; + double rt = params.getRetriangulationThreshold(); + EXPECT_DOUBLES_EQUAL(1e-5, rt, 1e-7); + params.setRetriangulationThreshold(1e-3); + rt = params.getRetriangulationThreshold(); + EXPECT_DOUBLES_EQUAL(1e-3, rt, 1e-7); +} + +/* ************************************************************************* */ +TEST( SmartProjectionPoseFactor, Equals ) { + using namespace vanillaPose; + SmartFactor::shared_ptr factor1(new SmartFactor(model, sharedK)); + factor1->add(measurement1, x1); + + SmartFactor::shared_ptr factor2(new SmartFactor(model,sharedK)); + factor2->add(measurement1, x1); + + CHECK(assert_equal(*factor1, *factor2)); +} + +/* *************************************************************************/ +TEST( SmartProjectionPoseFactor, 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); + + SmartFactor factor(model, sharedK); + factor.add(level_uv, x1); + factor.add(level_uv_right, x2); + + 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); + + SmartFactor::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 f = // + std::bind(&SmartFactor::whitenedError, factor, cameras, std::placeholders::_1); + + // Calculate using computeEP + Matrix actualE; + factor.triangulateAndComputeE(actualE, values); + + // get point + boost::optional point = factor.point(); + CHECK(point); + + // calculate numerical derivative with triangulated point + Matrix expectedE = sigma * numericalDerivative11(f, *point); + EXPECT(assert_equal(expectedE, actualE, 1e-7)); + + // Calculate using reprojectionError + SmartFactor::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; + SmartFactor::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( SmartProjectionPoseFactor, 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)); + + SmartFactor::shared_ptr factor(new SmartFactor(model, sharedK)); + factor->add(level_uv, x1); + factor->add(level_uv_right, x2); + + double actualError1 = factor->error(values); + + SmartFactor::shared_ptr factor2(new SmartFactor(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(SmartProjectionPoseFactor, 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); + + SmartFactor smartFactor1(model, sharedK, body_T_sensor, params); + smartFactor1.add(measurements_cam1, views); + + SmartFactor smartFactor2(model, sharedK, body_T_sensor, params); + smartFactor2.add(measurements_cam2, views); + + SmartFactor 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(x3))); +} + +/* *************************************************************************/ +TEST( SmartProjectionPoseFactor, 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); + + SmartFactor::shared_ptr smartFactor1(new SmartFactor(model, sharedK2)); + smartFactor1->add(measurements_cam1, views); + + SmartFactor::shared_ptr smartFactor2(new SmartFactor(model, sharedK2)); + smartFactor2->add(measurements_cam2, views); + + SmartFactor::shared_ptr smartFactor3(new SmartFactor(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(x3))); + + Values result; + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + result = optimizer.optimize(); + EXPECT(assert_equal(pose_above, result.at(x3), 1e-6)); +} + +/* *************************************************************************/ +TEST( SmartProjectionPoseFactor, 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}; + + SmartFactor::shared_ptr smartFactor1 = boost::make_shared(model, sharedK); + smartFactor1->add(measurements_cam1, views); + + SmartFactor::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 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 > 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; + SmartFactor::FBlocks Fs = list_of(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 > 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 expected(keys, Fs, E, whiteP, b); + + boost::shared_ptr > 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 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( SmartProjectionPoseFactor, 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); + + SmartFactor::shared_ptr smartFactor1(new SmartFactor(model, sharedK)); + smartFactor1->add(measurements_cam1, views); + + SmartFactor::shared_ptr smartFactor2(new SmartFactor(model, sharedK)); + smartFactor2->add(measurements_cam2, views); + + SmartFactor::shared_ptr smartFactor3(new SmartFactor(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(x3))); + + Values result; + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + result = optimizer.optimize(); + EXPECT(assert_equal(pose_above, result.at(x3), 1e-7)); +} + +/* *************************************************************************/ +TEST( SmartProjectionPoseFactor, 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); + SmartFactor factor1(model, sharedK, params); + + SmartFactor::shared_ptr smartFactor1( + new SmartFactor(model, sharedK, params)); + smartFactor1->add(measurements_cam1, views); + + SmartFactor::shared_ptr smartFactor2( + new SmartFactor(model, sharedK, params)); + smartFactor2->add(measurements_cam2, views); + + SmartFactor::shared_ptr smartFactor3( + new SmartFactor(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(x3), 1e-6)); +} + +/* *************************************************************************/ +TEST( SmartProjectionPoseFactor, 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); + + SmartFactor::shared_ptr smartFactor1( + new SmartFactor(model, sharedK, params)); + smartFactor1->add(measurements_cam1, views); + + SmartFactor::shared_ptr smartFactor2( + new SmartFactor(model, sharedK, params)); + smartFactor2->add(measurements_cam2, views); + + SmartFactor::shared_ptr smartFactor3( + new SmartFactor(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(x3), result.at(x3))); +} + +/* *************************************************************************/ +TEST( SmartProjectionPoseFactor, 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); + + SmartFactor::shared_ptr smartFactor1( + new SmartFactor(model, sharedK, params)); + smartFactor1->add(measurements_cam1, views); + + SmartFactor::shared_ptr smartFactor2( + new SmartFactor(model, sharedK, params)); + smartFactor2->add(measurements_cam2, views); + + SmartFactor::shared_ptr smartFactor3( + new SmartFactor(model, sharedK, params)); + smartFactor3->add(measurements_cam3, views); + + SmartFactor::shared_ptr smartFactor4( + new SmartFactor(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(x3))); +} + +/* *************************************************************************/ +TEST( SmartProjectionPoseFactor, 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); + + SmartFactor::shared_ptr smartFactor1( + new SmartFactor(model, sharedK, params)); + smartFactor1->add(measurements_cam1, views); + + SmartFactor::shared_ptr smartFactor2( + new SmartFactor(model, sharedK, params)); + smartFactor2->add(measurements_cam2, views); + + SmartFactor::shared_ptr smartFactor3( + new SmartFactor(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(x3), 1e-6)); +} + +/* *************************************************************************/ +TEST( SmartProjectionPoseFactor, 3poses_projection_factor ) { + + using namespace vanillaPose2; + + KeyVector views {x1, x2, x3}; + + typedef GenericProjectionFactor ProjectionFactor; + NonlinearFactorGraph graph; + + // Project three landmarks into three cameras + graph.emplace_shared(cam1.project(landmark1), model, x1, L(1), sharedK2); + graph.emplace_shared(cam2.project(landmark1), model, x2, L(1), sharedK2); + graph.emplace_shared(cam3.project(landmark1), model, x3, L(1), sharedK2); + + graph.emplace_shared(cam1.project(landmark2), model, x1, L(2), sharedK2); + graph.emplace_shared(cam2.project(landmark2), model, x2, L(2), sharedK2); + graph.emplace_shared(cam3.project(landmark2), model, x3, L(2), sharedK2); + + graph.emplace_shared(cam1.project(landmark3), model, x1, L(3), sharedK2); + graph.emplace_shared(cam2.project(landmark3), model, x2, L(3), sharedK2); + graph.emplace_shared(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(x3), 1e-7)); +} + +/* *************************************************************************/ +TEST( SmartProjectionPoseFactor, 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); + + SmartFactor::shared_ptr smartFactor1( + new SmartFactor(model, sharedK, params)); // HESSIAN, by default + smartFactor1->add(measurements_cam1, views); + + SmartFactor::shared_ptr smartFactor2( + new SmartFactor(model, sharedK, params)); // HESSIAN, by default + smartFactor2->add(measurements_cam2, views); + + SmartFactor::shared_ptr smartFactor3( + new SmartFactor(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(x3))); + + boost::shared_ptr factor1 = smartFactor1->linearize(values); + boost::shared_ptr factor2 = smartFactor2->linearize(values); + boost::shared_ptr factor3 = smartFactor3->linearize(values); + + Matrix CumulativeInformation = factor1->information() + factor2->information() + + factor3->information(); + + boost::shared_ptr 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( SmartProjectionPoseFactor, 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); + + SmartFactor::shared_ptr smartFactor1( + new SmartFactor(model, sharedK2, params)); + smartFactor1->add(measurements_cam1, views); + + SmartFactor::shared_ptr smartFactor2( + new SmartFactor(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 >(x2, positionPrior, noisePriorTranslation); + graph.emplace_shared >(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(x3))); +} + +/* *************************************************************************/ +TEST( SmartProjectionPoseFactor, 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); + + SmartFactor::shared_ptr smartFactor1( + new SmartFactor(model, sharedK, params)); + smartFactor1->add(measurements_cam1, views); + + SmartFactor::shared_ptr smartFactor2( + new SmartFactor(model, sharedK, params)); + smartFactor2->add(measurements_cam2, views); + + SmartFactor::shared_ptr smartFactor3( + new SmartFactor(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 >(x2, positionPrior, noisePriorTranslation); + graph.emplace_shared >(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(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(x3).rotation(), + Point3(0,0,1)), result.at(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(x3),1e-3)); +#endif +} + +/* *************************************************************************/ +TEST( SmartProjectionPoseFactor, 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); + + SmartFactor::shared_ptr smartFactor1(new SmartFactor(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 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( SmartProjectionPoseFactor, HessianWithRotation ) { + // cout << " ************************ SmartProjectionPoseFactor: 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); + + SmartFactor::shared_ptr smartFactorInstance(new SmartFactor(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 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 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 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( SmartProjectionPoseFactor, 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); + + SmartFactor::shared_ptr smartFactor(new SmartFactor(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 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 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 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( SmartProjectionPoseFactor, ConstructorWithCal3Bundler) { + using namespace bundlerPose; + SmartProjectionParams params; + params.setDegeneracyMode(gtsam::ZERO_ON_DEGENERACY); + SmartFactor factor(model, sharedBundlerK, params); + factor.add(measurement1, x1); +} + +/* *************************************************************************/ +TEST( SmartProjectionPoseFactor, 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}; + + SmartFactor::shared_ptr smartFactor1(new SmartFactor(model, sharedBundlerK)); + smartFactor1->add(measurements_cam1, views); + + SmartFactor::shared_ptr smartFactor2(new SmartFactor(model, sharedBundlerK)); + smartFactor2->add(measurements_cam2, views); + + SmartFactor::shared_ptr smartFactor3(new SmartFactor(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(x3))); + + Values result; + LevenbergMarquardtOptimizer optimizer(graph, values, lmParams); + result = optimizer.optimize(); + EXPECT(assert_equal(cam3.pose(), result.at(x3), 1e-6)); +} + +/* *************************************************************************/ +TEST( SmartProjectionPoseFactor, 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); + + SmartFactor::shared_ptr smartFactor1( + new SmartFactor(model, sharedBundlerK, params)); + smartFactor1->add(measurements_cam1, views); + + SmartFactor::shared_ptr smartFactor2( + new SmartFactor(model, sharedBundlerK, params)); + smartFactor2->add(measurements_cam2, views); + + SmartFactor::shared_ptr smartFactor3( + new SmartFactor(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 >(x2, positionPrior, noisePriorTranslation); + graph.emplace_shared >(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(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(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(SmartProjectionPoseFactor, serialize) { + using namespace vanillaPose; + using namespace gtsam::serializationTestHelpers; + SmartProjectionParams params; + params.setRankTolerance(rankTol); + SmartFactor factor(model, sharedK, params); + + EXPECT(equalsObj(factor)); + EXPECT(equalsXML(factor)); + EXPECT(equalsBinary(factor)); +} + +TEST(SmartProjectionPoseFactor, serialize2) { + using namespace vanillaPose; + using namespace gtsam::serializationTestHelpers; + SmartProjectionParams params; + params.setRankTolerance(rankTol); + Pose3 bts; + SmartFactor 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); +} +/* ************************************************************************* */ +