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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file testSmartProjectionRigFactor.cpp
* @brief Unit tests for SmartProjectionRigFactor Class
* @author Chris Beall
* @author Luca Carlone
* @author Zsolt Kira
* @author Frank Dellaert
* @date August 2021
*/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/numericalDerivative.h>
#include <gtsam/base/serializationTestHelpers.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/slam/PoseTranslationPrior.h>
#include <boost/assign/std/map.hpp>
#include <iostream>
#include "smartFactorScenarios.h"
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::L;
using symbol_shorthand::X;
// tests data
static Symbol x1('X', 1);
static Symbol x2('X', 2);
static Symbol x3('X', 3);
Key cameraId1 = 0; // first camera
Key cameraId2 = 1;
Key cameraId3 = 2;
static Point2 measurement1(323.0, 240.0);
LevenbergMarquardtParams lmParams;
// Make more verbose like so (in tests):
// params.verbosityLM = LevenbergMarquardtParams::SUMMARY;
/* ************************************************************************* */
TEST(SmartProjectionRigFactor, Constructor) {
using namespace vanillaPose;
Cameras cameraRig;
cameraRig.push_back(Camera(Pose3::identity(), sharedK));
SmartRigFactor::shared_ptr factor1(new SmartRigFactor(model, cameraRig));
}
/* ************************************************************************* */
TEST(SmartProjectionRigFactor, Constructor2) {
using namespace vanillaPose;
Cameras cameraRig;
SmartProjectionParams params;
params.setRankTolerance(rankTol);
SmartRigFactor factor1(model, cameraRig, params);
}
/* ************************************************************************* */
TEST(SmartProjectionRigFactor, Constructor3) {
using namespace vanillaPose;
Cameras cameraRig;
cameraRig.push_back(Camera(Pose3::identity(), sharedK));
SmartRigFactor::shared_ptr factor1(new SmartRigFactor(model, cameraRig));
factor1->add(measurement1, x1, cameraId1);
}
/* ************************************************************************* */
TEST(SmartProjectionRigFactor, Constructor4) {
using namespace vanillaPose;
Cameras cameraRig;
cameraRig.push_back(Camera(Pose3::identity(), sharedK));
SmartProjectionParams params;
params.setRankTolerance(rankTol);
SmartRigFactor factor1(model, cameraRig, params);
factor1.add(measurement1, x1, cameraId1);
}
/* ************************************************************************* */
TEST(SmartProjectionRigFactor, Constructor5) {
using namespace vanillaPose;
SmartProjectionParams params;
params.setRankTolerance(rankTol);
SmartRigFactor factor1(model, Camera(Pose3::identity(), sharedK), params);
factor1.add(measurement1, x1, cameraId1);
}
/* ************************************************************************* */
TEST(SmartProjectionRigFactor, Equals) {
using namespace vanillaPose;
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(Pose3::identity(), sharedK));
SmartRigFactor::shared_ptr factor1(new SmartRigFactor(model, cameraRig));
factor1->add(measurement1, x1, cameraId1);
SmartRigFactor::shared_ptr factor2(new SmartRigFactor(model, cameraRig));
factor2->add(measurement1, x1, cameraId1);
CHECK(assert_equal(*factor1, *factor2));
SmartRigFactor::shared_ptr factor3(
new SmartRigFactor(model, Camera(Pose3::identity(), sharedK)));
factor3->add(measurement1, x1); // now use default
CHECK(assert_equal(*factor1, *factor3));
}
/* *************************************************************************/
TEST(SmartProjectionRigFactor, 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);
SmartRigFactor factor(model, Camera(Pose3::identity(), sharedK));
factor.add(level_uv, x1); // both taken from the same camera
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);
SmartRigFactor::Cameras cameras = factor.cameras(values);
double actualError2 = factor.totalReprojectionError(cameras);
EXPECT_DOUBLES_EQUAL(expectedError, actualError2, 1e-7);
// Calculate expected derivative for point (easiest to check)
std::function<Vector(Point3)> f = //
std::bind(&SmartRigFactor::whitenedError<Point3>, factor, cameras,
std::placeholders::_1);
// Calculate using computeEP
Matrix actualE;
factor.triangulateAndComputeE(actualE, values);
// get point
boost::optional<Point3> point = factor.point();
CHECK(point);
// calculate numerical derivative with triangulated point
Matrix expectedE = sigma * numericalDerivative11<Vector, Point3>(f, *point);
EXPECT(assert_equal(expectedE, actualE, 1e-7));
// Calculate using reprojectionError
SmartRigFactor::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;
SmartRigFactor::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(SmartProjectionRigFactor, noisy) {
using namespace vanillaPose;
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(Pose3::identity(), sharedK));
// 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));
SmartRigFactor::shared_ptr factor(new SmartRigFactor(model, cameraRig));
factor->add(level_uv, x1, cameraId1);
factor->add(level_uv_right, x2, cameraId1);
double actualError1 = factor->error(values);
// create other factor by passing multiple measurements
SmartRigFactor::shared_ptr factor2(new SmartRigFactor(model, cameraRig));
Point2Vector measurements;
measurements.push_back(level_uv);
measurements.push_back(level_uv_right);
KeyVector views{x1, x2};
FastVector<size_t> cameraIds{0, 0};
factor2->add(measurements, views, cameraIds);
double actualError2 = factor2->error(values);
DOUBLES_EQUAL(actualError1, actualError2, 1e-7);
}
/* *************************************************************************/
TEST(SmartProjectionRigFactor, 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));
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(body_T_sensor, sharedK));
// 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};
FastVector<size_t> cameraIds{0, 0, 0};
SmartProjectionParams params;
params.setRankTolerance(1.0);
params.setDegeneracyMode(IGNORE_DEGENERACY);
params.setEnableEPI(false);
SmartRigFactor smartFactor1(model, cameraRig, params);
smartFactor1.add(
measurements_cam1,
views); // use default CameraIds since we have a single camera
SmartRigFactor smartFactor2(model, cameraRig, params);
smartFactor2.add(measurements_cam2, views);
SmartRigFactor smartFactor3(model, cameraRig, params);
smartFactor3.add(measurements_cam3, views);
const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
// Put all factors in factor graph, adding priors
NonlinearFactorGraph graph;
graph.push_back(smartFactor1);
graph.push_back(smartFactor2);
graph.push_back(smartFactor3);
graph.addPrior(x1, wTb1, noisePrior);
graph.addPrior(x2, wTb2, noisePrior);
// Check errors at ground truth poses
Values gtValues;
gtValues.insert(x1, wTb1);
gtValues.insert(x2, wTb2);
gtValues.insert(x3, wTb3);
double actualError = graph.error(gtValues);
double expectedError = 0.0;
DOUBLES_EQUAL(expectedError, actualError, 1e-7)
Pose3 noise_pose =
Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100), Point3(0.1, 0.1, 0.1));
Values values;
values.insert(x1, wTb1);
values.insert(x2, wTb2);
// initialize third pose with some noise, we expect it to move back to
// original pose3
values.insert(x3, wTb3 * noise_pose);
LevenbergMarquardtParams lmParams;
Values result;
LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
result = optimizer.optimize();
EXPECT(assert_equal(wTb3, result.at<Pose3>(x3)));
}
/* *************************************************************************/
TEST(SmartProjectionRigFactor, smartFactorWithMultipleCameras) {
using namespace vanillaPose;
// create arbitrary body_T_sensor (transforms from sensor to body)
Pose3 body_T_sensor1 =
Pose3(Rot3::Ypr(-M_PI / 2, 0., -M_PI / 2), Point3(1, 1, 1));
Pose3 body_T_sensor2 =
Pose3(Rot3::Ypr(-M_PI / 5, 0., -M_PI / 2), Point3(0, 0, 1));
Pose3 body_T_sensor3 = Pose3::identity();
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(body_T_sensor1, sharedK));
cameraRig.push_back(Camera(body_T_sensor2, sharedK));
cameraRig.push_back(Camera(body_T_sensor3, sharedK));
// These are the poses we want to estimate, from camera measurements
const Pose3 sensor_T_body1 = body_T_sensor1.inverse();
const Pose3 sensor_T_body2 = body_T_sensor2.inverse();
const Pose3 sensor_T_body3 = body_T_sensor3.inverse();
Pose3 wTb1 = cam1.pose() * sensor_T_body1;
Pose3 wTb2 = cam2.pose() * sensor_T_body2;
Pose3 wTb3 = cam3.pose() * sensor_T_body3;
// 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};
FastVector<size_t> cameraIds{0, 1, 2};
SmartProjectionParams params;
params.setRankTolerance(1.0);
params.setDegeneracyMode(IGNORE_DEGENERACY);
params.setEnableEPI(false);
SmartRigFactor smartFactor1(model, cameraRig, params);
smartFactor1.add(measurements_cam1, views, cameraIds);
SmartRigFactor smartFactor2(model, cameraRig, params);
smartFactor2.add(measurements_cam2, views, cameraIds);
SmartRigFactor smartFactor3(model, cameraRig, params);
smartFactor3.add(measurements_cam3, views, cameraIds);
const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
// Put all factors in factor graph, adding priors
NonlinearFactorGraph graph;
graph.push_back(smartFactor1);
graph.push_back(smartFactor2);
graph.push_back(smartFactor3);
graph.addPrior(x1, wTb1, noisePrior);
graph.addPrior(x2, wTb2, noisePrior);
// Check errors at ground truth poses
Values gtValues;
gtValues.insert(x1, wTb1);
gtValues.insert(x2, wTb2);
gtValues.insert(x3, wTb3);
double actualError = graph.error(gtValues);
double expectedError = 0.0;
DOUBLES_EQUAL(expectedError, actualError, 1e-7)
Pose3 noise_pose =
Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100), Point3(0.1, 0.1, 0.1));
Values values;
values.insert(x1, wTb1);
values.insert(x2, wTb2);
// initialize third pose with some noise, we expect it to move back to
// original pose3
values.insert(x3, wTb3 * noise_pose);
LevenbergMarquardtParams lmParams;
Values result;
LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
result = optimizer.optimize();
EXPECT(assert_equal(wTb3, result.at<Pose3>(x3)));
}
/* *************************************************************************/
TEST(SmartProjectionRigFactor, 3poses_smart_projection_factor) {
using namespace vanillaPose2;
Point2Vector measurements_cam1, measurements_cam2, measurements_cam3;
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(Pose3::identity(), sharedK2));
// 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};
FastVector<size_t> cameraIds{
0, 0, 0}; // 3 measurements from the same camera in the rig
SmartRigFactor::shared_ptr smartFactor1(new SmartRigFactor(model, cameraRig));
smartFactor1->add(measurements_cam1, views, cameraIds);
SmartRigFactor::shared_ptr smartFactor2(new SmartRigFactor(model, cameraRig));
smartFactor2->add(measurements_cam2, views, cameraIds);
SmartRigFactor::shared_ptr smartFactor3(new SmartRigFactor(model, cameraRig));
smartFactor3->add(measurements_cam3, views, cameraIds);
const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
NonlinearFactorGraph graph;
graph.push_back(smartFactor1);
graph.push_back(smartFactor2);
graph.push_back(smartFactor3);
graph.addPrior(x1, cam1.pose(), noisePrior);
graph.addPrior(x2, cam2.pose(), noisePrior);
Values groundTruth;
groundTruth.insert(x1, cam1.pose());
groundTruth.insert(x2, cam2.pose());
groundTruth.insert(x3, cam3.pose());
DOUBLES_EQUAL(0, graph.error(groundTruth), 1e-9);
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10),
// Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
Point3(0.1, 0.1, 0.1)); // smaller noise
Values values;
values.insert(x1, cam1.pose());
values.insert(x2, cam2.pose());
// initialize third pose with some noise, we expect it to move back to
// original pose_above
values.insert(x3, pose_above * noise_pose);
EXPECT(assert_equal(
Pose3(Rot3(0, -0.0314107591, 0.99950656, -0.99950656, -0.0313952598,
-0.000986635786, 0.0314107591, -0.999013364, -0.0313952598),
Point3(0.1, -0.1, 1.9)),
values.at<Pose3>(x3)));
Values result;
LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
result = optimizer.optimize();
EXPECT(assert_equal(pose_above, result.at<Pose3>(x3), 1e-6));
}
/* *************************************************************************/
TEST(SmartProjectionRigFactor, 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};
FastVector<size_t> cameraIds{0, 0};
SmartRigFactor::shared_ptr smartFactor1 = boost::make_shared<SmartRigFactor>(
model, Camera(Pose3::identity(), sharedK));
smartFactor1->add(measurements_cam1,
views); // we have a single camera so use default cameraIds
SmartRigFactor::Cameras cameras;
cameras.push_back(cam1);
cameras.push_back(cam2);
// Make sure triangulation works
CHECK(smartFactor1->triangulateSafe(cameras));
CHECK(!smartFactor1->isDegenerate());
CHECK(!smartFactor1->isPointBehindCamera());
boost::optional<Point3> p = smartFactor1->point();
CHECK(p);
EXPECT(assert_equal(landmark1, *p));
VectorValues zeroDelta;
Vector6 delta;
delta.setZero();
zeroDelta.insert(x1, delta);
zeroDelta.insert(x2, delta);
VectorValues perturbedDelta;
delta.setOnes();
perturbedDelta.insert(x1, delta);
perturbedDelta.insert(x2, delta);
double expectedError = 2500;
// After eliminating the point, A1 and A2 contain 2-rank information on
// cameras:
Matrix16 A1, A2;
A1 << -10, 0, 0, 0, 1, 0;
A2 << 10, 0, 1, 0, -1, 0;
A1 *= 10. / sigma;
A2 *= 10. / sigma;
Matrix expectedInformation; // filled below
{
// createHessianFactor
Matrix66 G11 = 0.5 * A1.transpose() * A1;
Matrix66 G12 = 0.5 * A1.transpose() * A2;
Matrix66 G22 = 0.5 * A2.transpose() * A2;
Vector6 g1;
g1.setZero();
Vector6 g2;
g2.setZero();
double f = 0;
RegularHessianFactor<6> expected(x1, x2, G11, G12, g1, G22, g2, f);
expectedInformation = expected.information();
Values values;
values.insert(x1, cam1.pose());
values.insert(x2, cam2.pose());
boost::shared_ptr<RegularHessianFactor<6>> actual =
smartFactor1->createHessianFactor(values, 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);
}
}
/* *************************************************************************/
TEST(SmartProjectionRigFactor, 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);
std::vector<boost::shared_ptr<Cal3_S2>> sharedKs;
sharedKs.push_back(sharedK);
sharedKs.push_back(sharedK);
sharedKs.push_back(sharedK);
// create smart factor
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(Pose3::identity(), sharedK));
FastVector<size_t> cameraIds{0, 0, 0};
SmartRigFactor::shared_ptr smartFactor1(new SmartRigFactor(model, cameraRig));
smartFactor1->add(measurements_cam1, views, cameraIds);
SmartRigFactor::shared_ptr smartFactor2(new SmartRigFactor(model, cameraRig));
smartFactor2->add(measurements_cam2, views, cameraIds);
SmartRigFactor::shared_ptr smartFactor3(new SmartRigFactor(model, cameraRig));
smartFactor3->add(measurements_cam3, views, cameraIds);
const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
NonlinearFactorGraph graph;
graph.push_back(smartFactor1);
graph.push_back(smartFactor2);
graph.push_back(smartFactor3);
graph.addPrior(x1, cam1.pose(), noisePrior);
graph.addPrior(x2, cam2.pose(), noisePrior);
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10),
// Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
Point3(0.1, 0.1, 0.1)); // smaller noise
Values values;
values.insert(x1, cam1.pose());
values.insert(x2, cam2.pose());
// initialize third pose with some noise, we expect it to move back to
// original pose_above
values.insert(x3, pose_above * noise_pose);
EXPECT(assert_equal(Pose3(Rot3(1.11022302e-16, -0.0314107591, 0.99950656,
-0.99950656, -0.0313952598, -0.000986635786,
0.0314107591, -0.999013364, -0.0313952598),
Point3(0.1, -0.1, 1.9)),
values.at<Pose3>(x3)));
Values result;
LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
result = optimizer.optimize();
EXPECT(assert_equal(pose_above, result.at<Pose3>(x3), 1e-7));
}
/* *************************************************************************/
TEST(SmartProjectionRigFactor, 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);
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(Pose3::identity(), sharedK));
FastVector<size_t> cameraIds{0, 0, 0};
SmartRigFactor::shared_ptr smartFactor1(
new SmartRigFactor(model, cameraRig, params));
smartFactor1->add(measurements_cam1, views, cameraIds);
SmartRigFactor::shared_ptr smartFactor2(
new SmartRigFactor(model, cameraRig, params));
smartFactor2->add(measurements_cam2, views, cameraIds);
SmartRigFactor::shared_ptr smartFactor3(
new SmartRigFactor(model, cameraRig, params));
smartFactor3->add(measurements_cam3, views, cameraIds);
const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
NonlinearFactorGraph graph;
graph.push_back(smartFactor1);
graph.push_back(smartFactor2);
graph.push_back(smartFactor3);
graph.addPrior(x1, cam1.pose(), noisePrior);
graph.addPrior(x2, cam2.pose(), noisePrior);
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10),
// Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
Point3(0.1, 0.1, 0.1)); // smaller noise
Values values;
values.insert(x1, cam1.pose());
values.insert(x2, cam2.pose());
values.insert(x3, pose_above * noise_pose);
// All factors are disabled and pose should remain where it is
Values result;
LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
result = optimizer.optimize();
EXPECT(assert_equal(values.at<Pose3>(x3), result.at<Pose3>(x3)));
}
/* *************************************************************************/
TEST(SmartProjectionRigFactor, 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::HESSIAN);
params.setDegeneracyMode(gtsam::ZERO_ON_DEGENERACY);
params.setLandmarkDistanceThreshold(excludeLandmarksFutherThanDist);
params.setDynamicOutlierRejectionThreshold(dynamicOutlierRejectionThreshold);
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(Pose3::identity(), sharedK));
FastVector<size_t> cameraIds{0, 0, 0};
SmartRigFactor::shared_ptr smartFactor1(
new SmartRigFactor(model, cameraRig, params));
smartFactor1->add(measurements_cam1, views, cameraIds);
SmartRigFactor::shared_ptr smartFactor2(
new SmartRigFactor(model, cameraRig, params));
smartFactor2->add(measurements_cam2, views, cameraIds);
SmartRigFactor::shared_ptr smartFactor3(
new SmartRigFactor(model, cameraRig, params));
smartFactor3->add(measurements_cam3, views, cameraIds);
SmartRigFactor::shared_ptr smartFactor4(
new SmartRigFactor(model, cameraRig, params));
smartFactor4->add(measurements_cam4, views, cameraIds);
const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
NonlinearFactorGraph graph;
graph.push_back(smartFactor1);
graph.push_back(smartFactor2);
graph.push_back(smartFactor3);
graph.push_back(smartFactor4);
graph.addPrior(x1, cam1.pose(), noisePrior);
graph.addPrior(x2, cam2.pose(), noisePrior);
Values values;
values.insert(x1, cam1.pose());
values.insert(x2, cam2.pose());
values.insert(x3, cam3.pose());
// All factors are disabled and pose should remain where it is
Values result;
LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
result = optimizer.optimize();
EXPECT(assert_equal(cam3.pose(), result.at<Pose3>(x3)));
}
/* *************************************************************************/
TEST(SmartProjectionRigFactor, 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);
params.setDegeneracyMode(gtsam::ZERO_ON_DEGENERACY);
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(Pose3::identity(), sharedK));
FastVector<size_t> cameraIds{0, 0, 0};
SmartRigFactor::shared_ptr smartFactor1(
new SmartRigFactor(model, cameraRig, params)); // HESSIAN, by default
smartFactor1->add(measurements_cam1, views, cameraIds);
SmartRigFactor::shared_ptr smartFactor2(
new SmartRigFactor(model, cameraRig, params)); // HESSIAN, by default
smartFactor2->add(measurements_cam2, views, cameraIds);
SmartRigFactor::shared_ptr smartFactor3(
new SmartRigFactor(model, cameraRig, params)); // HESSIAN, by default
smartFactor3->add(measurements_cam3, views, cameraIds);
NonlinearFactorGraph graph;
graph.push_back(smartFactor1);
graph.push_back(smartFactor2);
graph.push_back(smartFactor3);
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10),
// Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
Point3(0.1, 0.1, 0.1)); // smaller noise
Values values;
values.insert(x1, cam1.pose());
values.insert(x2, cam2.pose());
// initialize third pose with some noise, we expect it to move back to
// original pose_above
values.insert(x3, pose3 * noise_pose);
EXPECT(assert_equal(Pose3(Rot3(0.00563056869, -0.130848107, 0.991386438,
-0.991390265, -0.130426831, -0.0115837907,
0.130819108, -0.98278564, -0.130455917),
Point3(0.0897734171, -0.110201006, 0.901022872)),
values.at<Pose3>(x3)));
boost::shared_ptr<GaussianFactor> factor1 = smartFactor1->linearize(values);
boost::shared_ptr<GaussianFactor> factor2 = smartFactor2->linearize(values);
boost::shared_ptr<GaussianFactor> factor3 = smartFactor3->linearize(values);
Matrix CumulativeInformation =
factor1->information() + factor2->information() + factor3->information();
boost::shared_ptr<GaussianFactorGraph> GaussianGraph =
graph.linearize(values);
Matrix GraphInformation = GaussianGraph->hessian().first;
// Check Hessian
EXPECT(assert_equal(GraphInformation, CumulativeInformation, 1e-6));
Matrix AugInformationMatrix = factor1->augmentedInformation() +
factor2->augmentedInformation() +
factor3->augmentedInformation();
// Check Information vector
Vector InfoVector = AugInformationMatrix.block(
0, 18, 18, 1); // 18x18 Hessian + information vector
// Check Hessian
EXPECT(assert_equal(InfoVector, GaussianGraph->hessian().second, 1e-6));
}
/* *************************************************************************/
TEST(SmartProjectionRigFactor, 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);
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(Pose3::identity(), sharedK2));
FastVector<size_t> cameraIds{0, 0};
SmartRigFactor::shared_ptr smartFactor1(new SmartRigFactor(model, cameraRig));
smartFactor1->add(measurements_cam1, views, cameraIds);
Pose3 noise_pose =
Pose3(Rot3::Ypr(-M_PI / 10, 0., -M_PI / 10), Point3(0.5, 0.1, 0.3));
Values values;
values.insert(x1, cam1.pose());
values.insert(x2, cam2.pose());
boost::shared_ptr<GaussianFactor> factor = smartFactor1->linearize(values);
// compute triangulation from linearization point
// compute reprojection errors (sum squared)
// compare with factor.info(): the bottom right element is the squared sum of
// the reprojection errors (normalized by the covariance) check that it is
// correctly scaled when using noiseProjection = [1/4 0; 0 1/4]
}
/* ************************************************************************* */
TEST(SmartProjectionRigFactor, ConstructorWithCal3Bundler) {
using namespace bundlerPose;
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(Pose3::identity(), sharedBundlerK));
SmartProjectionParams params;
params.setDegeneracyMode(gtsam::ZERO_ON_DEGENERACY);
SmartRigFactor factor(model, cameraRig, params);
factor.add(measurement1, x1, cameraId1);
}
/* *************************************************************************/
TEST(SmartProjectionRigFactor, 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};
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(Pose3::identity(), sharedBundlerK));
FastVector<size_t> cameraIds{0, 0, 0};
SmartRigFactor::shared_ptr smartFactor1(new SmartRigFactor(model, cameraRig));
smartFactor1->add(measurements_cam1, views, cameraIds);
SmartRigFactor::shared_ptr smartFactor2(new SmartRigFactor(model, cameraRig));
smartFactor2->add(measurements_cam2, views, cameraIds);
SmartRigFactor::shared_ptr smartFactor3(new SmartRigFactor(model, cameraRig));
smartFactor3->add(measurements_cam3, views, cameraIds);
const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
NonlinearFactorGraph graph;
graph.push_back(smartFactor1);
graph.push_back(smartFactor2);
graph.push_back(smartFactor3);
graph.addPrior(x1, cam1.pose(), noisePrior);
graph.addPrior(x2, cam2.pose(), noisePrior);
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10),
// Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
Point3(0.1, 0.1, 0.1)); // smaller noise
Values values;
values.insert(x1, cam1.pose());
values.insert(x2, cam2.pose());
// initialize third pose with some noise, we expect it to move back to
// original pose_above
values.insert(x3, pose_above * noise_pose);
EXPECT(assert_equal(
Pose3(Rot3(0, -0.0314107591, 0.99950656, -0.99950656, -0.0313952598,
-0.000986635786, 0.0314107591, -0.999013364, -0.0313952598),
Point3(0.1, -0.1, 1.9)),
values.at<Pose3>(x3)));
Values result;
LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
result = optimizer.optimize();
EXPECT(assert_equal(cam3.pose(), result.at<Pose3>(x3), 1e-6));
}
#include <gtsam/slam/ProjectionFactor.h>
typedef GenericProjectionFactor<Pose3, Point3> TestProjectionFactor;
static Symbol l0('L', 0);
/* *************************************************************************/
TEST(SmartProjectionRigFactor,
hessianComparedToProjFactors_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 vanillaPose;
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
KeyVector keys{x1, x2, x3, x1};
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(Pose3::identity(), sharedK));
FastVector<size_t> cameraIds{0, 0, 0, 0};
SmartRigFactor::shared_ptr smartFactor1(new SmartRigFactor(model, cameraRig));
smartFactor1->add(measurements_lmk1_redundant, keys, cameraIds);
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 ProjectionFactor factor to calculate the Jacobians
Matrix F = Matrix::Zero(2 * 4, 6 * 3);
Matrix E = Matrix::Zero(2 * 4, 3);
Vector b = Vector::Zero(2 * 4);
// create projection factors rolling shutter
TestProjectionFactor factor11(measurements_lmk1_redundant[0], model, x1, l0,
sharedK);
Matrix HPoseActual, HEActual;
// note: b is minus the reprojection error, cf the smart factor jacobian
// computation
b.segment<2>(0) =
-factor11.evaluateError(pose1, *point, HPoseActual, HEActual);
F.block<2, 6>(0, 0) = HPoseActual;
E.block<2, 3>(0, 0) = HEActual;
TestProjectionFactor factor12(measurements_lmk1_redundant[1], model, x2, l0,
sharedK);
b.segment<2>(2) =
-factor12.evaluateError(pose2, *point, HPoseActual, HEActual);
F.block<2, 6>(2, 6) = HPoseActual;
E.block<2, 3>(2, 0) = HEActual;
TestProjectionFactor factor13(measurements_lmk1_redundant[2], model, x3, l0,
sharedK);
b.segment<2>(4) =
-factor13.evaluateError(pose3, *point, HPoseActual, HEActual);
F.block<2, 6>(4, 12) = HPoseActual;
E.block<2, 3>(4, 0) = HEActual;
TestProjectionFactor factor14(measurements_lmk1_redundant[3], model, x1, l0,
sharedK);
b.segment<2>(6) =
-factor11.evaluateError(pose1, *point, HPoseActual, HEActual);
F.block<2, 6>(6, 0) = HPoseActual;
E.block<2, 3>(6, 0) = HEActual;
// 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_projFactors;
nfg_projFactors.add(factor11);
nfg_projFactors.add(factor12);
nfg_projFactors.add(factor13);
nfg_projFactors.add(factor14);
values.insert(l0, *point);
double actualError = smartFactor1->error(values);
double expectedError = nfg_projFactors.error(values);
EXPECT_DOUBLES_EQUAL(expectedError, actualError, 1e-7);
}
/* *************************************************************************/
TEST(SmartProjectionRigFactor, optimization_3poses_measurementsFromSamePose) {
using namespace vanillaPose;
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
KeyVector keys{x1, x2, x3};
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(Pose3::identity(), sharedK));
FastVector<size_t> cameraIds{0, 0, 0};
FastVector<size_t> cameraIdsRedundant{0, 0, 0, 0};
// 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
KeyVector keys_redundant = keys;
keys_redundant.push_back(keys.at(0)); // we readd the first key
SmartRigFactor::shared_ptr smartFactor1(new SmartRigFactor(model, cameraRig));
smartFactor1->add(measurements_lmk1_redundant, keys_redundant,
cameraIdsRedundant);
SmartRigFactor::shared_ptr smartFactor2(new SmartRigFactor(model, cameraRig));
smartFactor2->add(measurements_lmk2, keys, cameraIds);
SmartRigFactor::shared_ptr smartFactor3(new SmartRigFactor(model, cameraRig));
smartFactor3->add(measurements_lmk3, keys, cameraIds);
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, level_pose, noisePrior);
graph.addPrior(x2, pose_right, noisePrior);
Values groundTruth;
groundTruth.insert(x1, level_pose);
groundTruth.insert(x2, pose_right);
groundTruth.insert(x3, pose_above);
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>
// this factor is slightly slower (but comparable) to original
// SmartProjectionPoseFactor
//-Total: 0 CPU (0 times, 0 wall, 0.17 children, min: 0 max: 0)
//| -SmartRigFactor LINEARIZE: 0.11 CPU (10000 times, 0.086311 wall, 0.11
// children, min: 0 max: 0) | -SmartPoseFactor LINEARIZE: 0.06 CPU (10000
// times, 0.065103 wall, 0.06 children, min: 0 max: 0)
/* *************************************************************************/
TEST(SmartProjectionRigFactor, 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();
Cameras cameraRig; // single camera in the rig
cameraRig.push_back(Camera(body_P_sensorId, sharedKSimple));
// 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++) {
SmartRigFactor::shared_ptr smartFactorP(
new SmartRigFactor(model, cameraRig));
smartFactorP->add(measurements_lmk1[0], x1, cameraId1);
smartFactorP->add(measurements_lmk1[1], x1, cameraId1);
Values values;
values.insert(x1, pose1);
values.insert(x2, pose2);
gttic_(SmartRigFactor_LINEARIZE);
smartFactorP->linearize(values);
gttoc_(SmartRigFactor_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_(SmartPoseFactor_LINEARIZE);
smartFactor->linearize(values);
gttoc_(SmartPoseFactor_LINEARIZE);
}
tictoc_print_();
}
#endif
///* *************************************************************************
///*/
// 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(SmartProjectionRigFactor, serialize) {
// using namespace vanillaPose;
// using namespace gtsam::serializationTestHelpers;
// SmartProjectionParams params;
// params.setRankTolerance(rankTol);
//
// Cameras cameraRig; // single camera in the rig
// cameraRig.push_back( Camera(Pose3::identity(), sharedK) );
//
// SmartRigFactor factor(model, cameraRig, params);
//
// EXPECT(equalsObj(factor));
// EXPECT(equalsXML(factor));
// EXPECT(equalsBinary(factor));
//}
//
//// SERIALIZATION TEST FAILS: to be fixed
////TEST(SmartProjectionRigFactor, serialize2) {
//// using namespace vanillaPose;
//// using namespace gtsam::serializationTestHelpers;
//// SmartProjectionParams params;
//// params.setRankTolerance(rankTol);
//// SmartRigFactor factor(model, params);
////
//// // insert some measurements
//// KeyVector key_view;
//// Point2Vector meas_view;
//// std::vector<boost::shared_ptr<Cal3_S2>> sharedKs;
////
//// key_view.push_back(Symbol('x', 1));
//// meas_view.push_back(Point2(10, 10));
//// sharedKs.push_back(sharedK);
//// factor.add(meas_view, key_view, sharedKs);
////
//// EXPECT(equalsObj(factor));
//// EXPECT(equalsXML(factor));
//// EXPECT(equalsBinary(factor));
////}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */
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