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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 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 <gtsam/slam/ProjectionFactor.h>
#include <gtsam/slam/PoseTranslationPrior.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/base/numericalDerivative.h>
#include <gtsam/base/serializationTestHelpers.h>
#include <CppUnitLite/TestHarness.h>
#include <boost/assign/std/map.hpp>
#include <boost/bind/bind.hpp>
#include <iostream>
using namespace boost::assign;
using namespace boost::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)
boost::function<Vector(Point3)> f = //
boost::bind(&SmartFactor::whitenedError<Point3>, factor, cameras, _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
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<Pose3>(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<Pose3>(x3)));
Values result;
LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
result = optimizer.optimize();
EXPECT(assert_equal(pose_above, result.at<Pose3>(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<SmartFactor>(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<Point3> p = smartFactor1->point();
CHECK(p);
EXPECT(assert_equal(landmark1, *p));
VectorValues zeroDelta;
Vector6 delta;
delta.setZero();
zeroDelta.insert(x1, delta);
zeroDelta.insert(x2, delta);
VectorValues perturbedDelta;
delta.setOnes();
perturbedDelta.insert(x1, delta);
perturbedDelta.insert(x2, delta);
double expectedError = 2500;
// After eliminating the point, A1 and A2 contain 2-rank information on cameras:
Matrix16 A1, A2;
A1 << -10, 0, 0, 0, 1, 0;
A2 << 10, 0, 1, 0, -1, 0;
A1 *= 10. / sigma;
A2 *= 10. / sigma;
Matrix expectedInformation; // filled below
{
// createHessianFactor
Matrix66 G11 = 0.5 * A1.transpose() * A1;
Matrix66 G12 = 0.5 * A1.transpose() * A2;
Matrix66 G22 = 0.5 * A2.transpose() * A2;
Vector6 g1;
g1.setZero();
Vector6 g2;
g2.setZero();
double f = 0;
RegularHessianFactor<6> expected(x1, x2, G11, G12, g1, G22, g2, f);
expectedInformation = expected.information();
boost::shared_ptr<RegularHessianFactor<6> > actual =
smartFactor1->createHessianFactor(cameras, 0.0);
EXPECT(assert_equal(expectedInformation, actual->information(), 1e-6));
EXPECT(assert_equal(expected, *actual, 1e-6));
EXPECT_DOUBLES_EQUAL(0, actual->error(zeroDelta), 1e-6);
EXPECT_DOUBLES_EQUAL(expectedError, actual->error(perturbedDelta), 1e-6);
}
{
Matrix26 F1;
F1.setZero();
F1(0, 1) = -100;
F1(0, 3) = -10;
F1(1, 0) = 100;
F1(1, 4) = -10;
Matrix26 F2;
F2.setZero();
F2(0, 1) = -101;
F2(0, 3) = -10;
F2(0, 5) = -1;
F2(1, 0) = 100;
F2(1, 2) = 10;
F2(1, 4) = -10;
Matrix E(4, 3);
E.setZero();
E(0, 0) = 10;
E(1, 1) = 10;
E(2, 0) = 10;
E(2, 2) = 1;
E(3, 1) = 10;
SmartFactor::FBlocks Fs = list_of<Matrix>(F1)(F2);
Vector b(4);
b.setZero();
// Create smart factors
KeyVector keys;
keys.push_back(x1);
keys.push_back(x2);
// createJacobianQFactor
SharedIsotropic n = noiseModel::Isotropic::Sigma(4, sigma);
Matrix3 P = (E.transpose() * E).inverse();
JacobianFactorQ<6, 2> expectedQ(keys, Fs, E, P, b, n);
EXPECT(assert_equal(expectedInformation, expectedQ.information(), 1e-6));
boost::shared_ptr<JacobianFactorQ<6, 2> > actualQ =
smartFactor1->createJacobianQFactor(cameras, 0.0);
CHECK(actualQ);
EXPECT(assert_equal(expectedInformation, actualQ->information(), 1e-6));
EXPECT(assert_equal(expectedQ, *actualQ));
EXPECT_DOUBLES_EQUAL(0, actualQ->error(zeroDelta), 1e-6);
EXPECT_DOUBLES_EQUAL(expectedError, actualQ->error(perturbedDelta), 1e-6);
// Whiten for RegularImplicitSchurFactor (does not have noise model)
model->WhitenSystem(E, b);
Matrix3 whiteP = (E.transpose() * E).inverse();
Fs[0] = model->Whiten(Fs[0]);
Fs[1] = model->Whiten(Fs[1]);
// createRegularImplicitSchurFactor
RegularImplicitSchurFactor<Camera> expected(keys, Fs, E, whiteP, b);
boost::shared_ptr<RegularImplicitSchurFactor<Camera> > actual =
smartFactor1->createRegularImplicitSchurFactor(cameras, 0.0);
CHECK(actual);
EXPECT(assert_equal(expectedInformation, expected.information(), 1e-6));
EXPECT(assert_equal(expectedInformation, actual->information(), 1e-6));
EXPECT(assert_equal(expected, *actual));
EXPECT_DOUBLES_EQUAL(0, actual->error(zeroDelta), 1e-6);
EXPECT_DOUBLES_EQUAL(expectedError, actual->error(perturbedDelta), 1e-6);
}
{
// createJacobianSVDFactor
Vector1 b;
b.setZero();
double s = sigma * sin(M_PI_4);
SharedIsotropic n = noiseModel::Isotropic::Sigma(4 - 3, sigma);
JacobianFactor expected(x1, s * A1, x2, s * A2, b, n);
EXPECT(assert_equal(expectedInformation, expected.information(), 1e-6));
boost::shared_ptr<JacobianFactor> actual =
smartFactor1->createJacobianSVDFactor(cameras, 0.0);
CHECK(actual);
EXPECT(assert_equal(expectedInformation, actual->information(), 1e-6));
EXPECT(assert_equal(expected, *actual));
EXPECT_DOUBLES_EQUAL(0, actual->error(zeroDelta), 1e-6);
EXPECT_DOUBLES_EQUAL(expectedError, actual->error(perturbedDelta), 1e-6);
}
}
/* *************************************************************************/
TEST( 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<Pose3>(x3)));
Values result;
LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
result = optimizer.optimize();
EXPECT(assert_equal(pose_above, result.at<Pose3>(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<Pose3>(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<Pose3>(x3), result.at<Pose3>(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<Pose3>(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<Pose3>(x3), 1e-6));
}
/* *************************************************************************/
TEST( SmartProjectionPoseFactor, 3poses_projection_factor ) {
using namespace vanillaPose2;
KeyVector views {x1, x2, x3};
typedef GenericProjectionFactor<Pose3, Point3> ProjectionFactor;
NonlinearFactorGraph graph;
// Project three landmarks into three cameras
graph.emplace_shared<ProjectionFactor>(cam1.project(landmark1), model, x1, L(1), sharedK2);
graph.emplace_shared<ProjectionFactor>(cam2.project(landmark1), model, x2, L(1), sharedK2);
graph.emplace_shared<ProjectionFactor>(cam3.project(landmark1), model, x3, L(1), sharedK2);
graph.emplace_shared<ProjectionFactor>(cam1.project(landmark2), model, x1, L(2), sharedK2);
graph.emplace_shared<ProjectionFactor>(cam2.project(landmark2), model, x2, L(2), sharedK2);
graph.emplace_shared<ProjectionFactor>(cam3.project(landmark2), model, x3, L(2), sharedK2);
graph.emplace_shared<ProjectionFactor>(cam1.project(landmark3), model, x1, L(3), sharedK2);
graph.emplace_shared<ProjectionFactor>(cam2.project(landmark3), model, x2, L(3), sharedK2);
graph.emplace_shared<ProjectionFactor>(cam3.project(landmark3), model, x3, L(3), sharedK2);
const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
graph.addPrior(x1, level_pose, noisePrior);
graph.addPrior(x2, pose_right, noisePrior);
Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 10, 0., -M_PI / 10),
Point3(0.5, 0.1, 0.3));
Values values;
values.insert(x1, level_pose);
values.insert(x2, pose_right);
values.insert(x3, pose_above * noise_pose);
values.insert(L(1), landmark1);
values.insert(L(2), landmark2);
values.insert(L(3), landmark3);
DOUBLES_EQUAL(48406055, graph.error(values), 1);
LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
Values result = optimizer.optimize();
DOUBLES_EQUAL(0, graph.error(result), 1e-9);
EXPECT(assert_equal(pose_above, result.at<Pose3>(x3), 1e-7));
}
/* *************************************************************************/
TEST( 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<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( 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<PoseTranslationPrior<Pose3> >(x2, positionPrior, noisePriorTranslation);
graph.emplace_shared<PoseTranslationPrior<Pose3> >(x3, positionPrior, noisePriorTranslation);
Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
Point3(0.1, 0.1, 0.1)); // smaller noise
Values values;
values.insert(x1, cam1.pose());
values.insert(x2, pose2 * noise_pose);
values.insert(x3, pose3 * noise_pose);
// params.verbosityLM = LevenbergMarquardtParams::SUMMARY;
LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
Values result = optimizer.optimize();
EXPECT(assert_equal(pose3, result.at<Pose3>(x3)));
}
/* *************************************************************************/
TEST( 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<PoseTranslationPrior<Pose3> >(x2, positionPrior, noisePriorTranslation);
graph.emplace_shared<PoseTranslationPrior<Pose3> >(x3, positionPrior, noisePriorTranslation);
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10), Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
Point3(0.1, 0.1, 0.1)); // smaller noise
Values values;
values.insert(x1, cam1.pose());
values.insert(x2, cam2.pose());
values.insert(x3, pose3 * noise_pose);
EXPECT(
assert_equal(
Pose3(
Rot3(0.00563056869, -0.130848107, 0.991386438, -0.991390265,
-0.130426831, -0.0115837907, 0.130819108, -0.98278564,
-0.130455917),
Point3(0.0897734171, -0.110201006, 0.901022872)),
values.at<Pose3>(x3)));
Values result;
LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
result = optimizer.optimize();
// Since we do not do anything on degenerate instances (ZERO_ON_DEGENERACY)
// rotation remains the same as the initial guess, but position is fixed by PoseTranslationPrior
#ifdef GTSAM_THROW_CHEIRALITY_EXCEPTION
EXPECT(assert_equal(Pose3(values.at<Pose3>(x3).rotation(),
Point3(0,0,1)), result.at<Pose3>(x3)));
#else
// if the check is disabled, no cheirality exception if thrown and the pose converges to the right rotation
// with modest accuracy since the configuration is essentially degenerate without the translation due to noise (noise_pose)
EXPECT(assert_equal(pose3, result.at<Pose3>(x3),1e-3));
#endif
}
/* *************************************************************************/
TEST( 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<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( 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<GaussianFactor> factor = smartFactorInstance->linearize(
values);
Pose3 poseDrift = Pose3(Rot3::Ypr(-M_PI / 2, 0., -M_PI / 2), Point3(0, 0, 0));
Values rotValues;
rotValues.insert(x1, poseDrift.compose(level_pose));
rotValues.insert(x2, poseDrift.compose(pose_right));
rotValues.insert(x3, poseDrift.compose(pose_above));
boost::shared_ptr<GaussianFactor> factorRot = smartFactorInstance->linearize(
rotValues);
// Hessian is invariant to rotations in the nondegenerate case
EXPECT(assert_equal(factor->information(), factorRot->information(), 1e-7));
Pose3 poseDrift2 = Pose3(Rot3::Ypr(-M_PI / 2, -M_PI / 3, -M_PI / 2),
Point3(10, -4, 5));
Values tranValues;
tranValues.insert(x1, poseDrift2.compose(level_pose));
tranValues.insert(x2, poseDrift2.compose(pose_right));
tranValues.insert(x3, poseDrift2.compose(pose_above));
boost::shared_ptr<GaussianFactor> factorRotTran =
smartFactorInstance->linearize(tranValues);
// Hessian is invariant to rotations and translations in the nondegenerate case
EXPECT(assert_equal(factor->information(), factorRotTran->information(), 1e-7));
}
/* *************************************************************************/
TEST( 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<GaussianFactor> factor = smartFactor->linearize(values);
Pose3 poseDrift = Pose3(Rot3::Ypr(-M_PI / 2, 0., -M_PI / 2), Point3(0, 0, 0));
Values rotValues;
rotValues.insert(x1, poseDrift.compose(level_pose));
rotValues.insert(x2, poseDrift.compose(level_pose));
rotValues.insert(x3, poseDrift.compose(level_pose));
boost::shared_ptr<GaussianFactor> factorRot = //
smartFactor->linearize(rotValues);
// Hessian is invariant to rotations in the nondegenerate case
EXPECT(assert_equal(factor->information(), factorRot->information(), 1e-7));
Pose3 poseDrift2 = Pose3(Rot3::Ypr(-M_PI / 2, -M_PI / 3, -M_PI / 2),
Point3(10, -4, 5));
Values tranValues;
tranValues.insert(x1, poseDrift2.compose(level_pose));
tranValues.insert(x2, poseDrift2.compose(level_pose));
tranValues.insert(x3, poseDrift2.compose(level_pose));
boost::shared_ptr<GaussianFactor> factorRotTran = smartFactor->linearize(
tranValues);
// Hessian is invariant to rotations and translations in the nondegenerate case
EXPECT(assert_equal(factor->information(), factorRotTran->information(), 1e-7));
}
/* ************************************************************************* */
TEST( 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<Pose3>(x3)));
Values result;
LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
result = optimizer.optimize();
EXPECT(assert_equal(cam3.pose(), result.at<Pose3>(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<PoseTranslationPrior<Pose3> >(x2, positionPrior, noisePriorTranslation);
graph.emplace_shared<PoseTranslationPrior<Pose3> >(x3, positionPrior, noisePriorTranslation);
// Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI/10, 0., -M_PI/10), Point3(0.5,0.1,0.3)); // noise from regular projection factor test below
Pose3 noise_pose = Pose3(Rot3::Ypr(-M_PI / 100, 0., -M_PI / 100),
Point3(0.1, 0.1, 0.1)); // smaller noise
Values values;
values.insert(x1, cam1.pose());
values.insert(x2, cam2.pose());
// initialize third pose with some noise, we expect it to move back to original pose_above
values.insert(x3, pose3 * noise_pose);
EXPECT(
assert_equal(
Pose3(
Rot3(0.00563056869, -0.130848107, 0.991386438, -0.991390265,
-0.130426831, -0.0115837907, 0.130819108, -0.98278564,
-0.130455917),
Point3(0.0897734171, -0.110201006, 0.901022872)),
values.at<Pose3>(x3)));
Values result;
LevenbergMarquardtOptimizer optimizer(graph, values, lmParams);
result = optimizer.optimize();
EXPECT(
assert_equal(
Pose3(
Rot3(0.00563056869, -0.130848107, 0.991386438, -0.991390265,
-0.130426831, -0.0115837907, 0.130819108, -0.98278564,
-0.130455917),
Point3(0.0897734171, -0.110201006, 0.901022872)),
values.at<Pose3>(x3)));
}
/* ************************************************************************* */
BOOST_CLASS_EXPORT_GUID(gtsam::noiseModel::Constrained, "gtsam_noiseModel_Constrained");
BOOST_CLASS_EXPORT_GUID(gtsam::noiseModel::Diagonal, "gtsam_noiseModel_Diagonal");
BOOST_CLASS_EXPORT_GUID(gtsam::noiseModel::Gaussian, "gtsam_noiseModel_Gaussian");
BOOST_CLASS_EXPORT_GUID(gtsam::noiseModel::Unit, "gtsam_noiseModel_Unit");
BOOST_CLASS_EXPORT_GUID(gtsam::noiseModel::Isotropic, "gtsam_noiseModel_Isotropic");
BOOST_CLASS_EXPORT_GUID(gtsam::SharedNoiseModel, "gtsam_SharedNoiseModel");
BOOST_CLASS_EXPORT_GUID(gtsam::SharedDiagonal, "gtsam_SharedDiagonal");
TEST(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);
}
/* ************************************************************************* */
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