736 lines
29 KiB
C++
736 lines
29 KiB
C++
/* ----------------------------------------------------------------------------
|
|
|
|
* 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 TestSmartProjectionFactor.cpp
|
|
* @brief Unit tests for ProjectionFactor Class
|
|
* @author Frank Dellaert
|
|
* @date Nov 2009
|
|
*/
|
|
|
|
#include <CppUnitLite/TestHarness.h>
|
|
#include <iostream>
|
|
|
|
|
|
|
|
#include <gtsam/slam/PriorFactor.h>
|
|
#include <gtsam/slam/BetweenFactor.h>
|
|
#include <gtsam/slam/ProjectionFactor.h>
|
|
#include <gtsam/slam/PoseTranslationPrior.h>
|
|
#include <gtsam_unstable/slam/SmartProjectionFactor.h>
|
|
#include <gtsam/nonlinear/ISAM2.h>
|
|
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
|
|
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
|
|
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
|
|
#include <gtsam/nonlinear/LinearContainerFactor.h>
|
|
#include <gtsam/nonlinear/Values.h>
|
|
#include <gtsam/inference/Symbol.h>
|
|
#include <gtsam/inference/JunctionTree.h>
|
|
#include <gtsam_unstable/geometry/triangulation.h>
|
|
#include <gtsam/geometry/Pose3.h>
|
|
#include <gtsam/geometry/Point3.h>
|
|
#include <gtsam/geometry/Point2.h>
|
|
#include <gtsam/geometry/Cal3DS2.h>
|
|
#include <gtsam/geometry/Cal3_S2.h>
|
|
#include <gtsam/geometry/SimpleCamera.h>
|
|
|
|
|
|
#include <boost/assign/std/vector.hpp>
|
|
|
|
using namespace std;
|
|
using namespace boost::assign;
|
|
using namespace gtsam;
|
|
|
|
// make a realistic calibration matrix
|
|
static double fov = 60; // degrees
|
|
static size_t w=640,h=480;
|
|
static Cal3_S2::shared_ptr K(new Cal3_S2(fov,w,h));
|
|
|
|
// Create a noise model for the pixel error
|
|
static SharedNoiseModel model(noiseModel::Unit::Create(2));
|
|
|
|
// Convenience for named keys
|
|
using symbol_shorthand::X;
|
|
using symbol_shorthand::L;
|
|
|
|
typedef SmartProjectionFactor<Pose3, Point3> TestSmartProjectionFactor;
|
|
|
|
/* ************************************************************************* */
|
|
TEST( SmartProjectionFactor, Constructor) {
|
|
Key poseKey(X(1));
|
|
|
|
std::vector<Key> views;
|
|
views += poseKey;
|
|
|
|
std::vector<Point2> measurements;
|
|
measurements.push_back(Point2(323.0, 240.0));
|
|
|
|
TestSmartProjectionFactor factor(measurements, model, views, K);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( SmartProjectionFactor, ConstructorWithTransform) {
|
|
Key poseKey(X(1));
|
|
|
|
std::vector<Key> views;
|
|
views += poseKey;
|
|
|
|
std::vector<Point2> measurements;
|
|
measurements.push_back(Point2(323.0, 240.0));
|
|
Pose3 body_P_sensor(Rot3::RzRyRx(-M_PI_2, 0.0, -M_PI_2), Point3(0.25, -0.10, 1.0));
|
|
|
|
TestSmartProjectionFactor factor(measurements, model, views, K, body_P_sensor);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( SmartProjectionFactor, Equals ) {
|
|
// Create two identical factors and make sure they're equal
|
|
std::vector<Point2> measurements;
|
|
measurements.push_back(Point2(323.0, 240.0));
|
|
|
|
std::vector<Key> views;
|
|
views += X(1);
|
|
TestSmartProjectionFactor factor1(measurements, model, views, K);
|
|
TestSmartProjectionFactor factor2(measurements, model, views, K);
|
|
|
|
CHECK(assert_equal(factor1, factor2));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( SmartProjectionFactor, EqualsWithTransform ) {
|
|
// Create two identical factors and make sure they're equal
|
|
std::vector<Point2> measurements;
|
|
measurements.push_back(Point2(323.0, 240.0));
|
|
Pose3 body_P_sensor(Rot3::RzRyRx(-M_PI_2, 0.0, -M_PI_2), Point3(0.25, -0.10, 1.0));
|
|
|
|
std::vector<Key> views;
|
|
views += X(1);
|
|
TestSmartProjectionFactor factor1(measurements, model, views, K, body_P_sensor);
|
|
TestSmartProjectionFactor factor2(measurements, model, views, K, body_P_sensor);
|
|
|
|
CHECK(assert_equal(factor1, factor2));
|
|
}
|
|
|
|
|
|
/* ************************************************************************* */
|
|
TEST( SmartProjectionFactor, noisy ){
|
|
cout << " ************************ SmartProjectionFactor: noisy ****************************" << endl;
|
|
|
|
Symbol x1('X', 1);
|
|
Symbol x2('X', 2);
|
|
|
|
const SharedDiagonal noiseProjection = noiseModel::Isotropic::Sigma(2, 1);
|
|
|
|
std::vector<Key> views;
|
|
views += x1, x2; //, x3;
|
|
|
|
Cal3_S2::shared_ptr K(new Cal3_S2(1500, 1200, 0, 640, 480));
|
|
|
|
// create first camera. Looking along X-axis, 1 meter above ground plane (x-y)
|
|
Pose3 level_pose = Pose3(Rot3::ypr(-M_PI/2, 0., -M_PI/2), gtsam::Point3(0,0,1));
|
|
SimpleCamera level_camera(level_pose, *K);
|
|
|
|
// create second camera 1 meter to the right of first camera
|
|
Pose3 level_pose_right = level_pose * Pose3(Rot3(), Point3(1,0,0));
|
|
SimpleCamera level_camera_right(level_pose_right, *K);
|
|
|
|
// landmark ~5 meters infront of camera
|
|
Point3 landmark(5, 0.5, 1.2);
|
|
|
|
// 1. Project two landmarks into two cameras and triangulate
|
|
Point2 pixelError(0.2,0.2);
|
|
Point2 level_uv = level_camera.project(landmark) + pixelError;
|
|
Point2 level_uv_right = level_camera_right.project(landmark);
|
|
|
|
Values values;
|
|
values.insert(x1, level_pose);
|
|
Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/10, 0., -M_PI/10), gtsam::Point3(0.5,0.1,0.3));
|
|
values.insert(x2, level_pose_right.compose(noise_pose));
|
|
|
|
vector<Point2> measurements;
|
|
measurements += level_uv, level_uv_right;
|
|
|
|
SmartProjectionFactor<Pose3, Point3, Cal3_S2>::shared_ptr
|
|
smartFactor(new SmartProjectionFactor<Pose3, Point3, Cal3_S2>(measurements, noiseProjection, views, K));
|
|
|
|
double actualError = smartFactor->error(values);
|
|
std::cout << "Error: " << actualError << std::endl;
|
|
|
|
// we do not expect to be able to predict the error, since the error on the pixel will change
|
|
// the triangulation of the landmark which is internal to the factor.
|
|
// DOUBLES_EQUAL(expectedError, actualError, 1e-7);
|
|
}
|
|
|
|
/* *************************************************************************
|
|
TEST( SmartProjectionFactor, 3poses_1iteration_projection_factor_comparison ){
|
|
cout << " ************************ SmartProjectionFactor: 3 cams + 3 landmarks, 1 iteration, comparison b/w Generic and Smart Projection Factors **********************" << endl;
|
|
|
|
Symbol x1('X', 1);
|
|
Symbol x2('X', 2);
|
|
Symbol x3('X', 3);
|
|
|
|
const SharedDiagonal noiseProjection = noiseModel::Isotropic::Sigma(2, 1);
|
|
|
|
std::vector<Key> views;
|
|
views += x1, x2, x3;
|
|
|
|
Cal3_S2::shared_ptr K(new Cal3_S2(1500, 1200, 0, 640, 480));
|
|
|
|
// create first camera. Looking along X-axis, 1 meter above ground plane (x-y)
|
|
Pose3 pose1 = Pose3(Rot3::ypr(-M_PI/2, 0., -M_PI/2), gtsam::Point3(0,0,1));
|
|
SimpleCamera cam1(pose1, *K);
|
|
|
|
// create second camera 1 meter to the right of first camera
|
|
Pose3 pose2 = pose1 * Pose3(Rot3(), Point3(1,0,0));
|
|
SimpleCamera cam2(pose2, *K);
|
|
|
|
// create third camera 1 meter above the first camera
|
|
Pose3 pose3 = pose1 * Pose3(Rot3(), Point3(0,-1,0));
|
|
SimpleCamera cam3(pose3, *K);
|
|
|
|
// three landmarks ~5 meters infront of camera
|
|
Point3 landmark1(5, 0.5, 1.2);
|
|
Point3 landmark2(5, -0.5, 1.2);
|
|
Point3 landmark3(3, 0, 3.0);
|
|
|
|
vector<Point2> measurements_cam1, measurements_cam2, measurements_cam3;
|
|
|
|
// 1. Project three landmarks into three cameras and triangulate
|
|
Point2 cam1_uv1 = cam1.project(landmark1);
|
|
Point2 cam2_uv1 = cam2.project(landmark1);
|
|
Point2 cam3_uv1 = cam3.project(landmark1);
|
|
measurements_cam1 += cam1_uv1, cam2_uv1, cam3_uv1;
|
|
|
|
//
|
|
Point2 cam1_uv2 = cam1.project(landmark2);
|
|
Point2 cam2_uv2 = cam2.project(landmark2);
|
|
Point2 cam3_uv2 = cam3.project(landmark2);
|
|
measurements_cam2 += cam1_uv2, cam2_uv2, cam3_uv2;
|
|
|
|
|
|
Point2 cam1_uv3 = cam1.project(landmark3);
|
|
Point2 cam2_uv3 = cam2.project(landmark3);
|
|
Point2 cam3_uv3 = cam3.project(landmark3);
|
|
measurements_cam3 += cam1_uv3, cam2_uv3, cam3_uv3;
|
|
|
|
typedef SmartProjectionFactor<Pose3, Point3, Cal3_S2> SmartFactor;
|
|
typedef GenericProjectionFactor<Pose3, Point3> ProjectionFactor;
|
|
|
|
SmartFactor::shared_ptr smartFactor1(new SmartFactor(measurements_cam1, noiseProjection, views, K, boost::make_optional<Point3>(landmark1) ));
|
|
SmartFactor::shared_ptr smartFactor2(new SmartFactor(measurements_cam2, noiseProjection, views, K, boost::make_optional<Point3>(landmark2) ));
|
|
SmartFactor::shared_ptr smartFactor3(new SmartFactor(measurements_cam3, noiseProjection, views, K, boost::make_optional<Point3>(landmark3) ));
|
|
|
|
const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
|
|
|
|
NonlinearFactorGraph graphWithOriginalFactor;
|
|
graphWithOriginalFactor.push_back(ProjectionFactor(cam1.project(landmark1), noiseProjection, x1, L(1), K));
|
|
graphWithOriginalFactor.push_back(ProjectionFactor(cam2.project(landmark1), noiseProjection, x2, L(1), K));
|
|
graphWithOriginalFactor.push_back(ProjectionFactor(cam3.project(landmark1), noiseProjection, x3, L(1), K));
|
|
|
|
graphWithOriginalFactor.push_back(ProjectionFactor(cam1.project(landmark2), noiseProjection, x1, L(2), K));
|
|
graphWithOriginalFactor.push_back(ProjectionFactor(cam2.project(landmark2), noiseProjection, x2, L(2), K));
|
|
graphWithOriginalFactor.push_back(ProjectionFactor(cam3.project(landmark2), noiseProjection, x3, L(2), K));
|
|
|
|
graphWithOriginalFactor.push_back(ProjectionFactor(cam1.project(landmark3), noiseProjection, x1, L(3), K));
|
|
graphWithOriginalFactor.push_back(ProjectionFactor(cam2.project(landmark3), noiseProjection, x2, L(3), K));
|
|
graphWithOriginalFactor.push_back(ProjectionFactor(cam3.project(landmark3), noiseProjection, x3, L(3), K));
|
|
|
|
graphWithOriginalFactor.push_back(PriorFactor<Pose3>(x1, pose1, noisePrior));
|
|
graphWithOriginalFactor.push_back(PriorFactor<Pose3>(x2, pose2, noisePrior));
|
|
|
|
Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/10, 0., -M_PI/10), gtsam::Point3(0.5,0.1,0.3));
|
|
Values valuesOriginalFactor;
|
|
valuesOriginalFactor.insert(x1, pose1);
|
|
valuesOriginalFactor.insert(x2, pose2);
|
|
valuesOriginalFactor.insert(x3, pose3* noise_pose);
|
|
valuesOriginalFactor.insert(L(1), landmark1);
|
|
valuesOriginalFactor.insert(L(2), landmark2);
|
|
valuesOriginalFactor.insert(L(3), landmark3);
|
|
|
|
NonlinearFactorGraph graphWithSmartFactor;
|
|
graphWithSmartFactor.push_back(smartFactor1);
|
|
graphWithSmartFactor.push_back(smartFactor2);
|
|
graphWithSmartFactor.push_back(smartFactor3);
|
|
graphWithSmartFactor.push_back(PriorFactor<Pose3>(x1, pose1, noisePrior));
|
|
graphWithSmartFactor.push_back(PriorFactor<Pose3>(x2, pose2, noisePrior));
|
|
|
|
Values valuesSmartFactor;
|
|
valuesSmartFactor.insert(x1, pose1);
|
|
valuesSmartFactor.insert(x2, pose2);
|
|
// initialize third pose with some noise, we expect it to move back to original pose3
|
|
valuesSmartFactor.insert(x3, pose3*noise_pose);
|
|
valuesSmartFactor.at<Pose3>(x3).print("Pose3 before optimization: ");
|
|
pose3.print("Pose3 ground truth: ");
|
|
|
|
LevenbergMarquardtParams params;
|
|
params.maxIterations = 1;
|
|
params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
|
|
params.verbosity = NonlinearOptimizerParams::ERROR;
|
|
|
|
Values resultWithOriginalFactor;
|
|
std::cout << "\n=========================================" << std::endl;
|
|
std::cout << "Optimizing GenericProjectionFactor" << std::endl;
|
|
LevenbergMarquardtOptimizer optimizerForOriginalFactor(graphWithOriginalFactor, valuesOriginalFactor, params);
|
|
resultWithOriginalFactor = optimizerForOriginalFactor.optimize();
|
|
|
|
Values resultWithSmartFactor;
|
|
std::cout << "\n=========================================" << std::endl;
|
|
std::cout << "Optimizing SmartProjectionfactor" << std::endl;
|
|
LevenbergMarquardtOptimizer optimizerForSmartFactor(graphWithSmartFactor, valuesSmartFactor, params);
|
|
resultWithSmartFactor = optimizerForSmartFactor.optimize();
|
|
|
|
std::cout << "\n=========================================" << std::endl;
|
|
// result.print("results of 3 camera, 3 landmark optimization \n");
|
|
resultWithOriginalFactor.at<Pose3>(x3).print("Original: Pose3 after optimization: ");
|
|
resultWithSmartFactor.at<Pose3>(x3).print("\nSmart: Pose3 after optimization: ");
|
|
EXPECT(assert_equal(resultWithOriginalFactor.at<Pose3>(x3),resultWithSmartFactor.at<Pose3>(x3)));
|
|
|
|
std::cout << "\n================= STARTING GN ITERATION ========================" << std::endl;
|
|
GaussNewtonParams params2;
|
|
params2.maxIterations = 1;
|
|
Values resultWithOriginalFactor2;
|
|
params2.verbosity = NonlinearOptimizerParams::DELTA;
|
|
GaussNewtonOptimizer optimizerForOriginalFactor2(graphWithOriginalFactor, valuesOriginalFactor, params2);
|
|
resultWithOriginalFactor2 = optimizerForOriginalFactor2.optimize();
|
|
|
|
Values resultWithSmartFactor2;
|
|
GaussNewtonOptimizer optimizerForSmartFactor2(graphWithSmartFactor, valuesSmartFactor, params2);
|
|
resultWithSmartFactor2 = optimizerForSmartFactor2.optimize();
|
|
|
|
std::cout << "\n=========================================" << std::endl;
|
|
resultWithOriginalFactor2.at<Pose3>(x3).print("Original: Pose3 after optimization (GaussNewton): ");
|
|
resultWithSmartFactor2.at<Pose3>(x3).print("\nSmart: Pose3 after optimization (GaussNewton): ");
|
|
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( SmartProjectionFactor, 3poses_smart_projection_factor ){
|
|
cout << " ************************ SmartProjectionFactor: 3 cams + 3 landmarks **********************" << endl;
|
|
|
|
Symbol x1('X', 1);
|
|
Symbol x2('X', 2);
|
|
Symbol x3('X', 3);
|
|
|
|
const SharedDiagonal noiseProjection = noiseModel::Isotropic::Sigma(2, 1);
|
|
|
|
std::vector<Key> views;
|
|
views += x1, x2, x3;
|
|
|
|
Cal3_S2::shared_ptr K(new Cal3_S2(1500, 1200, 0, 640, 480));
|
|
|
|
// create first camera. Looking along X-axis, 1 meter above ground plane (x-y)
|
|
Pose3 pose1 = Pose3(Rot3::ypr(-M_PI/2, 0., -M_PI/2), gtsam::Point3(0,0,1));
|
|
SimpleCamera cam1(pose1, *K);
|
|
|
|
// create second camera 1 meter to the right of first camera
|
|
Pose3 pose2 = pose1 * Pose3(Rot3(), Point3(1,0,0));
|
|
SimpleCamera cam2(pose2, *K);
|
|
|
|
// create third camera 1 meter above the first camera
|
|
Pose3 pose3 = pose1 * Pose3(Rot3(), Point3(0,-1,0));
|
|
SimpleCamera cam3(pose3, *K);
|
|
|
|
// three landmarks ~5 meters infront of camera
|
|
Point3 landmark1(5, 0.5, 1.2);
|
|
Point3 landmark2(5, -0.5, 1.2);
|
|
Point3 landmark3(3, 0, 3.0);
|
|
|
|
vector<Point2> measurements_cam1, measurements_cam2, measurements_cam3;
|
|
|
|
// 1. Project three landmarks into three cameras and triangulate
|
|
Point2 cam1_uv1 = cam1.project(landmark1);
|
|
Point2 cam2_uv1 = cam2.project(landmark1);
|
|
Point2 cam3_uv1 = cam3.project(landmark1);
|
|
measurements_cam1 += cam1_uv1, cam2_uv1, cam3_uv1;
|
|
|
|
//
|
|
Point2 cam1_uv2 = cam1.project(landmark2);
|
|
Point2 cam2_uv2 = cam2.project(landmark2);
|
|
Point2 cam3_uv2 = cam3.project(landmark2);
|
|
measurements_cam2 += cam1_uv2, cam2_uv2, cam3_uv2;
|
|
|
|
|
|
Point2 cam1_uv3 = cam1.project(landmark3);
|
|
Point2 cam2_uv3 = cam2.project(landmark3);
|
|
Point2 cam3_uv3 = cam3.project(landmark3);
|
|
measurements_cam3 += cam1_uv3, cam2_uv3, cam3_uv3;
|
|
|
|
typedef SmartProjectionFactor<Pose3, Point3, Cal3_S2> SmartFactor;
|
|
|
|
SmartFactor::shared_ptr smartFactor1(new SmartFactor(measurements_cam1, noiseProjection, views, K));
|
|
SmartFactor::shared_ptr smartFactor2(new SmartFactor(measurements_cam2, noiseProjection, views, K));
|
|
SmartFactor::shared_ptr smartFactor3(new SmartFactor(measurements_cam3, noiseProjection, views, K));
|
|
|
|
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(PriorFactor<Pose3>(x1, pose1, noisePrior));
|
|
graph.push_back(PriorFactor<Pose3>(x2, pose2, noisePrior));
|
|
|
|
// Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/10, 0., -M_PI/10), gtsam::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), gtsam::Point3(0.1,0.1,0.1)); // smaller noise
|
|
Values values;
|
|
values.insert(x1, pose1);
|
|
values.insert(x2, pose2);
|
|
// initialize third pose with some noise, we expect it to move back to original pose3
|
|
values.insert(x3, pose3*noise_pose);
|
|
values.at<Pose3>(x3).print("Smart: Pose3 before optimization: ");
|
|
|
|
LevenbergMarquardtParams params;
|
|
params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
|
|
params.verbosity = NonlinearOptimizerParams::ERROR;
|
|
|
|
Values result;
|
|
gttic_(SmartProjectionFactor);
|
|
LevenbergMarquardtOptimizer optimizer(graph, values, params);
|
|
result = optimizer.optimize();
|
|
gttoc_(SmartProjectionFactor);
|
|
tictoc_finishedIteration_();
|
|
|
|
// result.print("results of 3 camera, 3 landmark optimization \n");
|
|
result.at<Pose3>(x3).print("Smart: Pose3 after optimization: ");
|
|
EXPECT(assert_equal(pose3,result.at<Pose3>(x3)));
|
|
tictoc_print_();
|
|
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( SmartProjectionFactor, 3poses_iterative_smart_projection_factor ){
|
|
cout << " ************************ SmartProjectionFactor: 3 cams + 3 landmarks **********************" << endl;
|
|
|
|
Symbol x1('X', 1);
|
|
Symbol x2('X', 2);
|
|
Symbol x3('X', 3);
|
|
|
|
const SharedDiagonal noiseProjection = noiseModel::Isotropic::Sigma(2, 1);
|
|
|
|
std::vector<Key> views;
|
|
views += x1, x2, x3;
|
|
|
|
Cal3_S2::shared_ptr K(new Cal3_S2(1500, 1200, 0, 640, 480));
|
|
|
|
// create first camera. Looking along X-axis, 1 meter above ground plane (x-y)
|
|
Pose3 pose1 = Pose3(Rot3::ypr(-M_PI/2, 0., -M_PI/2), gtsam::Point3(0,0,1));
|
|
SimpleCamera cam1(pose1, *K);
|
|
|
|
// create second camera 1 meter to the right of first camera
|
|
Pose3 pose2 = pose1 * Pose3(Rot3(), Point3(1,0,0));
|
|
SimpleCamera cam2(pose2, *K);
|
|
|
|
// create third camera 1 meter above the first camera
|
|
Pose3 pose3 = pose1 * Pose3(Rot3(), Point3(0,-1,0));
|
|
SimpleCamera cam3(pose3, *K);
|
|
|
|
// three landmarks ~5 meters infront of camera
|
|
Point3 landmark1(5, 0.5, 1.2);
|
|
Point3 landmark2(5, -0.5, 1.2);
|
|
Point3 landmark3(3, 0, 3.0);
|
|
|
|
vector<Point2> measurements_cam1, measurements_cam2, measurements_cam3;
|
|
|
|
// 1. Project three landmarks into three cameras and triangulate
|
|
Point2 cam1_uv1 = cam1.project(landmark1);
|
|
Point2 cam2_uv1 = cam2.project(landmark1);
|
|
Point2 cam3_uv1 = cam3.project(landmark1);
|
|
measurements_cam1 += cam1_uv1, cam2_uv1, cam3_uv1;
|
|
|
|
//
|
|
Point2 cam1_uv2 = cam1.project(landmark2);
|
|
Point2 cam2_uv2 = cam2.project(landmark2);
|
|
Point2 cam3_uv2 = cam3.project(landmark2);
|
|
measurements_cam2 += cam1_uv2, cam2_uv2, cam3_uv2;
|
|
|
|
|
|
Point2 cam1_uv3 = cam1.project(landmark3);
|
|
Point2 cam2_uv3 = cam2.project(landmark3);
|
|
Point2 cam3_uv3 = cam3.project(landmark3);
|
|
measurements_cam3 += cam1_uv3, cam2_uv3, cam3_uv3;
|
|
|
|
typedef SmartProjectionFactor<Pose3, Point3, Cal3_S2> SmartFactor;
|
|
|
|
SmartFactor::shared_ptr smartFactor1(new SmartFactor(noiseProjection, K));
|
|
smartFactor1->add(cam1_uv1, views[0]);
|
|
smartFactor1->add(cam2_uv1, views[1]);
|
|
smartFactor1->add(cam3_uv1, views[2]);
|
|
|
|
SmartFactor::shared_ptr smartFactor2(new SmartFactor(noiseProjection, K));
|
|
smartFactor2->add(cam1_uv2, views[0]);
|
|
smartFactor2->add(cam2_uv2, views[1]);
|
|
smartFactor2->add(cam3_uv2, views[2]);
|
|
|
|
SmartFactor::shared_ptr smartFactor3(new SmartFactor(noiseProjection, K));
|
|
smartFactor3->add(cam1_uv3, views[0]);
|
|
smartFactor3->add(cam2_uv3, views[1]);
|
|
smartFactor3->add(cam3_uv3, views[2]);
|
|
|
|
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(PriorFactor<Pose3>(x1, pose1, noisePrior));
|
|
graph.push_back(PriorFactor<Pose3>(x2, pose2, noisePrior));
|
|
|
|
// Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/10, 0., -M_PI/10), gtsam::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), gtsam::Point3(0.1,0.1,0.1)); // smaller noise
|
|
Values values;
|
|
values.insert(x1, pose1);
|
|
values.insert(x2, pose2);
|
|
// initialize third pose with some noise, we expect it to move back to original pose3
|
|
values.insert(x3, pose3*noise_pose);
|
|
values.at<Pose3>(x3).print("Smart: Pose3 before optimization: ");
|
|
|
|
LevenbergMarquardtParams params;
|
|
params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
|
|
params.verbosity = NonlinearOptimizerParams::ERROR;
|
|
|
|
Values result;
|
|
gttic_(SmartProjectionFactor);
|
|
LevenbergMarquardtOptimizer optimizer(graph, values, params);
|
|
result = optimizer.optimize();
|
|
gttoc_(SmartProjectionFactor);
|
|
tictoc_finishedIteration_();
|
|
|
|
// result.print("results of 3 camera, 3 landmark optimization \n");
|
|
result.at<Pose3>(x3).print("Smart: Pose3 after optimization: ");
|
|
EXPECT(assert_equal(pose3,result.at<Pose3>(x3)));
|
|
tictoc_print_();
|
|
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( SmartProjectionFactor, 3poses_projection_factor ){
|
|
// cout << " ************************ Normal ProjectionFactor: 3 cams + 3 landmarks **********************" << endl;
|
|
|
|
Symbol x1('X', 1);
|
|
Symbol x2('X', 2);
|
|
Symbol x3('X', 3);
|
|
|
|
const SharedDiagonal noiseProjection = noiseModel::Isotropic::Sigma(2, 1);
|
|
|
|
std::vector<Key> views;
|
|
views += x1, x2, x3;
|
|
|
|
Cal3_S2::shared_ptr K(new Cal3_S2(1500, 1200, 0, 640, 480));
|
|
// create first camera. Looking along X-axis, 1 meter above ground plane (x-y)
|
|
Pose3 pose1 = Pose3(Rot3::ypr(-M_PI/2, 0., -M_PI/2), gtsam::Point3(0,0,1));
|
|
SimpleCamera cam1(pose1, *K);
|
|
|
|
// create second camera 1 meter to the right of first camera
|
|
Pose3 pose2 = pose1 * Pose3(Rot3(), Point3(1,0,0));
|
|
SimpleCamera cam2(pose2, *K);
|
|
|
|
// create third camera 1 meter above the first camera
|
|
Pose3 pose3 = pose1 * Pose3(Rot3(), Point3(0,-1,0));
|
|
SimpleCamera cam3(pose3, *K);
|
|
|
|
// three landmarks ~5 meters infront of camera
|
|
Point3 landmark1(5, 0.5, 1.2);
|
|
Point3 landmark2(5, -0.5, 1.2);
|
|
Point3 landmark3(3, 0, 3.0);
|
|
|
|
typedef GenericProjectionFactor<Pose3, Point3> ProjectionFactor;
|
|
NonlinearFactorGraph graph;
|
|
|
|
// 1. Project three landmarks into three cameras and triangulate
|
|
graph.push_back(ProjectionFactor(cam1.project(landmark1), noiseProjection, x1, L(1), K));
|
|
graph.push_back(ProjectionFactor(cam2.project(landmark1), noiseProjection, x2, L(1), K));
|
|
graph.push_back(ProjectionFactor(cam3.project(landmark1), noiseProjection, x3, L(1), K));
|
|
|
|
//
|
|
graph.push_back(ProjectionFactor(cam1.project(landmark2), noiseProjection, x1, L(2), K));
|
|
graph.push_back(ProjectionFactor(cam2.project(landmark2), noiseProjection, x2, L(2), K));
|
|
graph.push_back(ProjectionFactor(cam3.project(landmark2), noiseProjection, x3, L(2), K));
|
|
|
|
graph.push_back(ProjectionFactor(cam1.project(landmark3), noiseProjection, x1, L(3), K));
|
|
graph.push_back(ProjectionFactor(cam2.project(landmark3), noiseProjection, x2, L(3), K));
|
|
graph.push_back(ProjectionFactor(cam3.project(landmark3), noiseProjection, x3, L(3), K));
|
|
|
|
const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
|
|
graph.push_back(PriorFactor<Pose3>(x1, pose1, noisePrior));
|
|
graph.push_back(PriorFactor<Pose3>(x2, pose2, noisePrior));
|
|
|
|
Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/10, 0., -M_PI/10), gtsam::Point3(0.5,0.1,0.3));
|
|
Values values;
|
|
values.insert(x1, pose1);
|
|
values.insert(x2, pose2);
|
|
values.insert(x3, pose3* noise_pose);
|
|
values.insert(L(1), landmark1);
|
|
values.insert(L(2), landmark2);
|
|
values.insert(L(3), landmark3);
|
|
// values.at<Pose3>(x3).print("Pose3 before optimization: ");
|
|
|
|
LevenbergMarquardtParams params;
|
|
// params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
|
|
// params.verbosity = NonlinearOptimizerParams::ERROR;
|
|
LevenbergMarquardtOptimizer optimizer(graph, values, params);
|
|
Values result = optimizer.optimize();
|
|
|
|
// result.at<Pose3>(x3).print("Pose3 after optimization: ");
|
|
EXPECT(assert_equal(pose3,result.at<Pose3>(x3)));
|
|
|
|
}
|
|
|
|
/* *************************************************************************
|
|
TEST( SmartProjectionFactor, 3poses_rotation_only_smart_projection_factor ){
|
|
cout << " ************************ SmartProjectionFactor: 3 cams + 3 landmarks: Rotation Only**********************" << endl;
|
|
|
|
Symbol x1('X', 1);
|
|
Symbol x2('X', 2);
|
|
Symbol x3('X', 3);
|
|
|
|
const SharedDiagonal noiseProjection = noiseModel::Isotropic::Sigma(2, 1);
|
|
|
|
std::vector<Key> views;
|
|
views += x1, x2, x3;
|
|
|
|
Cal3_S2::shared_ptr K(new Cal3_S2(1500, 1200, 0, 640, 480));
|
|
|
|
// create first camera. Looking along X-axis, 1 meter above ground plane (x-y)
|
|
Pose3 pose1 = Pose3(Rot3::ypr(-M_PI/2, 0., -M_PI/2), gtsam::Point3(0,0,1));
|
|
SimpleCamera cam1(pose1, *K);
|
|
|
|
// create second camera 1 meter to the right of first camera
|
|
Pose3 pose2 = Pose3(Rot3::RzRyRx(-0.05, 0.0, -0.05), Point3(0,0,0)) * pose1;
|
|
SimpleCamera cam2(pose2, *K);
|
|
|
|
// create third camera 1 meter above the first camera
|
|
Pose3 pose3 = Pose3(Rot3::RzRyRx(-0.05, 0.0, -0.05), Point3(0,0,0)) * pose2;
|
|
SimpleCamera cam3(pose3, *K);
|
|
|
|
// three landmarks ~5 meters infront of camera
|
|
Point3 landmark1(5, 0.5, 1.2);
|
|
Point3 landmark2(5, -0.5, 1.2);
|
|
Point3 landmark3(3, 0, 3.0);
|
|
|
|
vector<Point2> measurements_cam1, measurements_cam2, measurements_cam3;
|
|
|
|
// 1. Project three landmarks into three cameras and triangulate
|
|
Point2 cam1_uv1 = cam1.project(landmark1);
|
|
Point2 cam2_uv1 = cam2.project(landmark1);
|
|
Point2 cam3_uv1 = cam3.project(landmark1);
|
|
measurements_cam1 += cam1_uv1, cam2_uv1, cam3_uv1;
|
|
|
|
//
|
|
Point2 cam1_uv2 = cam1.project(landmark2);
|
|
Point2 cam2_uv2 = cam2.project(landmark2);
|
|
Point2 cam3_uv2 = cam3.project(landmark2);
|
|
measurements_cam2 += cam1_uv2, cam2_uv2, cam3_uv2;
|
|
|
|
|
|
Point2 cam1_uv3 = cam1.project(landmark3);
|
|
Point2 cam2_uv3 = cam2.project(landmark3);
|
|
Point2 cam3_uv3 = cam3.project(landmark3);
|
|
measurements_cam3 += cam1_uv3, cam2_uv3, cam3_uv3;
|
|
|
|
typedef SmartProjectionFactor<Pose3, Point3, Cal3_S2> SmartFactor;
|
|
|
|
SmartFactor::shared_ptr smartFactor1(new SmartFactor(measurements_cam1, noiseProjection, views, K));
|
|
SmartFactor::shared_ptr smartFactor2(new SmartFactor(measurements_cam2, noiseProjection, views, K));
|
|
SmartFactor::shared_ptr smartFactor3(new SmartFactor(measurements_cam3, noiseProjection, views, K));
|
|
|
|
const SharedDiagonal noisePrior = noiseModel::Isotropic::Sigma(6, 0.10);
|
|
const SharedDiagonal noisePriorTranslation = noiseModel::Isotropic::Sigma(3, 0.10);
|
|
Point3 positionPrior = gtsam::Point3(0,0,1);
|
|
|
|
NonlinearFactorGraph graph;
|
|
graph.push_back(smartFactor1);
|
|
graph.push_back(smartFactor2);
|
|
graph.push_back(smartFactor3);
|
|
graph.push_back(PriorFactor<Pose3>(x1, pose1, noisePrior));
|
|
graph.push_back(PoseTranslationPrior<Pose3>(x2, positionPrior, noisePriorTranslation));
|
|
graph.push_back(PoseTranslationPrior<Pose3>(x3, positionPrior, noisePriorTranslation));
|
|
graph.print();
|
|
// Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/10, 0., -M_PI/10), gtsam::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), gtsam::Point3(0.1,0.1,0.1)); // smaller noise
|
|
Values values;
|
|
values.insert(x1, pose1);
|
|
values.insert(x2, pose2);
|
|
// initialize third pose with some noise, we expect it to move back to original pose3
|
|
values.insert(x3, pose3*noise_pose);
|
|
values.at<Pose3>(x3).print("Smart: Pose3 before optimization: ");
|
|
|
|
LevenbergMarquardtParams params;
|
|
params.verbosityLM = LevenbergMarquardtParams::TRYDELTA;
|
|
params.verbosity = NonlinearOptimizerParams::ERROR;
|
|
|
|
Values result;
|
|
gttic_(SmartProjectionFactor);
|
|
LevenbergMarquardtOptimizer optimizer(graph, values, params);
|
|
result = optimizer.optimize();
|
|
gttoc_(SmartProjectionFactor);
|
|
tictoc_finishedIteration_();
|
|
|
|
// result.print("results of 3 camera, 3 landmark optimization \n");
|
|
result.at<Pose3>(x3).print("Smart: Pose3 after optimization: ");
|
|
EXPECT(assert_equal(pose3,result.at<Pose3>(x3)));
|
|
tictoc_print_();
|
|
|
|
}
|
|
|
|
|
|
/* ************************************************************************* */
|
|
TEST( SmartProjectionFactor, Hessian ){
|
|
cout << " ************************ SmartProjectionFactor: Hessian **********************" << endl;
|
|
|
|
Symbol x1('X', 1);
|
|
Symbol x2('X', 2);
|
|
|
|
const SharedDiagonal noiseProjection = noiseModel::Isotropic::Sigma(2, 1);
|
|
|
|
std::vector<Key> views;
|
|
views += x1, x2;
|
|
|
|
Cal3_S2::shared_ptr K(new Cal3_S2(1500, 1200, 0, 640, 480));
|
|
// create first camera. Looking along X-axis, 1 meter above ground plane (x-y)
|
|
Pose3 pose1 = Pose3(Rot3::ypr(-M_PI/2, 0., -M_PI/2), gtsam::Point3(0,0,1));
|
|
SimpleCamera cam1(pose1, *K);
|
|
|
|
// create second camera 1 meter to the right of first camera
|
|
Pose3 pose2 = pose1 * Pose3(Rot3(), Point3(1,0,0));
|
|
SimpleCamera cam2(pose2, *K);
|
|
|
|
// three landmarks ~5 meters infront of camera
|
|
Point3 landmark1(5, 0.5, 1.2);
|
|
|
|
// 1. Project three landmarks into three cameras and triangulate
|
|
Point2 cam1_uv1 = cam1.project(landmark1);
|
|
Point2 cam2_uv1 = cam2.project(landmark1);
|
|
vector<Point2> measurements_cam1;
|
|
measurements_cam1 += cam1_uv1, cam2_uv1;
|
|
|
|
SmartProjectionFactor<Pose3, Point3, Cal3_S2> smartFactor(measurements_cam1, noiseProjection, views, K);
|
|
|
|
Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/10, 0., -M_PI/10), gtsam::Point3(0.5,0.1,0.3));
|
|
Values values;
|
|
values.insert(x1, pose1);
|
|
values.insert(x2, pose2);
|
|
// values.insert(L(1), landmark1);
|
|
|
|
boost::shared_ptr<GaussianFactor> hessianFactor = smartFactor.linearize(values);
|
|
hessianFactor->print("Hessian factor \n");
|
|
|
|
// compute triangulation from linearization point
|
|
// compute reprojection errors (sum squared)
|
|
// compare with hessianFactor.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]
|
|
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
|
|
/* ************************************************************************* */
|
|
|