309 lines
11 KiB
C++
309 lines
11 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file testProjectionFactor.cpp
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* @brief Unit tests for ProjectionFactor Class
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* @author Frank Dellaert
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* @date Nov 2009
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*/
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#include <gtsam_unstable/nonlinear/ConcurrentBatchFilter.h>
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <gtsam/slam/ProjectionFactor.h>
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#include <gtsam_unstable/slam/SmartProjectionFactor.h>
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#include <gtsam/nonlinear/ISAM2.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/LinearContainerFactor.h>
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#include <gtsam/nonlinear/Ordering.h>
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#include <gtsam/nonlinear/Values.h>
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#include <gtsam/nonlinear/Symbol.h>
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#include <gtsam/nonlinear/Key.h>
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#include <gtsam/linear/GaussianSequentialSolver.h>
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#include <gtsam/inference/JunctionTree.h>
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#include <gtsam_unstable/geometry/triangulation.h>
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#include <gtsam/geometry/Pose3.h>
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#include <gtsam/geometry/Point3.h>
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#include <gtsam/geometry/Point2.h>
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#include <gtsam/geometry/Cal3DS2.h>
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#include <gtsam/geometry/Cal3_S2.h>
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#include <gtsam/geometry/SimpleCamera.h>
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#include <CppUnitLite/TestHarness.h>
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using namespace std;
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using namespace gtsam;
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// make a realistic calibration matrix
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static double fov = 60; // degrees
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static size_t w=640,h=480;
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static Cal3_S2::shared_ptr K(new Cal3_S2(fov,w,h));
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// Create a noise model for the pixel error
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static SharedNoiseModel model(noiseModel::Unit::Create(2));
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// Convenience for named keys
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//using symbol_shorthand::X;
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//using symbol_shorthand::L;
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//typedef GenericProjectionFactor<Pose3, Point3> TestProjectionFactor;
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///* ************************************************************************* */
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TEST( MultiProjectionFactor, noiseless ){
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cout << " ************************ MultiProjectionFactor: noiseless ****************************" << endl;
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Values theta;
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NonlinearFactorGraph graph;
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Symbol x1('X', 1);
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Symbol x2('X', 2);
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// Symbol x3('X', 3);
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const SharedDiagonal noiseProjection = noiseModel::Isotropic::Sigma(2, 1);
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std::vector<Key> views;
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views += x1, x2; //, x3;
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Cal3_S2::shared_ptr K(new Cal3_S2(1500, 1200, 0, 640, 480));
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// create first camera. Looking along X-axis, 1 meter above ground plane (x-y)
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Pose3 level_pose = Pose3(Rot3::ypr(-M_PI/2, 0., -M_PI/2), gtsam::Point3(0,0,1));
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SimpleCamera level_camera(level_pose, *K);
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// create second camera 1 meter to the right of first camera
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Pose3 level_pose_right = level_pose * Pose3(Rot3(), Point3(1,0,0));
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SimpleCamera level_camera_right(level_pose_right, *K);
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// landmark ~5 meters infront of camera
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Point3 landmark(5, 0.5, 1.2);
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// 1. Project two landmarks into two cameras and triangulate
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Point2 level_uv = level_camera.project(landmark);
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Point2 level_uv_right = level_camera_right.project(landmark);
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Values value;
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value.insert(x1, level_pose);
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value.insert(x2, level_pose_right);
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// poses += level_pose, level_pose_right;
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vector<Point2> measurements;
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measurements += level_uv, level_uv_right;
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SmartProjectionFactor<Pose3, Point3, Cal3_S2> smartFactor(measurements, noiseProjection, views, K);
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double actualError = smartFactor.error(value);
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double expectedError = 0.0;
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DOUBLES_EQUAL(expectedError, actualError, 1e-7);
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}
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///* ************************************************************************* */
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TEST( MultiProjectionFactor, noisy ){
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cout << " ************************ MultiProjectionFactor: noisy ****************************" << endl;
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Values theta;
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NonlinearFactorGraph graph;
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Symbol x1('X', 1);
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Symbol x2('X', 2);
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// Symbol x3('X', 3);
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const SharedDiagonal noiseProjection = noiseModel::Isotropic::Sigma(2, 1);
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std::vector<Key> views;
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views += x1, x2; //, x3;
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Cal3_S2::shared_ptr K(new Cal3_S2(1500, 1200, 0, 640, 480));
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// create first camera. Looking along X-axis, 1 meter above ground plane (x-y)
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Pose3 level_pose = Pose3(Rot3::ypr(-M_PI/2, 0., -M_PI/2), gtsam::Point3(0,0,1));
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SimpleCamera level_camera(level_pose, *K);
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// create second camera 1 meter to the right of first camera
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Pose3 level_pose_right = level_pose * Pose3(Rot3(), Point3(1,0,0));
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SimpleCamera level_camera_right(level_pose_right, *K);
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// landmark ~5 meters infront of camera
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Point3 landmark(5, 0.5, 1.2);
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// 1. Project two landmarks into two cameras and triangulate
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Point2 pixelError(0.2,0.2);
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Point2 level_uv = level_camera.project(landmark) + pixelError;
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Point2 level_uv_right = level_camera_right.project(landmark);
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Values value;
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value.insert(x1, level_pose);
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value.insert(x2, level_pose_right);
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// poses += level_pose, level_pose_right;
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vector<Point2> measurements;
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measurements += level_uv, level_uv_right;
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SmartProjectionFactor<Pose3, Point3, Cal3_S2> smartFactor(measurements, noiseProjection, views, K);
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double actualError = smartFactor.error(value);
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double expectedError = sqrt(0.08);
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// we do not expect to be able to predict the error, since the error on the pixel will change
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// the triangulation of the landmark which is internal to the factor.
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// DOUBLES_EQUAL(expectedError, actualError, 1e-7);
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}
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///* ************************************************************************* */
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//TEST( ProjectionFactor, nonStandard ) {
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// GenericProjectionFactor<Pose3, Point3, Cal3DS2> f;
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//}
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//
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///* ************************************************************************* */
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//TEST( ProjectionFactor, Constructor) {
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// Key poseKey(X(1));
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// Key pointKey(L(1));
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//
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// Point2 measurement(323.0, 240.0);
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//
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// TestProjectionFactor factor(measurement, model, poseKey, pointKey, K);
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//}
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//
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///* ************************************************************************* */
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//TEST( ProjectionFactor, ConstructorWithTransform) {
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// Key poseKey(X(1));
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// Key pointKey(L(1));
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//
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// Point2 measurement(323.0, 240.0);
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// Pose3 body_P_sensor(Rot3::RzRyRx(-M_PI_2, 0.0, -M_PI_2), Point3(0.25, -0.10, 1.0));
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//
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// TestProjectionFactor factor(measurement, model, poseKey, pointKey, K, body_P_sensor);
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//}
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//
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///* ************************************************************************* */
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//TEST( ProjectionFactor, Equals ) {
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// // Create two identical factors and make sure they're equal
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// Point2 measurement(323.0, 240.0);
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//
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// TestProjectionFactor factor1(measurement, model, X(1), L(1), K);
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// TestProjectionFactor factor2(measurement, model, X(1), L(1), K);
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//
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// CHECK(assert_equal(factor1, factor2));
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//}
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//
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///* ************************************************************************* */
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//TEST( ProjectionFactor, EqualsWithTransform ) {
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// // Create two identical factors and make sure they're equal
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// Point2 measurement(323.0, 240.0);
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// Pose3 body_P_sensor(Rot3::RzRyRx(-M_PI_2, 0.0, -M_PI_2), Point3(0.25, -0.10, 1.0));
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//
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// TestProjectionFactor factor1(measurement, model, X(1), L(1), K, body_P_sensor);
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// TestProjectionFactor factor2(measurement, model, X(1), L(1), K, body_P_sensor);
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//
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// CHECK(assert_equal(factor1, factor2));
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//}
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//
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///* ************************************************************************* */
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//TEST( ProjectionFactor, Error ) {
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// // Create the factor with a measurement that is 3 pixels off in x
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// Key poseKey(X(1));
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// Key pointKey(L(1));
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// Point2 measurement(323.0, 240.0);
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// TestProjectionFactor factor(measurement, model, poseKey, pointKey, K);
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//
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// // Set the linearization point
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// Pose3 pose(Rot3(), Point3(0,0,-6));
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// Point3 point(0.0, 0.0, 0.0);
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//
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// // Use the factor to calculate the error
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// Vector actualError(factor.evaluateError(pose, point));
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//
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// // The expected error is (-3.0, 0.0) pixels / UnitCovariance
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// Vector expectedError = Vector_(2, -3.0, 0.0);
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//
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// // Verify we get the expected error
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// CHECK(assert_equal(expectedError, actualError, 1e-9));
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//}
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//
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///* ************************************************************************* */
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//TEST( ProjectionFactor, ErrorWithTransform ) {
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// // Create the factor with a measurement that is 3 pixels off in x
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// Key poseKey(X(1));
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// Key pointKey(L(1));
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// Point2 measurement(323.0, 240.0);
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// Pose3 body_P_sensor(Rot3::RzRyRx(-M_PI_2, 0.0, -M_PI_2), Point3(0.25, -0.10, 1.0));
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// TestProjectionFactor factor(measurement, model, poseKey, pointKey, K, body_P_sensor);
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//
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// // Set the linearization point. The vehicle pose has been selected to put the camera at (-6, 0, 0)
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// Pose3 pose(Rot3(), Point3(-6.25, 0.10 , -1.0));
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// Point3 point(0.0, 0.0, 0.0);
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//
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// // Use the factor to calculate the error
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// Vector actualError(factor.evaluateError(pose, point));
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//
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// // The expected error is (-3.0, 0.0) pixels / UnitCovariance
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// Vector expectedError = Vector_(2, -3.0, 0.0);
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//
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// // Verify we get the expected error
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// CHECK(assert_equal(expectedError, actualError, 1e-9));
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//}
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//
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///* ************************************************************************* */
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//TEST( ProjectionFactor, Jacobian ) {
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// // Create the factor with a measurement that is 3 pixels off in x
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// Key poseKey(X(1));
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// Key pointKey(L(1));
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// Point2 measurement(323.0, 240.0);
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// TestProjectionFactor factor(measurement, model, poseKey, pointKey, K);
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//
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// // Set the linearization point
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// Pose3 pose(Rot3(), Point3(0,0,-6));
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// Point3 point(0.0, 0.0, 0.0);
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//
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// // Use the factor to calculate the Jacobians
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// Matrix H1Actual, H2Actual;
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// factor.evaluateError(pose, point, H1Actual, H2Actual);
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//
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// // The expected Jacobians
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// Matrix H1Expected = Matrix_(2, 6, 0., -554.256, 0., -92.376, 0., 0., 554.256, 0., 0., 0., -92.376, 0.);
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// Matrix H2Expected = Matrix_(2, 3, 92.376, 0., 0., 0., 92.376, 0.);
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//
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// // Verify the Jacobians are correct
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// CHECK(assert_equal(H1Expected, H1Actual, 1e-3));
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// CHECK(assert_equal(H2Expected, H2Actual, 1e-3));
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//}
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//
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///* ************************************************************************* */
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//TEST( ProjectionFactor, JacobianWithTransform ) {
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// // Create the factor with a measurement that is 3 pixels off in x
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// Key poseKey(X(1));
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// Key pointKey(L(1));
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// Point2 measurement(323.0, 240.0);
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// Pose3 body_P_sensor(Rot3::RzRyRx(-M_PI_2, 0.0, -M_PI_2), Point3(0.25, -0.10, 1.0));
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// TestProjectionFactor factor(measurement, model, poseKey, pointKey, K, body_P_sensor);
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//
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// // Set the linearization point. The vehicle pose has been selected to put the camera at (-6, 0, 0)
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// Pose3 pose(Rot3(), Point3(-6.25, 0.10 , -1.0));
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// Point3 point(0.0, 0.0, 0.0);
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//
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// // Use the factor to calculate the Jacobians
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// Matrix H1Actual, H2Actual;
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// factor.evaluateError(pose, point, H1Actual, H2Actual);
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//
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// // The expected Jacobians
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// Matrix H1Expected = Matrix_(2, 6, -92.376, 0., 577.350, 0., 92.376, 0., -9.2376, -577.350, 0., 0., 0., 92.376);
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// Matrix H2Expected = Matrix_(2, 3, 0., -92.376, 0., 0., 0., -92.376);
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//
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// // Verify the Jacobians are correct
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// CHECK(assert_equal(H1Expected, H1Actual, 1e-3));
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// CHECK(assert_equal(H2Expected, H2Actual, 1e-3));
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//}
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
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/* ************************************************************************* */
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