431 lines
13 KiB
C++
431 lines
13 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 testSimilarity3.cpp
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* @brief Unit tests for Similarity3 class
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*/
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#include <gtsam/geometry/Rot3.h>
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#include <gtsam/geometry/Point3.h>
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#include <gtsam/base/Manifold.h>
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namespace gtsam {
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/**
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* 3D similarity transform
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*/
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class Similarity3: public LieGroup<Similarity3, 7> {
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/** Pose Concept requirements */
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typedef Rot3 Rotation;
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typedef Point3 Translation;
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private:
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Rot3 R_;
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Point3 t_;
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double s_;
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public:
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/// Construct from Eigen types
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Similarity3(const Matrix3& R, const Vector3& t, double s) {
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R_ = R;
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t_ = t;
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s_ = s;
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}
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/// Return the translation
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const Vector3 t() const {
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return t_.vector();
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}
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/// Return the rotation matrix
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const Matrix3 R() const {
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return R_.matrix();
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}
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public:
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Similarity3() :
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R_(), t_(), s_(1){
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}
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/// Construct pure scaling
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Similarity3(double s) {
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s_ = s;
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}
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/// Construct from GTSAM types
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Similarity3(const Rot3& R, const Point3& t, double s) {
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R_ = R;
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t_ = t;
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s_ = s;
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}
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static Similarity3 identity() {
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std::cout << "Identity!" << std::endl;
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return Similarity3(); }
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static Vector7 Logmap(const Similarity3& s, OptionalJacobian<7, 7> Hm = boost::none) {
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std::cout << "Logmap!" << std::endl;
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return Vector7();
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}
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static Similarity3 Expmap(const Vector7& v, OptionalJacobian<7, 7> Hm = boost::none) {
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std::cout << "Expmap!" << std::endl;
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return Similarity3();
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}
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bool operator==(const Similarity3& other) const {
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return (R_.equals(other.R_)) && (t_ == other.t_) && (s_ == other.s_);
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}
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/// Compare with tolerance
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bool equals(const Similarity3& sim, double tol) const {
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return rotation().equals(sim.rotation(), tol) && translation().equals(sim.translation(), tol)
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&& scale() < (sim.scale()+tol) && scale() > (sim.scale()-tol);
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}
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Point3 transform_from(const Point3& p) const {
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/*if (Dpose) {
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const Matrix3 R = R_.matrix();
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Matrix3 DR = R * skewSymmetric(-p.x(), -p.y(), -p.z());
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(*Dpose) << DR, R;
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}
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if (Dpoint)
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*Dpoint = R_.matrix();*/
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return R_ * (s_ * p) + t_;
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}
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Matrix7 AdjointMap() const{
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const Matrix3 R = R_.matrix();
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const Vector3 t = t_.vector();
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Matrix3 A = s_ * skewSymmetric(t) * R;
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Matrix7 adj;
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adj << s_*R, A, -s_*t, Z_3x3, R, Eigen::Matrix<double, 3, 1>::Zero(), Eigen::Matrix<double, 1, 6>::Zero(), 1;
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return adj;
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}
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/** syntactic sugar for transform_from */
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inline Point3 operator*(const Point3& p) const {
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return transform_from(p);
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}
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Similarity3 inverse() const {
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Rot3 Rt = R_.inverse();
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Point3 sRt = R_.inverse() * (-s_ * t_);
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return Similarity3(Rt, sRt, 1.0/s_);
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}
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Similarity3 operator*(const Similarity3& T) const {
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return Similarity3(R_ * T.R_, ((1.0/T.s_)*t_) + R_ * T.t_, s_*T.s_);
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}
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void print(const std::string& s) const {
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std::cout << std::endl;
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std::cout << s;
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rotation().print("R:\n");
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translation().print("t: ");
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std::cout << "s: " << scale() << std::endl;
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}
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/// @name Manifold
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/// @{
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/// Dimensionality of tangent space = 7 DOF - used to autodetect sizes
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inline static size_t Dim() {
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return 7;
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}
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/// Dimensionality of tangent space = 7 DOF
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inline size_t dim() const {
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return 7;
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}
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/// Return the rotation matrix
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Rot3 rotation() const {
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return R_;
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}
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/// Return the translation
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Point3 translation() const {
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return t_;
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}
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/// Return the scale
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double scale() const {
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return s_;
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}
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/// Update Similarity transform via 7-dim vector in tangent space
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struct ChartAtOrigin {
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static Similarity3 Retract(const Vector7& v, ChartJacobian H = boost::none) {
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// Will retracting or localCoordinating R work if R is not a unit rotation?
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// Also, how do we actually get s out? Seems like we need to store it somewhere.
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Rot3 r; //Create a zero rotation to do our retraction.
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return Similarity3( //
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r.retract(v.head<3>()), // retract rotation using v[0,1,2]
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Point3(v.segment<3>(3)), // Retract the translation
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1.0 + v[6]); //finally, update scale using v[6]
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}
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/// 7-dimensional vector v in tangent space that makes other = this->retract(v)
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static Vector7 Local(const Similarity3& other, ChartJacobian H = boost::none) {
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Rot3 r; //Create a zero rotation to do the retraction
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Vector7 v;
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v.head<3>() = r.localCoordinates(other.R_);
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v.segment<3>(3) = other.t_.vector();
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//v.segment<3>(3) = translation().localCoordinates(other.translation());
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v[6] = other.s_ - 1.0;
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return v;
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}
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};
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using LieGroup<Similarity3, 7>::inverse; // version with derivative
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/// @}
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/// @name Lie Group
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/// @{
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// compose T1*T2
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// between T2*inverse(T1)
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// identity I4
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// inverse inverse(T)
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/// @}
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};
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template<>
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struct traits<Similarity3> : public internal::LieGroupTraits<Similarity3> {};
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}
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#include <gtsam/inference/Symbol.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/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/Values.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/base/Testable.h>
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#include <CppUnitLite/TestHarness.h>
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using namespace gtsam;
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using namespace std;
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using symbol_shorthand::X;
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GTSAM_CONCEPT_TESTABLE_INST(Similarity3)
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static Point3 P(0.2,0.7,-2);
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static Rot3 R = Rot3::rodriguez(0.3,0,0);
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static Similarity3 T(R,Point3(3.5,-8.2,4.2),1);
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static Similarity3 T2(Rot3::rodriguez(0.3,0.2,0.1),Point3(3.5,-8.2,4.2),1);
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static Similarity3 T3(Rot3::rodriguez(-90, 0, 0), Point3(1, 2, 3), 1);
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//******************************************************************************
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TEST(Similarity3, Constructors) {
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Similarity3 test;
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}
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//******************************************************************************
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TEST(Similarity3, Getters) {
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Similarity3 test;
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EXPECT(assert_equal(Rot3(), test.rotation()));
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EXPECT(assert_equal(Point3(), test.translation()));
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EXPECT_DOUBLES_EQUAL(1.0, test.scale(), 1e-9);
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}
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//******************************************************************************
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TEST(Similarity3, Getters2) {
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Similarity3 test(Rot3::ypr(1, 2, 3), Point3(4, 5, 6), 7);
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EXPECT(assert_equal(Rot3::ypr(1, 2, 3), test.rotation()));
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EXPECT(assert_equal(Point3(4, 5, 6), test.translation()));
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EXPECT_DOUBLES_EQUAL(7.0, test.scale(), 1e-9);
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}
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TEST(Similarity3, AdjointMap) {
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Similarity3 test(Rot3::ypr(1,2,3).inverse(), Point3(4,5,6), 7);
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Matrix7 result;
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result << -1.5739, -2.4512, -6.3651, -50.7671, -11.2503, 16.8859, -28.0000,
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6.3167, -2.9884, -0.4111, 0.8502, 8.6373, -49.7260, -35.0000,
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-2.5734, -5.8362, 2.8839, 33.1363, 0.3024, 30.1811, -42.0000,
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0, 0, 0, -0.2248, -0.3502, -0.9093, 0,
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0, 0, 0, 0.9024, -0.4269, -0.0587, 0,
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0, 0, 0, -0.3676, -0.8337, 0.4120, 0,
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0, 0, 0, 0, 0, 0, 1.0000;
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EXPECT(assert_equal(result, test.AdjointMap(), 1e-3));
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}
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TEST(Similarity3, inverse) {
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Similarity3 test(Rot3::ypr(1,2,3).inverse(), Point3(4,5,6), 7);
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Matrix3 Re;
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Re << -0.2248, 0.9024, -0.3676,
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-0.3502, -0.4269, -0.8337,
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-0.9093, -0.0587, 0.4120;
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Vector3 te(-9.8472, 59.7640, 10.2125);
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Similarity3 expected(Re, te, 1.0/7.0);
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EXPECT(assert_equal(expected, test.inverse(), 1e-3));
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}
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TEST(Similarity3, multiplication) {
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Similarity3 test1(Rot3::ypr(1,2,3).inverse(), Point3(4,5,6), 7);
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Similarity3 test2(Rot3::ypr(1,2,3).inverse(), Point3(8,9,10), 11);
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Matrix3 re;
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re << 0.0688, 0.9863, -0.1496,
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-0.5665, -0.0848, -0.8197,
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-0.8211, 0.1412, 0.5530;
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Vector3 te(-13.6797, 3.2441, -5.7794);
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Similarity3 expected(re, te, 77);
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EXPECT(assert_equal(expected, test1*test2, 1e-2));
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}
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//******************************************************************************
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TEST(Similarity3, Manifold) {
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EXPECT_LONGS_EQUAL(7, Similarity3::Dim());
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Vector z = Vector7::Zero();
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Similarity3 sim;
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EXPECT(sim.retract(z) == sim);
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Vector7 v = Vector7::Zero();
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v(6) = 2;
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Similarity3 sim2;
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EXPECT(sim2.retract(z) == sim2);
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EXPECT(assert_equal(z, sim2.localCoordinates(sim)));
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Similarity3 sim3 = Similarity3(Rot3(), Point3(1, 2, 3), 1);
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Vector v3(7);
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v3 << 0, 0, 0, 1, 2, 3, 0;
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EXPECT(assert_equal(v3, sim2.localCoordinates(sim3)));
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// Similarity3 other = Similarity3(Rot3::ypr(0.01, 0.02, 0.03), Point3(0.4, 0.5, 0.6), 1);
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Similarity3 other = Similarity3(Rot3::ypr(0.1, 0.2, 0.3),Point3(4,5,6),1);
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Vector vlocal = sim.localCoordinates(other);
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EXPECT(assert_equal(sim.retract(vlocal), other, 1e-2));
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Similarity3 other2 = Similarity3(Rot3::ypr(0.3, 0, 0),Point3(4,5,6),1);
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Rot3 R = Rot3::rodriguez(0.3,0,0);
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Vector vlocal2 = sim.localCoordinates(other2);
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EXPECT(assert_equal(sim.retract(vlocal2), other2, 1e-2));
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// TODO add unit tests for retract and localCoordinates
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}
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/* ************************************************************************* */
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TEST( Similarity3, retract_first_order)
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{
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Similarity3 id;
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Vector v = zero(7);
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v(0) = 0.3;
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EXPECT(assert_equal(Similarity3(R, Point3(), 1), id.retract(v),1e-2));
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v(3)=0.2;v(4)=0.7;v(5)=-2;
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EXPECT(assert_equal(Similarity3(R, P, 1),id.retract(v),1e-2));
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}
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/* ************************************************************************* */
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TEST(Similarity3, localCoordinates_first_order)
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{
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Vector d12 = repeat(7,0.1);
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d12(6) = 1.0;
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Similarity3 t1 = T, t2 = t1.retract(d12);
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EXPECT(assert_equal(d12, t1.localCoordinates(t2)));
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}
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/* ************************************************************************* */
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TEST(Similarity3, manifold_first_order)
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{
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Similarity3 t1 = T;
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Similarity3 t2 = T3;
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Similarity3 origin;
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Vector d12 = t1.localCoordinates(t2);
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EXPECT(assert_equal(t2, t1.retract(d12)));
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Vector d21 = t2.localCoordinates(t1);
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EXPECT(assert_equal(t1, t2.retract(d21)));
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}
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TEST(Similarity3, Optimization) {
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Similarity3 prior = Similarity3(Rot3::ypr(0.1, 0.2, 0.3), Point3(1, 2, 3), 4);
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//prior.print("Goal Transform");
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noiseModel::Isotropic::shared_ptr model = noiseModel::Isotropic::Sigma(7, 1);
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Symbol key('x',1);
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PriorFactor<Similarity3> factor(key, prior, model);
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NonlinearFactorGraph graph;
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graph.push_back(factor);
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//graph.print("Full Graph");
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Values initial;
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initial.insert<Similarity3>(key, Similarity3());
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//initial.print("Initial Estimate");
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Values result;
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LevenbergMarquardtParams params;
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params.setVerbosityLM("TRYCONFIG");
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result = LevenbergMarquardtOptimizer(graph, initial).optimize();
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//result.print("Optimized Estimate");
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EXPECT(assert_equal(prior, result.at<Similarity3>(key), 1e-4));
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}
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TEST(Similarity3, Optimization2) {
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Similarity3 prior = Similarity3();//Rot3::ypr(0, 0, 0), Point3(1, 2, 3), 4);
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Similarity3 m1 = Similarity3(Rot3::ypr(M_PI/2.0, 0, 0), Point3(1.0, 0, 0), 1.0);
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Similarity3 m2 = Similarity3(Rot3::ypr(M_PI/2.0, 0, 0), Point3(1.0, 0, 0), 1.0);
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Similarity3 m3 = Similarity3(Rot3::ypr(M_PI/2.0, 0, 0), Point3(0.9, 0, 0), 1.0);
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Similarity3 m4 = Similarity3(Rot3::ypr(M_PI/2.0, 0, 0), Point3(0.9, 0, 0), 1.0);
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//prior.print("Goal Transform");
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noiseModel::Isotropic::shared_ptr model = noiseModel::Isotropic::Sigma(7, 1);
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SharedDiagonal betweenNoise = noiseModel::Diagonal::Sigmas(
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(Vector(7) << 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.010).finished());
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PriorFactor<Similarity3> factor(X(1), prior, model);
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BetweenFactor<Similarity3> b1(X(1), X(2), m1, betweenNoise);
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BetweenFactor<Similarity3> b2(X(2), X(3), m2, betweenNoise);
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BetweenFactor<Similarity3> b3(X(3), X(4), m3, betweenNoise);
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BetweenFactor<Similarity3> b4(X(4), X(1), m4, betweenNoise);
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NonlinearFactorGraph graph;
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graph.push_back(factor);
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graph.push_back(b1);
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graph.push_back(b2);
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graph.push_back(b3);
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graph.push_back(b4);
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graph.print("Full Graph");
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Values initial;
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initial.insert<Similarity3>(X(1), Similarity3());
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initial.insert<Similarity3>(X(2), Similarity3(Rot3::ypr(M_PI/2.0, 0, 0), Point3(1, 0, 0), 1.0));
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initial.insert<Similarity3>(X(3), Similarity3(Rot3::ypr(2.0*M_PI/2.0, 0, 0), Point3(1, 1.5, 0), 1.0));
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initial.insert<Similarity3>(X(4), Similarity3(Rot3::ypr(3.0*M_PI/2.0, 0, 0), Point3(0, 1, 0), 1.0));
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initial.print("Initial Estimate");
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Values result;
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result = LevenbergMarquardtOptimizer(graph, initial).optimize();
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result.print("Optimized Estimate");
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//EXPECT(assert_equal(prior, result.at<Similarity3>(key), 1e-4));
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}
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//******************************************************************************
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int main() {
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TestResult tr;
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return TestRegistry::runAllTests(tr);
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}
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//******************************************************************************
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