gtsam/gtsam/geometry/Similarity3.cpp

296 lines
9.5 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 Similarity3.cpp
* @brief Implementation of Similarity3 transform
* @author Paul Drews
* @author John Lambert
*/
#include <gtsam/geometry/Similarity3.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/base/Manifold.h>
#include <gtsam/slam/KarcherMeanFactor-inl.h>
namespace gtsam {
using std::vector;
namespace {
/// Subtract centroids from point pairs.
static Point3Pairs subtractCentroids(const Point3Pairs &abPointPairs,
const Point3Pair &centroids) {
Point3Pairs d_abPointPairs;
for (const Point3Pair& abPair : abPointPairs) {
Point3 da = abPair.first - centroids.first;
Point3 db = abPair.second - centroids.second;
d_abPointPairs.emplace_back(da, db);
}
return d_abPointPairs;
}
/// Form inner products x and y and calculate scale.
static double calculateScale(const Point3Pairs &d_abPointPairs,
const Rot3 &aRb) {
double x = 0, y = 0;
Point3 da, db;
for (const Point3Pair& d_abPair : d_abPointPairs) {
std::tie(da, db) = d_abPair;
const Vector3 da_prime = aRb * db;
y += da.transpose() * da_prime;
x += da_prime.transpose() * da_prime;
}
const double s = y / x;
return s;
}
/// Form outer product H.
static Matrix3 calculateH(const Point3Pairs &d_abPointPairs) {
Matrix3 H = Z_3x3;
for (const Point3Pair& d_abPair : d_abPointPairs) {
H += d_abPair.first * d_abPair.second.transpose();
}
return H;
}
/// This method estimates the similarity transform from differences point pairs,
// given a known or estimated rotation and point centroids.
static Similarity3 align(const Point3Pairs &d_abPointPairs, const Rot3 &aRb,
const Point3Pair &centroids) {
const double s = calculateScale(d_abPointPairs, aRb);
// dividing aTb by s is required because the registration cost function
// minimizes ||a - sRb - t||, whereas Sim(3) computes s(Rb + t)
const Point3 aTb = (centroids.first - s * (aRb * centroids.second)) / s;
return Similarity3(aRb, aTb, s);
}
/// This method estimates the similarity transform from point pairs, given a known or estimated rotation.
// Refer to: http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2005/Zinsser05-PSR.pdf Chapter 3
static Similarity3 alignGivenR(const Point3Pairs &abPointPairs,
const Rot3 &aRb) {
auto centroids = means(abPointPairs);
auto d_abPointPairs = subtractCentroids(abPointPairs, centroids);
return align(d_abPointPairs, aRb, centroids);
}
} // namespace
Similarity3::Similarity3() :
t_(0,0,0), s_(1) {
}
Similarity3::Similarity3(double s) :
t_(0,0,0), s_(s) {
}
Similarity3::Similarity3(const Rot3& R, const Point3& t, double s) :
R_(R), t_(t), s_(s) {
}
Similarity3::Similarity3(const Matrix3& R, const Vector3& t, double s) :
R_(R), t_(t), s_(s) {
}
Similarity3::Similarity3(const Matrix4& T) :
R_(T.topLeftCorner<3, 3>()), t_(T.topRightCorner<3, 1>()), s_(1.0 / T(3, 3)) {
}
bool Similarity3::equals(const Similarity3& other, double tol) const {
return R_.equals(other.R_, tol) && traits<Point3>::Equals(t_, other.t_, tol)
&& s_ < (other.s_ + tol) && s_ > (other.s_ - tol);
}
bool Similarity3::operator==(const Similarity3& other) const {
return R_.matrix() == other.R_.matrix() && t_ == other.t_ && s_ == other.s_;
}
void Similarity3::print(const std::string& s) const {
std::cout << std::endl;
std::cout << s;
rotation().print("\nR:\n");
std::cout << "t: " << translation().transpose() << " s: " << scale() << std::endl;
}
Similarity3 Similarity3::identity() {
return Similarity3();
}
Similarity3 Similarity3::operator*(const Similarity3& S) const {
return Similarity3(R_ * S.R_, ((1.0 / S.s_) * t_) + R_ * S.t_, s_ * S.s_);
}
Similarity3 Similarity3::inverse() const {
const Rot3 Rt = R_.inverse();
const Point3 sRt = Rt * (-s_ * t_);
return Similarity3(Rt, sRt, 1.0 / s_);
}
Point3 Similarity3::transformFrom(const Point3& p, //
OptionalJacobian<3, 7> H1, OptionalJacobian<3, 3> H2) const {
const Point3 q = R_ * p + t_;
if (H1) {
// For this derivative, see LieGroups.pdf
const Matrix3 sR = s_ * R_.matrix();
const Matrix3 DR = sR * skewSymmetric(-p.x(), -p.y(), -p.z());
*H1 << DR, sR, sR * p;
}
if (H2)
*H2 = s_ * R_.matrix(); // just 3*3 sub-block of matrix()
return s_ * q;
}
Pose3 Similarity3::transformFrom(const Pose3& T) const {
Rot3 R = R_.compose(T.rotation());
Point3 t = Point3(s_ * (R_ * T.translation() + t_));
return Pose3(R, t);
}
Point3 Similarity3::operator*(const Point3& p) const {
return transformFrom(p);
}
Similarity3 Similarity3::Align(const Point3Pairs &abPointPairs) {
// Refer to Chapter 3 of
// http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2005/Zinsser05-PSR.pdf
if (abPointPairs.size() < 3)
throw std::runtime_error("input should have at least 3 pairs of points");
auto centroids = means(abPointPairs);
auto d_abPointPairs = subtractCentroids(abPointPairs, centroids);
Matrix3 H = calculateH(d_abPointPairs);
// ClosestTo finds rotation matrix closest to H in Frobenius sense
Rot3 aRb = Rot3::ClosestTo(H);
return align(d_abPointPairs, aRb, centroids);
}
Similarity3 Similarity3::Align(const vector<Pose3Pair> &abPosePairs) {
const size_t n = abPosePairs.size();
if (n < 2)
throw std::runtime_error("input should have at least 2 pairs of poses");
// calculate rotation
vector<Rot3> rotations;
Point3Pairs abPointPairs;
rotations.reserve(n);
abPointPairs.reserve(n);
// Below denotes the pose of the i'th object/camera/etc in frame "a" or frame "b"
Pose3 aTi, bTi;
for (const Pose3Pair &abPair : abPosePairs) {
std::tie(aTi, bTi) = abPair;
const Rot3 aRb = aTi.rotation().compose(bTi.rotation().inverse());
rotations.emplace_back(aRb);
abPointPairs.emplace_back(aTi.translation(), bTi.translation());
}
const Rot3 aRb_estimate = FindKarcherMean<Rot3>(rotations);
return alignGivenR(abPointPairs, aRb_estimate);
}
Matrix4 Similarity3::wedge(const Vector7 &xi) {
// http://www.ethaneade.org/latex2html/lie/node29.html
const auto w = xi.head<3>();
const auto u = xi.segment<3>(3);
const double lambda = xi[6];
Matrix4 W;
W << skewSymmetric(w), u, 0, 0, 0, -lambda;
return W;
}
Matrix7 Similarity3::AdjointMap() const {
// http://www.ethaneade.org/latex2html/lie/node30.html
const Matrix3 R = R_.matrix();
const Vector3 t = t_;
const Matrix3 A = s_ * skewSymmetric(t) * R;
Matrix7 adj;
adj << R, Z_3x3, Matrix31::Zero(), // 3*7
A, s_ * R, -s_ * t, // 3*7
Matrix16::Zero(), 1; // 1*7
return adj;
}
Matrix3 Similarity3::GetV(Vector3 w, double lambda) {
// http://www.ethaneade.org/latex2html/lie/node29.html
const double theta2 = w.transpose() * w;
double Y, Z, W;
if (theta2 > 1e-9) {
const double theta = sqrt(theta2);
const double X = sin(theta) / theta;
Y = (1 - cos(theta)) / theta2;
Z = (1 - X) / theta2;
W = (0.5 - Y) / theta2;
} else {
// Taylor series expansion for theta=0, X not needed (as is 1)
Y = 0.5 - theta2 / 24.0;
Z = 1.0 / 6.0 - theta2 / 120.0;
W = 1.0 / 24.0 - theta2 / 720.0;
}
const double lambda2 = lambda * lambda, lambda3 = lambda2 * lambda;
const double expMinLambda = exp(-lambda);
double A, alpha = 0.0, beta, mu;
if (lambda2 > 1e-9) {
A = (1.0 - expMinLambda) / lambda;
alpha = 1.0 / (1.0 + theta2 / lambda2);
beta = (expMinLambda - 1 + lambda) / lambda2;
mu = (1 - lambda + (0.5 * lambda2) - expMinLambda) / lambda3;
} else {
A = 1.0 - lambda / 2.0 + lambda2 / 6.0;
beta = 0.5 - lambda / 6.0 + lambda2 / 24.0 - lambda3 / 120.0;
mu = 1.0 / 6.0 - lambda / 24.0 + lambda2 / 120.0 - lambda3 / 720.0;
}
const double gamma = Y - (lambda * Z), upsilon = Z - (lambda * W);
const double B = alpha * (beta - gamma) + gamma;
const double C = alpha * (mu - upsilon) + upsilon;
const Matrix3 Wx = skewSymmetric(w[0], w[1], w[2]);
return A * I_3x3 + B * Wx + C * Wx * Wx;
}
Vector7 Similarity3::Logmap(const Similarity3& T, OptionalJacobian<7, 7> Hm) {
// To get the logmap, calculate w and lambda, then solve for u as shown by Ethan at
// www.ethaneade.org/latex2html/lie/node29.html
const Vector3 w = Rot3::Logmap(T.R_);
const double lambda = log(T.s_);
Vector7 result;
result << w, GetV(w, lambda).inverse() * T.t_, lambda;
if (Hm) {
throw std::runtime_error("Similarity3::Logmap: derivative not implemented");
}
return result;
}
Similarity3 Similarity3::Expmap(const Vector7& v, OptionalJacobian<7, 7> Hm) {
const auto w = v.head<3>();
const auto u = v.segment<3>(3);
const double lambda = v[6];
if (Hm) {
throw std::runtime_error("Similarity3::Expmap: derivative not implemented");
}
const Matrix3 V = GetV(w, lambda);
return Similarity3(Rot3::Expmap(w), Point3(V * u), exp(lambda));
}
std::ostream &operator<<(std::ostream &os, const Similarity3& p) {
os << "[" << p.rotation().xyz().transpose() << " "
<< p.translation().transpose() << " " << p.scale() << "]\';";
return os;
}
const Matrix4 Similarity3::matrix() const {
Matrix4 T;
T.topRows<3>() << R_.matrix(), t_;
T.bottomRows<1>() << 0, 0, 0, 1.0 / s_;
return T;
}
Similarity3::operator Pose3() const {
return Pose3(R_, s_ * t_);
}
} // namespace gtsam