392 PointCloudSource& output,
const Eigen::Matrix4f& guess)
394 PointCloudSource intm_cloud = output;
399 if (guess != Eigen::Matrix4f::Identity()) {
400 transformation_ = guess;
409 Eigen::Matrix4f& transformation = transformation_;
413 const Eigen::Matrix3f initial_rot(transformation.block<3, 3>(0, 0));
414 const Eigen::Vector3f rot_x(initial_rot * Eigen::Vector3f::UnitX());
415 const double z_rotation = std::atan2(rot_x[1], rot_x[0]);
417 Eigen::Vector3d xytheta_transformation(
418 transformation(0, 3), transformation(1, 3), z_rotation);
420 while (!converged_) {
421 const double cos_theta = std::cos(xytheta_transformation[2]);
422 const double sin_theta = std::sin(xytheta_transformation[2]);
423 previous_transformation_ = transformation;
427 for (std::size_t i = 0; i < intm_cloud.size(); i++)
428 score += target_ndt.
test(intm_cloud[i], cos_theta, sin_theta);
430 PCL_DEBUG(
"[pcl::NormalDistributionsTransform2D::computeTransformation] NDT score "
431 "%f (x=%f,y=%f,r=%f)\n",
433 xytheta_transformation[0],
434 xytheta_transformation[1],
435 xytheta_transformation[2]);
437 if (score.
value != 0) {
439 Eigen::EigenSolver<Eigen::Matrix3d> solver;
440 solver.compute(score.
hessian,
false);
441 double min_eigenvalue = 0;
442 for (
int i = 0; i < 3; i++)
443 if (solver.eigenvalues()[i].real() < min_eigenvalue)
444 min_eigenvalue = solver.eigenvalues()[i].real();
448 if (min_eigenvalue < 0) {
449 double lambda = 1.1 * min_eigenvalue - 1;
450 score.
hessian += Eigen::Vector3d(-lambda, -lambda, -lambda).asDiagonal();
451 solver.compute(score.
hessian,
false);
452 PCL_DEBUG(
"[pcl::NormalDistributionsTransform2D::computeTransformation] adjust "
453 "hessian: %f: new eigenvalues:%f %f %f\n",
455 solver.eigenvalues()[0].real(),
456 solver.eigenvalues()[1].real(),
457 solver.eigenvalues()[2].real());
459 assert(solver.eigenvalues()[0].real() >= 0 &&
460 solver.eigenvalues()[1].real() >= 0 &&
461 solver.eigenvalues()[2].real() >= 0);
463 Eigen::Vector3d delta_transformation(-score.
hessian.inverse() * score.
grad);
464 Eigen::Vector3d new_transformation =
465 xytheta_transformation + newton_lambda_.cwiseProduct(delta_transformation);
467 xytheta_transformation = new_transformation;
470 transformation.block<3, 3>(0, 0).matrix() = Eigen::Matrix3f(Eigen::AngleAxisf(
471 static_cast<float>(xytheta_transformation[2]), Eigen::Vector3f::UnitZ()));
472 transformation.block<3, 1>(0, 3).matrix() =
473 Eigen::Vector3f(
static_cast<float>(xytheta_transformation[0]),
474 static_cast<float>(xytheta_transformation[1]),
480 PCL_ERROR(
"[pcl::NormalDistributionsTransform2D::computeTransformation] no "
481 "overlap: try increasing the size or reducing the step of the grid\n");
489 if (update_visualizer_)
490 update_visualizer_(output, *indices_, *target_, *indices_);
495 Eigen::Matrix4f transformation_delta =
496 transformation.inverse() * previous_transformation_;
498 0.5 * (transformation_delta.coeff(0, 0) + transformation_delta.coeff(1, 1) +
499 transformation_delta.coeff(2, 2) - 1);
500 double translation_sqr =
501 transformation_delta.coeff(0, 3) * transformation_delta.coeff(0, 3) +
502 transformation_delta.coeff(1, 3) * transformation_delta.coeff(1, 3) +
503 transformation_delta.coeff(2, 3) * transformation_delta.coeff(2, 3);
505 if (nr_iterations_ >= max_iterations_ ||
506 ((transformation_epsilon_ > 0 && translation_sqr <= transformation_epsilon_) &&
507 (transformation_rotation_epsilon_ > 0 &&
508 cos_angle >= transformation_rotation_epsilon_)) ||
509 ((transformation_epsilon_ <= 0) &&
510 (transformation_rotation_epsilon_ > 0 &&
511 cos_angle >= transformation_rotation_epsilon_)) ||
512 ((transformation_epsilon_ > 0 && translation_sqr <= transformation_epsilon_) &&
513 (transformation_rotation_epsilon_ <= 0))) {
517 final_transformation_ = transformation;