话不多说,直接上代码
#include <pcl/registration/icp.h>
#include <pcl/point_types.h>
pc_xyz::Ptr result = boost::make_shared<pc_xyz>();
pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;
icp.setMaxCorrespondenceDistance(max_correspondence_dist_);// 设置corr_dist_threshold_
icp.setMaximumIterations(max_iterations_); //设置max_iterations_
icp.setTransformationEpsilon(1e-6); //设置transformation_epsilon_
icp.setEuclideanFitnessEpsilon(1e-2); // 设置euclidean_fitness_epsilon_
icp.setInputSource(local_map_xyz);//设置input_ ,source_cloud_updated_ = true;
icp.setInputTarget(global_map_xyz);//设置target_, target_cloud_updated_ = true;
if (do_ransac_outlier_rejection_) {
icp.setRANSACOutlierRejectionThreshold(ransac_outlier_rejection_threshold_);
icp.setRANSACIterations(ransac_iterations_);
}
icp.align(*result);//主要匹配函数
is_converged = icp.hasConverged();
fitness = icp.getFitnessScore();
correction = icp.getFinalTransformation();
第1小节中的
icp.align(*result);//主要匹配函数
对应的为pcl/registtation/impl/registation.hpp中的
template <typename PointSource, typename PointTarget, typename Scalar> inline void
pcl::Registration<PointSource, PointTarget, Scalar>::align (PointCloudSource &output)
{
align (output, Matrix4::Identity ());
}
其调用pcl::Registration<PointSource, PointTarget, Scalar>::align (PointCloudSource &output, const Matrix4& guess) 函数。
ICP的核心内容也就是该函数,接下来我们对其进行详细解释:
template <typename PointSource, typename PointTarget, typename Scalar> inline void
pcl::Registration<PointSource, PointTarget, Scalar>::align (PointCloudSource &output, const Matrix4& guess)
{
if (!initCompute ()) //初始化kd树,及点云索引
return;
// Resize the output dataset
if (output.points.size () != indices_->size ())
output.points.resize (indices_->size ());
// Copy the header
output.header = input_->header;
// Check if the output will be computed for all points or only a subset
if (indices_->size () != input_->points.size ())
{
output.width = static_cast<uint32_t> (indices_->size ());
output.height = 1;
}
else
{
output.width = static_cast<uint32_t> (input_->width);
output.height = input_->height;
}
output.is_dense = input_->is_dense;
// Copy the point data to output
for (size_t i = 0; i < indices_->size (); ++i)
output.points[i] = input_->points[(*indices_)[i]];//将原始点云复制到output中
// Set the internal point representation of choice unless otherwise noted
if (point_representation_ && !force_no_recompute_)
tree_->setPointRepresentation (point_representation_);
// Perform the actual transformation computation
converged_ = false;
final_transformation_ = transformation_ = previous_transformation_ = Matrix4::Identity ();
// Right before we estimate the transformation, we set all the point.data[3] values to 1 to aid the rigid
// transformation
for (size_t i = 0; i < indices_->size (); ++i)
output.points[i].data[3] = 1.0;
computeTransformation (output, guess);//求解旋转矩阵,动态绑定到pcl::IterativeClosestPoint::computeTransformation()
deinitCompute ();// return (true); 直接返回true
}
initCompute用于初始化kd树,及点云索引
template <typename PointSource, typename PointTarget, typename Scalar>
bool
Registration<PointSource, PointTarget, Scalar>::initCompute()
{
if (!target_) {
PCL_ERROR("[pcl::registration::%s::compute] No input target dataset was given!\n",
getClassName().c_str());
return (false);
}
// Only update target kd-tree if a new target cloud was set
if (target_cloud_updated_ && !force_no_recompute_) {//force_no_recompute_默认初始化为false,因此第一次运行时,进入该if判断
tree_->setInputCloud(target_);//用目标点云初始化kd树
target_cloud_updated_ = false;
}
// Update the correspondence estimation
if (correspondence_estimation_) {
correspondence_estimation_->setSearchMethodTarget(tree_, force_no_recompute_);//初始化correspondence_estimation_,包括 tree_ = tree; force_no_recompute_ = force_no_recompute; target_cloud_updated_ = true;
correspondence_estimation_->setSearchMethodSource(tree_reciprocal_,
force_no_recompute_reciprocal_);
}
// Note: we /cannot/ update the search method on all correspondence rejectors, because
// we know nothing about them. If they should be cached, they must be cached
// individually.
return (PCLBase<PointSource>::initCompute());//初始化与input_的size对应的indices_
}
enum ConvergenceState
{
CONVERGENCE_CRITERIA_NOT_CONVERGED,//默认值,converged_ = false;
CONVERGENCE_CRITERIA_ITERATIONS,//迭代结果为达到迭代次数,此时若设置failure_after_max_iter_ = true,就会导致converged_ = false;默认状态是failure_after_max_iter_ = false,因此该情况下超过迭代次数会判断converged_ = true
CONVERGENCE_CRITERIA_TRANSFORM,//平移距离的平方和满足transformation_epsilon_要求,则判断CONVERGENCE_CRITERIA_TRANSFORM, converged_ = true
CONVERGENCE_CRITERIA_ABS_MSE,//两次迭代的绝对距离差满足收敛要求,converged_ = true
CONVERGENCE_CRITERIA_REL_MSE,//两次迭代的相对距离差满足收敛要求,converged_ = true
CONVERGENCE_CRITERIA_NO_CORRESPONDENCES//迭代结果为对应点对太少,converged_ = false;
};
pcl::IterativeClosestPoint::computeTransformation() 位于pcl/registration/impl/icp.hpp文件中:
template <typename PointSource, typename PointTarget, typename Scalar>
void
IterativeClosestPoint<PointSource, PointTarget, Scalar>::computeTransformation(
PointCloudSource& output, const Matrix4& guess)
{
// Point cloud containing the correspondences of each point in <input, indices>
PointCloudSourcePtr input_transformed(new PointCloudSource);
nr_iterations_ = 0;
converged_ = false;
// Initialise final transformation to the guessed one
final_transformation_ = guess;
// If the guessed transformation is non identity
if (guess != Matrix4::Identity()) {
input_transformed->resize(input_->size());
// Apply guessed transformation prior to search for neighbours
transformCloud(*input_, *input_transformed, guess);
}
else
*input_transformed = *input_;
transformation_ = Matrix4::Identity();
// Make blobs if necessary
determineRequiredBlobData();// 未指定correspondence_rejectors_ 以及点云都没有法向量的话,need_source_blob_以及need_target_blob_ 都是false,即不要求处理“二进制长对象(blob)”
PCLPointCloud2::Ptr target_blob(new PCLPointCloud2);
if (need_target_blob_)
pcl::toPCLPointCloud2(*target_, *target_blob);
// Pass in the default target for the Correspondence Estimation/Rejection code
correspondence_estimation_->setInputTarget(target_); //设置CorrespondenceEstimationBase中的target_ ,target_cloud_updated_ = true;
if (correspondence_estimation_->requiresTargetNormals())
correspondence_estimation_->setTargetNormals(target_blob);
// Correspondence Rejectors need a binary blob
for (std::size_t i = 0; i < correspondence_rejectors_.size(); ++i) {
registration::CorrespondenceRejector::Ptr& rej = correspondence_rejectors_[i];
if (rej->requiresTargetPoints())
rej->setTargetPoints(target_blob);
if (rej->requiresTargetNormals() && target_has_normals_)
rej->setTargetNormals(target_blob);
}
convergence_criteria_->setMaximumIterations(max_iterations_);//设置DefaultConvergenceCriteria 中的max_iterations_
convergence_criteria_->setRelativeMSE(euclidean_fitness_epsilon_);//设置DefaultConvergenceCriteria 中的mse_threshold_relative_
convergence_criteria_->setTranslationThreshold(transformation_epsilon_);//设置DefaultConvergenceCriteria 中的translation_threshold_
if (transformation_rotation_epsilon_ > 0)
convergence_criteria_->setRotationThreshold(transformation_rotation_epsilon_);
else
convergence_criteria_->setRotationThreshold(1.0 - transformation_epsilon_);//设置DefaultConvergenceCriteria 中的rotation_threshold_
// Repeat until convergence
//以下进入icp迭代解算
do {
// Get blob data if needed
PCLPointCloud2::Ptr input_transformed_blob;
if (need_source_blob_) {
input_transformed_blob.reset(new PCLPointCloud2);
toPCLPointCloud2(*input_transformed, *input_transformed_blob);
}
// Save the previously estimated transformation
previous_transformation_ = transformation_;
// Set the source each iteration, to ensure the dirty flag is updated
correspondence_estimation_->setInputSource(input_transformed);
if (correspondence_estimation_->requiresSourceNormals())
correspondence_estimation_->setSourceNormals(input_transformed_blob);
// Estimate correspondences
if (use_reciprocal_correspondence_)
correspondence_estimation_->determineReciprocalCorrespondences(
*correspondences_, corr_dist_threshold_);
else
correspondence_estimation_->determineCorrespondences(*correspondences_,
corr_dist_threshold_);//基于kd树最近邻查找最近点。
// if (correspondence_rejectors_.empty ())
CorrespondencesPtr temp_correspondences(new Correspondences(*correspondences_));
for (std::size_t i = 0; i < correspondence_rejectors_.size(); ++i) {
registration::CorrespondenceRejector::Ptr& rej = correspondence_rejectors_[i];
PCL_DEBUG("Applying a correspondence rejector method: %s.\n",
rej->getClassName().c_str());
if (rej->requiresSourcePoints())
rej->setSourcePoints(input_transformed_blob);
if (rej->requiresSourceNormals() && source_has_normals_)
rej->setSourceNormals(input_transformed_blob);
rej->setInputCorrespondences(temp_correspondences);
rej->getCorrespondences(*correspondences_);
// Modify input for the next iteration
if (i < correspondence_rejectors_.size() - 1)
*temp_correspondences = *correspondences_;
}
std::size_t cnt = correspondences_->size();
// Check whether we have enough correspondences
if (static_cast<int>(cnt) < min_number_correspondences_) {
PCL_ERROR("[pcl::%s::computeTransformation] Not enough correspondences found. "
"Relax your threshold parameters.\n",
getClassName().c_str());
convergence_criteria_->setConvergenceState(
pcl::registration::DefaultConvergenceCriteria<
Scalar>::CONVERGENCE_CRITERIA_NO_CORRESPONDENCES);
converged_ = false;
break;//若对应点对数太少,则直接终止icp,收敛状态为false
}
// Estimate the transform
transformation_estimation_->estimateRigidTransformation(
*input_transformed, *target_, *correspondences_, transformation_);//进行svd位姿解算
// Transform the data
transformCloud(*input_transformed, *input_transformed, transformation_);
// Obtain the final transformation
final_transformation_ = transformation_ * final_transformation_;//叠加位姿估算结果
++nr_iterations_;
// Update the vizualization of icp convergence
// if (update_visualizer_ != 0)
// update_visualizer_(output, source_indices_good, *target_, target_indices_good );
converged_ = static_cast<bool>((*convergence_criteria_));//这里显式类型转换调用了pcl::registration::ConvergenceCriteria中对bool的重载,该重载中调用了pcl::registration::DefaultConvergenceCriteria<Scalar>::hasConverged()函数
} while (convergence_criteria_->getConvergenceState() ==
pcl::registration::DefaultConvergenceCriteria<
Scalar>::CONVERGENCE_CRITERIA_NOT_CONVERGED);
// Transform the input cloud using the final transformation
PCL_DEBUG("Transformation "
"is:\n\t%5f\t%5f\t%5f\t%5f\n\t%5f\t%5f\t%5f\t%5f\n\t%5f\t%5f\t%5f\t%5f\n\t%"
"5f\t%5f\t%5f\t%5f\n",
final_transformation_(0, 0),
final_transformation_(0, 1),
final_transformation_(0, 2),
final_transformation_(0, 3),
final_transformation_(1, 0),
final_transformation_(1, 1),
final_transformation_(1, 2),
final_transformation_(1, 3),
final_transformation_(2, 0),
final_transformation_(2, 1),
final_transformation_(2, 2),
final_transformation_(2, 3),
final_transformation_(3, 0),
final_transformation_(3, 1),
final_transformation_(3, 2),
final_transformation_(3, 3));//打印最终的变换矩阵
// Copy all the values
output = *input_;
// Transform the XYZ + normals
transformCloud(*input_, output, final_transformation_);//进行坐标转换获取最终结果output
}
template <typename Scalar> bool
pcl::registration::DefaultConvergenceCriteria<Scalar>::hasConverged ()
{
convergence_state_ = CONVERGENCE_CRITERIA_NOT_CONVERGED;
PCL_DEBUG ("[pcl::DefaultConvergenceCriteria::hasConverged] Iteration %d out of %d.\n", iterations_, max_iterations_);
// 1. Number of iterations has reached the maximum user imposed number of iterations
if (iterations_ >= max_iterations_)
{
if (failure_after_max_iter_)
return (false);
else
{
convergence_state_ = CONVERGENCE_CRITERIA_ITERATIONS;
return (true);
}
return (failure_after_max_iter_ ? false : true);
}
// 2. The epsilon (difference) between the previous transformation and the current estimated transformation
double cos_angle = 0.5 * (transformation_.coeff (0, 0) + transformation_.coeff (1, 1) + transformation_.coeff (2, 2) - 1);//罗德里格斯公式
double translation_sqr = transformation_.coeff (0, 3) * transformation_.coeff (0, 3) +
transformation_.coeff (1, 3) * transformation_.coeff (1, 3) +
transformation_.coeff (2, 3) * transformation_.coeff (2, 3);//平移二范数
PCL_DEBUG ("[pcl::DefaultConvergenceCriteria::hasConverged] Current transformation gave %f rotation (cosine) and %f translation.\n", cos_angle, translation_sqr);
if (cos_angle >= rotation_threshold_ && translation_sqr <= translation_threshold_)//平移距离的二范数满足要求则判断CONVERGENCE_CRITERIA_TRANSFORM
{
if (iterations_similar_transforms_ < max_iterations_similar_transforms_)
{
// Increment the number of transforms that the thresholds are allowed to be similar
++iterations_similar_transforms_;
return (false);
}
else
{
iterations_similar_transforms_ = 0;
convergence_state_ = CONVERGENCE_CRITERIA_TRANSFORM;
return (true);
}
}
correspondences_cur_mse_ = calculateMSE (correspondences_);//计算平均距离,单位为m
PCL_DEBUG ("[pcl::DefaultConvergenceCriteria::hasConverged] Previous / Current MSE for correspondences distances is: %f / %f.\n", correspondences_prev_mse_, correspondences_cur_mse_);
// 3. The relative sum of Euclidean squared errors is smaller than a user defined threshold
// Absolute
if (fabs (correspondences_cur_mse_ - correspondences_prev_mse_) < mse_threshold_absolute_)//两次迭代的绝对距离差满足收敛要求
{
if (iterations_similar_transforms_ < max_iterations_similar_transforms_)
{
// Increment the number of transforms that the thresholds are allowed to be similar
++iterations_similar_transforms_;
return (false);
}
else
{
iterations_similar_transforms_ = 0;
convergence_state_ = CONVERGENCE_CRITERIA_ABS_MSE;
return (true);
}
}
// Relative
if (fabs (correspondences_cur_mse_ - correspondences_prev_mse_) / correspondences_prev_mse_ < mse_threshold_relative_)//两次迭代的相对距离差满足收敛要求
{
if (iterations_similar_transforms_ < max_iterations_similar_transforms_)
{
// Increment the number of transforms that the thresholds are allowed to be similar
++iterations_similar_transforms_;
return (false);
}
else
{
iterations_similar_transforms_ = 0;
convergence_state_ = CONVERGENCE_CRITERIA_REL_MSE;
return (true);
}
}
correspondences_prev_mse_ = correspondences_cur_mse_;
return (false);
}