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Merge pull request #344 from sdmiller/adding_joint_icp
Feature: JointIterativeClosestPoint
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registration/include/pcl/registration/impl/joint_icp.hpp
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/* | ||
* Software License Agreement (BSD License) | ||
* | ||
* Point Cloud Library (PCL) - www.pointclouds.org | ||
* Copyright (c) 2009-2012, Willow Garage, Inc. | ||
* Copyright (c) 2012-, Open Perception, Inc. | ||
* | ||
* All rights reserved. | ||
* | ||
* Redistribution and use in source and binary forms, with or without | ||
* modification, are permitted provided that the following conditions | ||
* are met: | ||
* | ||
* * Redistributions of source code must retain the above copyright | ||
* notice, this list of conditions and the following disclaimer. | ||
* * Redistributions in binary form must reproduce the above | ||
* copyright notice, this list of conditions and the following | ||
* disclaimer in the documentation and/or other materials provided | ||
* with the distribution. | ||
* * Neither the name of the copyright holder(s) nor the names of its | ||
* contributors may be used to endorse or promote products derived | ||
* from this software without specific prior written permission. | ||
* | ||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS | ||
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT | ||
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS | ||
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE | ||
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, | ||
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, | ||
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; | ||
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER | ||
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT | ||
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN | ||
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE | ||
* POSSIBILITY OF SUCH DAMAGE. | ||
* | ||
*/ | ||
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#ifndef PCL_REGISTRATION_IMPL_JOINT_ICP_HPP_ | ||
#define PCL_REGISTRATION_IMPL_JOINT_ICP_HPP_ | ||
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#include <pcl/registration/boost.h> | ||
#include <pcl/correspondence.h> | ||
#include <pcl/console/print.h> | ||
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/////////////////////////////////////////////////////////////////////////////////////////// | ||
template <typename PointSource, typename PointTarget, typename Scalar> void | ||
pcl::JointIterativeClosestPoint<PointSource, PointTarget, Scalar>::computeTransformation ( | ||
PointCloudSource &output, const Matrix4 &guess) | ||
{ | ||
// Point clouds containing the correspondences of each point in <input, indices> | ||
if (sources_.size () != targets_.size () || sources_.empty () || targets_.empty ()) | ||
{ | ||
PCL_ERROR ("[pcl::%s::computeTransformation] Must set InputSources and InputTargets to the same, nonzero size!\n", | ||
getClassName ().c_str ()); | ||
return; | ||
} | ||
bool manual_correspondence_estimations_set = true; | ||
if (correspondence_estimations_.empty ()) | ||
{ | ||
manual_correspondence_estimations_set = false; | ||
correspondence_estimations_.resize (sources_.size ()); | ||
for (size_t i = 0; i < sources_.size (); i++) | ||
{ | ||
correspondence_estimations_[i].reset (new pcl::registration::CorrespondenceEstimation<PointSource, PointTarget, Scalar>); | ||
*correspondence_estimations_[i] = *correspondence_estimation_; | ||
KdTreeReciprocalPtr src_tree (new KdTreeReciprocal); | ||
KdTreePtr tgt_tree (new KdTree); | ||
correspondence_estimations_[i]->setSearchMethodTarget (tgt_tree); | ||
correspondence_estimations_[i]->setSearchMethodSource (src_tree); | ||
} | ||
} | ||
if (correspondence_estimations_.size () != sources_.size ()) | ||
{ | ||
PCL_ERROR ("[pcl::%s::computeTransform] Must set CorrespondenceEstimations to be the same size as the joint\n", | ||
getClassName ().c_str ()); | ||
return; | ||
} | ||
std::vector<PointCloudSourcePtr> inputs_transformed (sources_.size ()); | ||
for (size_t i = 0; i < sources_.size (); i++) | ||
{ | ||
inputs_transformed[i].reset (new PointCloudSource); | ||
} | ||
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nr_iterations_ = 0; | ||
converged_ = false; | ||
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// Initialise final transformation to the guessed one | ||
final_transformation_ = guess; | ||
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// Make a combined transformed input and output | ||
std::vector<size_t> input_offsets (sources_.size ()); | ||
std::vector<size_t> target_offsets (targets_.size ()); | ||
PointCloudSourcePtr sources_combined (new PointCloudSource); | ||
PointCloudSourcePtr inputs_transformed_combined (new PointCloudSource); | ||
PointCloudTargetPtr targets_combined (new PointCloudTarget); | ||
size_t input_offset = 0; | ||
size_t target_offset = 0; | ||
for (size_t i = 0; i < sources_.size (); i++) | ||
{ | ||
// If the guessed transformation is non identity | ||
if (guess != Matrix4::Identity ()) | ||
{ | ||
// Apply guessed transformation prior to search for neighbours | ||
pcl::transformPointCloud (*sources_[i], *inputs_transformed[i], guess); | ||
} | ||
else | ||
{ | ||
*inputs_transformed[i] = *sources_[i]; | ||
} | ||
*sources_combined += *sources_[i]; | ||
*inputs_transformed_combined += *inputs_transformed[i]; | ||
*targets_combined += *targets_[i]; | ||
input_offsets[i] = input_offset; | ||
target_offsets[i] = target_offset; | ||
input_offset += inputs_transformed[i]->size (); | ||
target_offset += targets_[i]->size (); | ||
} | ||
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transformation_ = Matrix4::Identity (); | ||
// Pass in the default target for the Correspondence Estimation/Rejection code | ||
for (size_t i = 0; i < sources_.size (); i++) | ||
{ | ||
correspondence_estimations_[i]->setInputTarget (targets_[i]); | ||
} | ||
// We should be doing something like this | ||
// for (size_t i = 0; i < correspondence_rejectors_.size (); ++i) | ||
// { | ||
// correspondence_rejectors_[i]->setTargetCloud (target_); | ||
// if (target_has_normals_) | ||
// correspondence_rejectors_[i]->setTargetNormals (target_); | ||
// } | ||
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convergence_criteria_->setMaximumIterations (max_iterations_); | ||
convergence_criteria_->setRelativeMSE (euclidean_fitness_epsilon_); | ||
convergence_criteria_->setTranslationThreshold (transformation_epsilon_); | ||
convergence_criteria_->setRotationThreshold (1.0 - transformation_epsilon_); | ||
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// Repeat until convergence | ||
std::vector<CorrespondencesPtr> partial_correspondences_ (sources_.size ()); | ||
for (size_t i = 0; i < sources_.size (); i++) | ||
{ | ||
partial_correspondences_[i].reset (new pcl::Correspondences); | ||
} | ||
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do | ||
{ | ||
// Save the previously estimated transformation | ||
previous_transformation_ = transformation_; | ||
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// Set the source each iteration, to ensure the dirty flag is updated | ||
correspondences_->clear (); | ||
for (size_t i = 0; i < correspondence_estimations_.size (); i++) | ||
{ | ||
correspondence_estimations_[i]->setInputSource (inputs_transformed[i]); | ||
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// Estimate correspondences on each cloud pair separately | ||
if (use_reciprocal_correspondence_) | ||
{ | ||
correspondence_estimations_[i]->determineReciprocalCorrespondences (*partial_correspondences_[i], corr_dist_threshold_); | ||
} | ||
else | ||
{ | ||
correspondence_estimations_[i]->determineCorrespondences (*partial_correspondences_[i], corr_dist_threshold_); | ||
} | ||
PCL_DEBUG ("[pcl::%s::computeTransformation] Found %d partial correspondences for cloud [%d]\n", | ||
getClassName ().c_str (), | ||
partial_correspondences_[i]->size (), i); | ||
for (size_t j = 0; j < partial_correspondences_[i]->size (); j++) | ||
{ | ||
pcl::Correspondence corr = partial_correspondences_[i]->at (j); | ||
// Update the offsets to be for the combined clouds | ||
corr.index_query += input_offsets[i]; | ||
corr.index_match += target_offsets[i]; | ||
correspondences_->push_back (corr); | ||
} | ||
} | ||
PCL_DEBUG ("[pcl::%s::computeTransformation] Total correspondences: %d\n", getClassName ().c_str (), correspondences_->size ()); | ||
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CorrespondencesPtr temp_correspondences (new Correspondences (*correspondences_)); | ||
for (size_t i = 0; i < correspondence_rejectors_.size (); ++i) | ||
{ | ||
PCL_DEBUG ("Applying a correspondence rejector method: %s.\n", correspondence_rejectors_[i]->getClassName ().c_str ()); | ||
// We should be doing something like this | ||
// correspondence_rejectors_[i]->setInputSource (input_transformed); | ||
// if (source_has_normals_) | ||
// correspondence_rejectors_[i]->setInputNormals (input_transformed); | ||
correspondence_rejectors_[i]->setInputCorrespondences (temp_correspondences); | ||
correspondence_rejectors_[i]->getCorrespondences (*correspondences_); | ||
// Modify input for the next iteration | ||
if (i < correspondence_rejectors_.size () - 1) | ||
*temp_correspondences = *correspondences_; | ||
} | ||
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size_t cnt = correspondences_->size (); | ||
// Check whether we have enough correspondences | ||
if (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; | ||
} | ||
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// Estimate the transform jointly, on a combined correspondence set | ||
transformation_estimation_->estimateRigidTransformation (*inputs_transformed_combined, *targets_combined, *correspondences_, transformation_); | ||
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// Tranform the combined data | ||
pcl::transformPointCloud (*inputs_transformed_combined, *inputs_transformed_combined, transformation_); | ||
// And all its components | ||
for (size_t i = 0; i < sources_.size (); i++) | ||
{ | ||
pcl::transformPointCloud (*inputs_transformed[i], *inputs_transformed[i], transformation_); | ||
} | ||
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// Obtain the final transformation | ||
final_transformation_ = transformation_ * final_transformation_; | ||
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++nr_iterations_; | ||
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// Update the vizualization of icp convergence | ||
//if (update_visualizer_ != 0) | ||
// update_visualizer_(output, source_indices_good, *target_, target_indices_good ); | ||
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converged_ = static_cast<bool> ((*convergence_criteria_)); | ||
} | ||
while (!converged_); | ||
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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)); | ||
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// For fitness checks, etc, we'll use an aggregated cloud for now (should be evaluating independently for correctness, but this requires propagating a few virtual methods from Registration) | ||
IterativeClosestPoint<PointSource, PointTarget, Scalar>::setInputSource (sources_combined); | ||
IterativeClosestPoint<PointSource, PointTarget, Scalar>::setInputTarget (targets_combined); | ||
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// If we automatically set the correspondence estimators, we should clear them now | ||
if (!manual_correspondence_estimations_set) | ||
{ | ||
correspondence_estimations_.clear (); | ||
} | ||
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// By definition, this method will return an empty cloud (for compliance with the ICP API). | ||
// We can figure out a better solution, if necessary. | ||
output = PointCloudSource (); | ||
} | ||
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#endif /* PCL_REGISTRATION_IMPL_JOINT_ICP_HPP_ */ | ||
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