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Split test_registration into three binaries #2727

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Dec 20, 2018
Merged

Split test_registration into three binaries #2727

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3e3a61e
Extract NormalDistributionTransform test into a separate file
taketwo Dec 19, 2018
214775f
Extract SampleConsensusInitialAlignment and SampleConsensusPrerejecti…
taketwo Dec 19, 2018
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12 changes: 11 additions & 1 deletion test/registration/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -20,9 +20,19 @@ if(build)
if(BUILD_io)
PCL_ADD_TEST(a_registration_test test_registration
FILES test_registration.cpp
LINK_WITH pcl_gtest pcl_io pcl_registration pcl_features pcl_search pcl_kdtree pcl_kdtree
LINK_WITH pcl_gtest pcl_io pcl_registration pcl_features pcl_search pcl_kdtree
ARGUMENTS "${PCL_SOURCE_DIR}/test/bun0.pcd" "${PCL_SOURCE_DIR}/test/bun4.pcd" "${PCL_SOURCE_DIR}/test/milk_color.pcd")

PCL_ADD_TEST(ndt test_ndt
FILES test_ndt.cpp
LINK_WITH pcl_gtest pcl_io pcl_registration
ARGUMENTS "${PCL_SOURCE_DIR}/test/bun0.pcd" "${PCL_SOURCE_DIR}/test/bun4.pcd")

PCL_ADD_TEST(sac_ia test_sac_ia
FILES test_sac_ia.cpp
LINK_WITH pcl_gtest pcl_io pcl_registration
ARGUMENTS "${PCL_SOURCE_DIR}/test/bun0.pcd" "${PCL_SOURCE_DIR}/test/bun4.pcd")

PCL_ADD_TEST(registration_api test_registration_api
FILES test_registration_api.cpp test_registration_api_data.h
LINK_WITH pcl_gtest pcl_io pcl_registration pcl_features pcl_kdtree
Expand Down
119 changes: 119 additions & 0 deletions test/registration/test_ndt.cpp
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Original file line number Diff line number Diff line change
@@ -0,0 +1,119 @@
/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2010-2011, Willow Garage, Inc.
* Copyright (c) 2018-, 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.
*
* $Id$
*
*/

#include <gtest/gtest.h>

#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/registration/ndt.h>

using namespace pcl;
using namespace pcl::io;

PointCloud<PointXYZ> cloud_source, cloud_target;

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (PCL, NormalDistributionsTransform)
{
typedef PointNormal PointT;
PointCloud<PointT>::Ptr src (new PointCloud<PointT>);
copyPointCloud (cloud_source, *src);
PointCloud<PointT>::Ptr tgt (new PointCloud<PointT>);
copyPointCloud (cloud_target, *tgt);
PointCloud<PointT> output;

NormalDistributionsTransform<PointT, PointT> reg;
reg.setStepSize (0.05);
reg.setResolution (0.025f);
reg.setInputSource (src);
reg.setInputTarget (tgt);
reg.setMaximumIterations (50);
reg.setTransformationEpsilon (1e-8);
// Register
reg.align (output);
EXPECT_EQ (int (output.points.size ()), int (cloud_source.points.size ()));
EXPECT_LT (reg.getFitnessScore (), 0.001);

// Check again, for all possible caching schemes
for (int iter = 0; iter < 4; iter++)
{
bool force_cache = (bool) iter/2;
bool force_cache_reciprocal = (bool) iter%2;
pcl::search::KdTree<PointT>::Ptr tree(new pcl::search::KdTree<PointT>);
// Ensure that, when force_cache is not set, we are robust to the wrong input
if (force_cache)
tree->setInputCloud (tgt);
reg.setSearchMethodTarget (tree, force_cache);

pcl::search::KdTree<PointT>::Ptr tree_recip (new pcl::search::KdTree<PointT>);
if (force_cache_reciprocal)
tree_recip->setInputCloud (src);
reg.setSearchMethodSource (tree_recip, force_cache_reciprocal);

// Register
reg.align (output);
EXPECT_EQ (int (output.points.size ()), int (cloud_source.points.size ()));
EXPECT_LT (reg.getFitnessScore (), 0.001);
}
}

int
main (int argc, char** argv)
{
if (argc < 3)
{
std::cerr << "No test files given. Please download `bun0.pcd` and `bun4.pcd`pass their path to the test." << std::endl;
return (-1);
}

if (loadPCDFile (argv[1], cloud_source) < 0)
{
std::cerr << "Failed to read test file. Please download `bun0.pcd` and pass its path to the test." << std::endl;
return (-1);
}
if (loadPCDFile (argv[2], cloud_target) < 0)
{
std::cerr << "Failed to read test file. Please download `bun4.pcd` and pass its path to the test." << std::endl;
return (-1);
}

testing::InitGoogleTest (&argc, argv);
return (RUN_ALL_TESTS ());
}
233 changes: 1 addition & 232 deletions test/registration/test_registration.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,6 @@
#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/fpfh.h>
#include <pcl/registration/registration.h>
#include <pcl/registration/icp.h>
#include <pcl/registration/joint_icp.h>
Expand All @@ -55,12 +54,10 @@
#include <pcl/registration/correspondence_rejection_sample_consensus.h>
#include <pcl/registration/correspondence_rejection_surface_normal.h>
#include <pcl/registration/correspondence_estimation_normal_shooting.h>
#include <pcl/registration/ia_ransac.h>
#include <pcl/registration/pyramid_feature_matching.h>
#include <pcl/features/ppf.h>
#include <pcl/registration/ppf_registration.h>
#include <pcl/registration/ndt.h>
#include <pcl/registration/sample_consensus_prerejective.h>
#include <pcl/filters/voxel_grid.h>
// We need Histogram<2> to function, so we'll explicitly add kdtree_flann.hpp here
#include <pcl/kdtree/impl/kdtree_flann.hpp>
//(pcl::Histogram<2>)
Expand Down Expand Up @@ -592,234 +589,6 @@ TEST (PCL, GeneralizedIterativeClosestPoint6D)
}
}

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (PCL, NormalDistributionsTransform)
{
typedef PointNormal PointT;
PointCloud<PointT>::Ptr src (new PointCloud<PointT>);
copyPointCloud (cloud_source, *src);
PointCloud<PointT>::Ptr tgt (new PointCloud<PointT>);
copyPointCloud (cloud_target, *tgt);
PointCloud<PointT> output;

NormalDistributionsTransform<PointT, PointT> reg;
reg.setStepSize (0.05);
reg.setResolution (0.025f);
reg.setInputSource (src);
reg.setInputTarget (tgt);
reg.setMaximumIterations (50);
reg.setTransformationEpsilon (1e-8);
// Register
reg.align (output);
EXPECT_EQ (int (output.points.size ()), int (cloud_source.points.size ()));
EXPECT_LT (reg.getFitnessScore (), 0.001);

// Check again, for all possible caching schemes
for (int iter = 0; iter < 4; iter++)
{
bool force_cache = (bool) iter/2;
bool force_cache_reciprocal = (bool) iter%2;
pcl::search::KdTree<PointT>::Ptr tree(new pcl::search::KdTree<PointT>);
// Ensure that, when force_cache is not set, we are robust to the wrong input
if (force_cache)
tree->setInputCloud (tgt);
reg.setSearchMethodTarget (tree, force_cache);

pcl::search::KdTree<PointT>::Ptr tree_recip (new pcl::search::KdTree<PointT>);
if (force_cache_reciprocal)
tree_recip->setInputCloud (src);
reg.setSearchMethodSource (tree_recip, force_cache_reciprocal);

// Register
reg.align (output);
EXPECT_EQ (int (output.points.size ()), int (cloud_source.points.size ()));
EXPECT_LT (reg.getFitnessScore (), 0.001);
}
}


//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (PCL, SampleConsensusInitialAlignment)
{
// Transform the source cloud by a large amount
Eigen::Vector3f initial_offset (100, 0, 0);
float angle = static_cast<float> (M_PI) / 2.0f;
Eigen::Quaternionf initial_rotation (cos (angle / 2), 0, 0, sin (angle / 2));
PointCloud<PointXYZ> cloud_source_transformed;
transformPointCloud (cloud_source, cloud_source_transformed, initial_offset, initial_rotation);

// Create shared pointers
PointCloud<PointXYZ>::Ptr cloud_source_ptr, cloud_target_ptr;
cloud_source_ptr = cloud_source_transformed.makeShared ();
cloud_target_ptr = cloud_target.makeShared ();

// Initialize estimators for surface normals and FPFH features
search::KdTree<PointXYZ>::Ptr tree (new search::KdTree<PointXYZ>);

NormalEstimation<PointXYZ, Normal> norm_est;
norm_est.setSearchMethod (tree);
norm_est.setRadiusSearch (0.05);
PointCloud<Normal> normals;

FPFHEstimation<PointXYZ, Normal, FPFHSignature33> fpfh_est;
fpfh_est.setSearchMethod (tree);
fpfh_est.setRadiusSearch (0.05);
PointCloud<FPFHSignature33> features_source, features_target;

// Estimate the FPFH features for the source cloud
norm_est.setInputCloud (cloud_source_ptr);
norm_est.compute (normals);
fpfh_est.setInputCloud (cloud_source_ptr);
fpfh_est.setInputNormals (normals.makeShared ());
fpfh_est.compute (features_source);

// Estimate the FPFH features for the target cloud
norm_est.setInputCloud (cloud_target_ptr);
norm_est.compute (normals);
fpfh_est.setInputCloud (cloud_target_ptr);
fpfh_est.setInputNormals (normals.makeShared ());
fpfh_est.compute (features_target);

// Initialize Sample Consensus Initial Alignment (SAC-IA)
SampleConsensusInitialAlignment<PointXYZ, PointXYZ, FPFHSignature33> reg;
reg.setMinSampleDistance (0.05f);
reg.setMaxCorrespondenceDistance (0.1);
reg.setMaximumIterations (1000);

reg.setInputSource (cloud_source_ptr);
reg.setInputTarget (cloud_target_ptr);
reg.setSourceFeatures (features_source.makeShared ());
reg.setTargetFeatures (features_target.makeShared ());

// Register
reg.align (cloud_reg);
EXPECT_EQ (int (cloud_reg.points.size ()), int (cloud_source.points.size ()));
EXPECT_LT (reg.getFitnessScore (), 0.0005);

// Check again, for all possible caching schemes
typedef pcl::PointXYZ PointT;
for (int iter = 0; iter < 4; iter++)
{
bool force_cache = (bool) iter/2;
bool force_cache_reciprocal = (bool) iter%2;
pcl::search::KdTree<PointT>::Ptr tree(new pcl::search::KdTree<PointT>);
// Ensure that, when force_cache is not set, we are robust to the wrong input
if (force_cache)
tree->setInputCloud (cloud_target_ptr);
reg.setSearchMethodTarget (tree, force_cache);

pcl::search::KdTree<PointT>::Ptr tree_recip (new pcl::search::KdTree<PointT>);
if (force_cache_reciprocal)
tree_recip->setInputCloud (cloud_source_ptr);
reg.setSearchMethodSource(tree_recip, force_cache_reciprocal);

// Register
reg.align (cloud_reg);
EXPECT_EQ (int (cloud_reg.points.size ()), int (cloud_source.points.size ()));
EXPECT_LT (reg.getFitnessScore (), 0.0005);
}
}


//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (PCL, SampleConsensusPrerejective)
{
/*
* This test is a near-exact copy of the SampleConsensusInitialAlignment test,
* with the only modifications that:
* 1) the number of iterations is increased 1000 --> 5000
* 2) the feature correspondence randomness (the number of kNNs) is decreased 10 --> 2
*/

// Transform the source cloud by a large amount
Eigen::Vector3f initial_offset (100, 0, 0);
float angle = static_cast<float> (M_PI) / 2.0f;
Eigen::Quaternionf initial_rotation (cos (angle / 2), 0, 0, sin (angle / 2));
PointCloud<PointXYZ> cloud_source_transformed;
transformPointCloud (cloud_source, cloud_source_transformed, initial_offset, initial_rotation);

// Create shared pointers
PointCloud<PointXYZ>::Ptr cloud_source_ptr, cloud_target_ptr;
cloud_source_ptr = cloud_source_transformed.makeShared ();
cloud_target_ptr = cloud_target.makeShared ();

// Initialize estimators for surface normals and FPFH features
search::KdTree<PointXYZ>::Ptr tree (new search::KdTree<PointXYZ>);

// Normal estimator
NormalEstimation<PointXYZ, Normal> norm_est;
norm_est.setSearchMethod (tree);
norm_est.setRadiusSearch (0.005);
PointCloud<Normal> normals;

// FPFH estimator
FPFHEstimation<PointXYZ, Normal, FPFHSignature33> fpfh_est;
fpfh_est.setSearchMethod (tree);
fpfh_est.setRadiusSearch (0.05);
PointCloud<FPFHSignature33> features_source, features_target;

// Estimate the normals and the FPFH features for the source cloud
norm_est.setInputCloud (cloud_source_ptr);
norm_est.compute (normals);
fpfh_est.setInputCloud (cloud_source_ptr);
fpfh_est.setInputNormals (normals.makeShared ());
fpfh_est.compute (features_source);

// Estimate the normals and the FPFH features for the target cloud
norm_est.setInputCloud (cloud_target_ptr);
norm_est.compute (normals);
fpfh_est.setInputCloud (cloud_target_ptr);
fpfh_est.setInputNormals (normals.makeShared ());
fpfh_est.compute (features_target);

// Initialize Sample Consensus Prerejective with 5x the number of iterations and 1/5 feature kNNs as SAC-IA
SampleConsensusPrerejective<PointXYZ, PointXYZ, FPFHSignature33> reg;
reg.setMaxCorrespondenceDistance (0.1);
reg.setMaximumIterations (5000);
reg.setSimilarityThreshold (0.6f);
reg.setCorrespondenceRandomness (2);

// Set source and target cloud/features
reg.setInputSource (cloud_source_ptr);
reg.setInputTarget (cloud_target_ptr);
reg.setSourceFeatures (features_source.makeShared ());
reg.setTargetFeatures (features_target.makeShared ());

// Register
reg.align (cloud_reg);

// Check output consistency and quality of alignment
EXPECT_EQ (static_cast<int> (cloud_reg.points.size ()), static_cast<int> (cloud_source.points.size ()));
float inlier_fraction = static_cast<float> (reg.getInliers ().size ()) / static_cast<float> (cloud_source.points.size ());
EXPECT_GT (inlier_fraction, 0.95f);

// Check again, for all possible caching schemes
typedef pcl::PointXYZ PointT;
for (int iter = 0; iter < 4; iter++)
{
bool force_cache = (bool) iter/2;
bool force_cache_reciprocal = (bool) iter%2;
pcl::search::KdTree<PointT>::Ptr tree(new pcl::search::KdTree<PointT>);
// Ensure that, when force_cache is not set, we are robust to the wrong input
if (force_cache)
tree->setInputCloud (cloud_target_ptr);
reg.setSearchMethodTarget (tree, force_cache);

pcl::search::KdTree<PointT>::Ptr tree_recip (new pcl::search::KdTree<PointT>);
if (force_cache_reciprocal)
tree_recip->setInputCloud (cloud_source_ptr);
reg.setSearchMethodSource(tree_recip, force_cache_reciprocal);

// Register
reg.align (cloud_reg);

// Check output consistency and quality of alignment
EXPECT_EQ (int (cloud_reg.points.size ()), int (cloud_source.points.size ()));
inlier_fraction = static_cast<float> (reg.getInliers ().size ()) / static_cast<float> (cloud_source.points.size ());
EXPECT_GT (inlier_fraction, 0.95f);
}
}


//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (PCL, PyramidFeatureHistogram)
Expand Down
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