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/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2010-2012, Willow Garage, Inc.
* Copyright (c) 2014-, 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.
*
*/
#include <pcl/test/gtest.h>
#include <pcl/pcl_tests.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/common/utils.h>
#include <pcl/sample_consensus/msac.h>
#include <pcl/sample_consensus/lmeds.h>
#include <pcl/sample_consensus/rmsac.h>
#include <pcl/sample_consensus/mlesac.h>
#include <pcl/sample_consensus/ransac.h>
#include <pcl/sample_consensus/rransac.h>
#include <pcl/sample_consensus/sac_model_plane.h>
#include <pcl/sample_consensus/sac_model_normal_plane.h>
#include <pcl/sample_consensus/sac_model_normal_parallel_plane.h>
using namespace pcl;
using namespace pcl::io;
using SampleConsensusModelPlanePtr = SampleConsensusModelPlane<PointXYZ>::Ptr;
using SampleConsensusModelNormalPlanePtr = SampleConsensusModelNormalPlane<PointXYZ, Normal>::Ptr;
using SampleConsensusModelNormalParallelPlanePtr = SampleConsensusModelNormalParallelPlane<PointXYZ, Normal>::Ptr;
PointCloud<PointXYZ>::Ptr cloud_ (new PointCloud<PointXYZ> ());
PointCloud<Normal>::Ptr normals_ (new PointCloud<Normal> ());
pcl::Indices indices_;
float plane_coeffs_[] = {-0.8964f, -0.5868f, -1.208f};
template <typename ModelType, typename SacType>
void verifyPlaneSac (ModelType& model,
SacType& sac,
unsigned int inlier_number = 2000,
float tol = 1e-1f,
float refined_tol = 1e-1f,
float proj_tol = 1e-3f)
{
// Algorithm tests
bool result = sac.computeModel ();
ASSERT_TRUE (result);
pcl::Indices sample;
sac.getModel (sample);
EXPECT_EQ (3, sample.size ());
pcl::Indices inliers;
sac.getInliers (inliers);
EXPECT_LT (inlier_number, inliers.size ());
Eigen::VectorXf coeff;
sac.getModelCoefficients (coeff);
EXPECT_EQ (4, coeff.size ());
EXPECT_NEAR (plane_coeffs_[0], coeff[0] / coeff[3], tol);
EXPECT_NEAR (plane_coeffs_[1], coeff[1] / coeff[3], tol);
EXPECT_NEAR (plane_coeffs_[2], coeff[2] / coeff[3], tol);
Eigen::VectorXf coeff_refined;
model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
EXPECT_EQ (4, coeff_refined.size ());
EXPECT_NEAR (plane_coeffs_[0], coeff_refined[0] / coeff_refined[3], refined_tol);
EXPECT_NEAR (plane_coeffs_[1], coeff_refined[1] / coeff_refined[3], refined_tol);
// This test fails in Windows (VS 2010) -- not sure why yet -- relaxing the constraint from 1e-2 to 1e-1
// This test fails in MacOS too -- not sure why yet -- disabling
//EXPECT_NEAR (coeff_refined[2] / coeff_refined[3], plane_coeffs_[2], refined_tol);
// Projection tests
PointCloud<PointXYZ> proj_points;
model->projectPoints (inliers, coeff_refined, proj_points);
EXPECT_XYZ_NEAR (PointXYZ (1.1266, 0.0152, -0.0156), proj_points[20], proj_tol);
EXPECT_XYZ_NEAR (PointXYZ (1.1843, -0.0635, -0.0201), proj_points[30], proj_tol);
EXPECT_XYZ_NEAR (PointXYZ (1.0749, -0.0586, 0.0587), proj_points[50], proj_tol);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (SampleConsensusModelPlane, Base)
{
// Create a shared plane model pointer directly
SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
// Basic tests
PointCloud<PointXYZ>::ConstPtr cloud = model->getInputCloud ();
ASSERT_EQ (cloud_->size (), cloud->size ());
model->setInputCloud (cloud);
cloud = model->getInputCloud ();
ASSERT_EQ (cloud_->size (), cloud->size ());
auto indices = model->getIndices ();
ASSERT_EQ (indices_.size (), indices->size ());
model->setIndices (indices_);
indices = model->getIndices ();
ASSERT_EQ (indices_.size (), indices->size ());
model->setIndices (indices);
indices = model->getIndices ();
ASSERT_EQ (indices_.size (), indices->size ());
}
TEST (SampleConsensusModelPlane, SampleValidationPointsCollinear)
{
PointCloud<PointXYZ> cloud;
cloud.resize (4);
// The "cheat point" makes it possible to find a set of valid samples and
// therefore avoids the log message of an unsuccessful sample validation
// being printed a 1000 times without any chance of success.
// The order is chosen such that with a known, fixed rng-state/-seed all
// validation steps are actually exercised.
const pcl::index_t firstCollinearPointIndex = 0;
const pcl::index_t secondCollinearPointIndex = 1;
const pcl::index_t thirdCollinearPointIndex = 2;
const pcl::index_t cheatPointIndex = 3;
cloud[firstCollinearPointIndex].getVector3fMap () << 0.1f, 0.1f, 0.1f;
cloud[secondCollinearPointIndex].getVector3fMap () << 0.2f, 0.2f, 0.2f;
cloud[thirdCollinearPointIndex].getVector3fMap () << 0.3f, 0.3f, 0.3f;
cloud[cheatPointIndex].getVector3fMap () << 0.0f, 0.1f, 0.0f; // <-- cheat point
// Create a shared line model pointer directly and explicitly disable the
// random seed for the reasons mentioned above
SampleConsensusModelPlanePtr model (
new SampleConsensusModelPlane<PointXYZ> (cloud.makeShared (), /* random = */ false));
// Algorithm tests
pcl::Indices samples;
int iterations = 0;
model->getSamples(iterations, samples);
EXPECT_EQ (samples.size(), 3);
// The "cheat point" has to be part of the sample, otherwise something is wrong.
// The best option would be to assert on stderr output here, but that doesn't
// seem to be that simple.
EXPECT_TRUE (std::find(samples.begin (), samples.end (), cheatPointIndex) != samples.end ());
pcl::Indices forcedSamples = {firstCollinearPointIndex, secondCollinearPointIndex, thirdCollinearPointIndex};
Eigen::VectorXf modelCoefficients;
EXPECT_FALSE (model->computeModelCoefficients (forcedSamples, modelCoefficients));
}
TEST (SampleConsensusModelPlane, SampleValidationPointsValid)
{
PointCloud<PointXYZ> cloud;
cloud.resize (3);
cloud[0].getVector3fMap () << 0.1f, 0.0f, 0.0f;
cloud[1].getVector3fMap () << 0.0f, 0.1f, 0.0f;
cloud[2].getVector3fMap () << 0.0f, 0.0f, 0.1f;
// Create a shared line model pointer directly
SampleConsensusModelPlanePtr model (
new SampleConsensusModelPlane<PointXYZ> (cloud.makeShared ()));
// Algorithm tests
pcl::Indices samples;
int iterations = 0;
model->getSamples(iterations, samples);
EXPECT_EQ (samples.size(), 3);
Eigen::VectorXf modelCoefficients;
EXPECT_TRUE (model->computeModelCoefficients (samples, modelCoefficients));
}
TEST (SampleConsensusModelPlane, SampleValidationNotEnoughSamples)
{
PointCloud<PointXYZ> cloud;
cloud.resize (2);
cloud[0].getVector3fMap () << 0.1f, 0.0f, 0.0f;
cloud[1].getVector3fMap () << 0.0f, 0.1f, 0.0f;
std::vector<pcl::Indices> testIndices = {{}, {0,}, {0, 1}};
for( const auto& indices : testIndices) {
PointCloud<PointXYZ> subCloud {cloud, indices};
// Create a shared line model pointer directly
SampleConsensusModelPlanePtr model (
new SampleConsensusModelPlane<PointXYZ> (subCloud.makeShared ()));
// Algorithm tests
pcl::Indices samples;
int iterations = 0;
model->getSamples(iterations, samples);
EXPECT_EQ (samples.size(), 0);
Eigen::VectorXf modelCoefficients;
EXPECT_FALSE (model->computeModelCoefficients (indices, modelCoefficients));
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (SampleConsensusModelPlane, RANSAC)
{
srand (0);
// Create a shared plane model pointer directly
SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
// Create the RANSAC object
RandomSampleConsensus<PointXYZ> sac (model, 0.03);
verifyPlaneSac (model, sac);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (SampleConsensusModelPlane, LMedS)
{
srand (0);
// Create a shared plane model pointer directly
SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
// Create the LMedS object
LeastMedianSquares<PointXYZ> sac (model, 0.03);
verifyPlaneSac (model, sac);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (SampleConsensusModelPlane, MSAC)
{
srand (0);
// Create a shared plane model pointer directly
SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
// Create the MSAC object
MEstimatorSampleConsensus<PointXYZ> sac (model, 0.03);
verifyPlaneSac (model, sac);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (SampleConsensusModelPlane, RRANSAC)
{
srand (0);
// Create a shared plane model pointer directly
SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
// Create the RRANSAC object
RandomizedRandomSampleConsensus<PointXYZ> sac (model, 0.03);
sac.setFractionNrPretest (0.1);
ASSERT_EQ (0.1, sac.getFractionNrPretest ());
verifyPlaneSac (model, sac, 600, 1.0f, 1.0f, 0.01f);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (SampleConsensusModelPlane, MLESAC)
{
srand (0);
// Create a shared plane model pointer directly
SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
// Create the MSAC object
MaximumLikelihoodSampleConsensus<PointXYZ> sac (model, 0.03);
verifyPlaneSac (model, sac, 1000, 0.3f, 0.2f, 0.01f);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (SampleConsensusModelPlane, RMSAC)
{
srand (0);
// Create a shared plane model pointer directly
SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
// Create the RMSAC object
RandomizedMEstimatorSampleConsensus<PointXYZ> sac (model, 0.03);
sac.setFractionNrPretest (10.0);
ASSERT_EQ (10.0, sac.getFractionNrPretest ());
verifyPlaneSac (model, sac, 600, 1.0f, 1.0f, 0.01f);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (SampleConsensusModelNormalPlane, RANSAC)
{
srand (0);
// Create a shared plane model pointer directly
SampleConsensusModelNormalPlanePtr model (new SampleConsensusModelNormalPlane<PointXYZ, Normal> (cloud_));
model->setInputNormals (normals_);
model->setNormalDistanceWeight (0.01);
// Create the RANSAC object
RandomSampleConsensus<PointXYZ> sac (model, 0.03);
verifyPlaneSac (model, sac);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (SampleConsensusModelNormalParallelPlane, RANSAC)
{
srand (0);
// Use a custom point cloud for these tests until we need something better
PointCloud<PointXYZ> cloud;
PointCloud<Normal> normals;
cloud.resize (10);
normals.resize (10);
for (std::size_t idx = 0; idx < cloud.size (); ++idx)
{
cloud[idx].x = static_cast<float> ((rand () % 200) - 100);
cloud[idx].y = static_cast<float> ((rand () % 200) - 100);
cloud[idx].z = 0.0f;
normals[idx].normal_x = 0.0f;
normals[idx].normal_y = 0.0f;
normals[idx].normal_z = 1.0f;
}
// Create a shared plane model pointer directly
SampleConsensusModelNormalParallelPlanePtr model (new SampleConsensusModelNormalParallelPlane<PointXYZ, Normal> (cloud.makeShared ()));
model->setInputNormals (normals.makeShared ());
const float max_angle_rad = 0.01f;
const float angle_eps = 0.001f;
model->setEpsAngle (max_angle_rad);
// Test true axis
{
model->setAxis (Eigen::Vector3f (0, 0, 1));
RandomSampleConsensus<PointXYZ> sac (model, 0.03);
sac.computeModel();
pcl::Indices inliers;
sac.getInliers (inliers);
ASSERT_EQ (cloud.size (), inliers.size ());
}
// test axis slightly in valid range
{
model->setAxis (Eigen::Vector3f (0, std::sin (max_angle_rad * (1 - angle_eps)), std::cos (max_angle_rad * (1 - angle_eps))));
RandomSampleConsensus<PointXYZ> sac (model, 0.03);
sac.computeModel ();
pcl::Indices inliers;
sac.getInliers (inliers);
ASSERT_EQ (cloud.size (), inliers.size ());
}
// test axis slightly out of valid range
{
model->setAxis (Eigen::Vector3f (0, std::sin (max_angle_rad * (1 + angle_eps)), std::cos (max_angle_rad * (1 + angle_eps))));
RandomSampleConsensus<PointXYZ> sac (model, 0.03);
sac.computeModel ();
pcl::Indices inliers;
sac.getInliers (inliers);
ASSERT_EQ (0, inliers.size ());
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT>
class SampleConsensusModelPlaneTest : private SampleConsensusModelPlane<PointT>
{
public:
using SampleConsensusModelPlane<PointT>::SampleConsensusModelPlane;
using SampleConsensusModelPlane<PointT>::countWithinDistanceStandard;
#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
using SampleConsensusModelPlane<PointT>::countWithinDistanceSSE;
#endif
#if defined (__AVX__) && defined (__AVX2__)
using SampleConsensusModelPlane<PointT>::countWithinDistanceAVX;
#endif
};
TEST (SampleConsensusModelPlane, SIMD_countWithinDistance) // Test if all countWithinDistance implementations return the same value
{
const auto seed = static_cast<unsigned> (std::time (nullptr));
srand (seed);
for (size_t i=0; i<100; i++) // Run as often as you like
{
// Generate a cloud with 1000 random points
PointCloud<PointXYZ> cloud;
pcl::Indices indices;
cloud.resize (1000);
for (std::size_t idx = 0; idx < cloud.size (); ++idx)
{
cloud[idx].x = 2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0;
cloud[idx].y = 2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0;
cloud[idx].z = 2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0;
if (rand () % 2 == 0)
{
indices.push_back (static_cast<int> (idx));
}
}
SampleConsensusModelPlaneTest<PointXYZ> model (cloud.makeShared (), indices, true);
// Generate random model parameters
Eigen::VectorXf model_coefficients(4);
model_coefficients << 2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0,
2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0,
2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0, 0.0;
model_coefficients.normalize ();
model_coefficients(3) = 2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0; // Last parameter
const double threshold = 0.1 * static_cast<double> (rand ()) / RAND_MAX; // threshold in [0; 0.1]
// The number of inliers is usually somewhere between 0 and 100
const auto res_standard = model.countWithinDistanceStandard (model_coefficients, threshold); // Standard
PCL_DEBUG ("seed=%lu, i=%lu, model=(%f, %f, %f, %f), threshold=%f, res_standard=%lu\n", seed, i,
model_coefficients(0), model_coefficients(1), model_coefficients(2), model_coefficients(3), threshold, res_standard);
#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
const auto res_sse = model.countWithinDistanceSSE (model_coefficients, threshold); // SSE
ASSERT_EQ (res_standard, res_sse);
#endif
#if defined (__AVX__) && defined (__AVX2__)
const auto res_avx = model.countWithinDistanceAVX (model_coefficients, threshold); // AVX
ASSERT_EQ (res_standard, res_avx);
#endif
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename PointNT>
class SampleConsensusModelNormalPlaneTest : private SampleConsensusModelNormalPlane<PointT, PointNT>
{
public:
using SampleConsensusModelNormalPlane<PointT, PointNT>::SampleConsensusModelNormalPlane;
using SampleConsensusModelNormalPlane<PointT, PointNT>::setNormalDistanceWeight;
using SampleConsensusModelNormalPlane<PointT, PointNT>::setInputNormals;
using SampleConsensusModelNormalPlane<PointT, PointNT>::countWithinDistanceStandard;
#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
using SampleConsensusModelNormalPlane<PointT, PointNT>::countWithinDistanceSSE;
#endif
#if defined (__AVX__) && defined (__AVX2__)
using SampleConsensusModelNormalPlane<PointT, PointNT>::countWithinDistanceAVX;
#endif
};
TEST (SampleConsensusModelNormalPlane, SIMD_countWithinDistance) // Test if all countWithinDistance implementations return the same value
{
const auto seed = static_cast<unsigned> (std::time (nullptr));
srand (seed);
for (size_t i=0; i<1000; i++) // Run as often as you like
{
// Generate a cloud with 10000 random points
PointCloud<PointXYZ> cloud;
PointCloud<Normal> normal_cloud;
pcl::Indices indices;
cloud.resize (10000);
normal_cloud.resize (10000);
for (std::size_t idx = 0; idx < cloud.size (); ++idx)
{
cloud[idx].x = 2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0;
cloud[idx].y = 2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0;
cloud[idx].z = 2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0;
const double a = 2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0;
const double b = 2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0;
const double c = 2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0;
const double factor = 1.0 / sqrt(a * a + b * b + c * c);
normal_cloud[idx].normal[0] = a * factor;
normal_cloud[idx].normal[1] = b * factor;
normal_cloud[idx].normal[2] = c * factor;
if (rand () % 4 != 0)
{
indices.push_back (static_cast<int> (idx));
}
}
SampleConsensusModelNormalPlaneTest<PointXYZ, Normal> model (cloud.makeShared (), indices, true);
const double normal_distance_weight = 0.3 * static_cast<double> (rand ()) / RAND_MAX; // in [0; 0.3]
model.setNormalDistanceWeight (normal_distance_weight);
model.setInputNormals (normal_cloud.makeShared ());
// Generate random model parameters
Eigen::VectorXf model_coefficients(4);
model_coefficients << 2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0,
2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0,
2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0, 0.0;
model_coefficients.normalize ();
model_coefficients(3) = 2.0 * static_cast<float> (rand ()) / RAND_MAX - 1.0; // Last parameter
const double threshold = 0.1 * static_cast<double> (rand ()) / RAND_MAX; // threshold in [0; 0.1]
// The number of inliers is usually somewhere between 0 and 100
const auto res_standard = model.countWithinDistanceStandard (model_coefficients, threshold); // Standard
pcl::utils::ignore(res_standard);
#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
const auto res_sse = model.countWithinDistanceSSE (model_coefficients, threshold); // SSE
EXPECT_LE ((res_standard > res_sse ? res_standard - res_sse : res_sse - res_standard), 2u) << "seed=" << seed << ", i=" << i
<< ", model=(" << model_coefficients(0) << ", " << model_coefficients(1) << ", " << model_coefficients(2) << ", " << model_coefficients(3)
<< "), threshold=" << threshold << ", normal_distance_weight=" << normal_distance_weight << ", res_standard=" << res_standard << std::endl;
#endif
#if defined (__AVX__) && defined (__AVX2__)
const auto res_avx = model.countWithinDistanceAVX (model_coefficients, threshold); // AVX
EXPECT_LE ((res_standard > res_avx ? res_standard - res_avx : res_avx - res_standard), 2u) << "seed=" << seed << ", i=" << i
<< ", model=(" << model_coefficients(0) << ", " << model_coefficients(1) << ", " << model_coefficients(2) << ", " << model_coefficients(3)
<< "), threshold=" << threshold << ", normal_distance_weight=" << normal_distance_weight << ", res_standard=" << res_standard << std::endl;
#endif
}
}
TEST (SampleConsensusModelPlane, OptimizeFarFromOrigin)
{ // Test if the model can successfully optimize a plane that is far from the origin
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
Eigen::Vector3d x(-0.435197968, 0.598251061, -0.672828654);
Eigen::Vector3d y(-0.547340139, 0.417556627, 0.725303548);
Eigen::Vector3d z( 0.714857680, 0.683916759, 0.145727023); // This is the normal of the plane
Eigen::Vector3d center(7380.86467, -8350.60056617, 4324.22814107);
for(double i=-0.5; i<0.5; i+=0.01)
for(double j=-0.5; j<0.5; j+=0.01) {
Eigen::Vector3d p = center + i*x + j*y;
cloud->emplace_back(p[0], p[1], p[2]);
}
pcl::SampleConsensusModelPlane<pcl::PointXYZ> model(cloud, true);
pcl::Indices inliers;
for(std::size_t i=0; i<cloud->size(); ++i) inliers.push_back(i);
Eigen::VectorXf coeffs(4); // Doesn't have to be initialized, the function doesn't use them
Eigen::VectorXf optimized_coeffs(4);
model.optimizeModelCoefficients(inliers, coeffs, optimized_coeffs);
#ifndef __i386__
EXPECT_NEAR(optimized_coeffs[0], z[0], 5e-6);
EXPECT_NEAR(optimized_coeffs[1], z[1], 5e-6);
EXPECT_NEAR(optimized_coeffs[2], z[2], 5e-6);
EXPECT_NEAR(optimized_coeffs[3], -z.dot(center), 5e-2);
#endif
}
int
main (int argc, char** argv)
{
if (argc < 2)
{
std::cerr << "No test file given. Please download `sac_plane_test.pcd` and pass its path to the test." << std::endl;
return (-1);
}
// Load a standard PCD file from disk
pcl::PCLPointCloud2 cloud_blob;
if (loadPCDFile (argv[1], cloud_blob) < 0)
{
std::cerr << "Failed to read test file. Please download `sac_plane_test.pcd` and pass its path to the test." << std::endl;
return (-1);
}
fromPCLPointCloud2 (cloud_blob, *cloud_);
fromPCLPointCloud2 (cloud_blob, *normals_);
indices_.resize (cloud_->size ());
for (std::size_t i = 0; i < indices_.size (); ++i) { indices_[i] = int (i); }
testing::InitGoogleTest (&argc, argv);
return (RUN_ALL_TESTS ());
}
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