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test_sample_consensus.cpp
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/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2010-2012, Willow Garage, 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/io/pcd_io.h>
#include "boost.h"
#include <pcl/sample_consensus/sac.h>
#include <pcl/sample_consensus/lmeds.h>
#include <pcl/sample_consensus/ransac.h>
#include <pcl/sample_consensus/rransac.h>
#include <pcl/sample_consensus/msac.h>
#include <pcl/sample_consensus/rmsac.h>
#include <pcl/sample_consensus/mlesac.h>
#include <pcl/sample_consensus/sac_model.h>
#include <pcl/sample_consensus/sac_model_plane.h>
#include <pcl/sample_consensus/sac_model_sphere.h>
#include <pcl/sample_consensus/sac_model_cone.h>
#include <pcl/sample_consensus/sac_model_cylinder.h>
#include <pcl/sample_consensus/sac_model_circle.h>
#include <pcl/sample_consensus/sac_model_circle3d.h>
#include <pcl/sample_consensus/sac_model_line.h>
#include <pcl/sample_consensus/sac_model_normal_plane.h>
#include <pcl/sample_consensus/sac_model_normal_sphere.h>
#include <pcl/sample_consensus/sac_model_parallel_plane.h>
#include <pcl/sample_consensus/sac_model_normal_parallel_plane.h>
#include <pcl/features/normal_3d.h>
using namespace pcl;
using namespace pcl::io;
using namespace std;
typedef SampleConsensusModel<PointXYZ>::Ptr SampleConsensusModelPtr;
typedef SampleConsensusModelPlane<PointXYZ>::Ptr SampleConsensusModelPlanePtr;
typedef SampleConsensusModelSphere<PointXYZ>::Ptr SampleConsensusModelSpherePtr;
typedef SampleConsensusModelCylinder<PointXYZ, Normal>::Ptr SampleConsensusModelCylinderPtr;
typedef SampleConsensusModelCone<PointXYZ, Normal>::Ptr SampleConsensusModelConePtr;
typedef SampleConsensusModelCircle2D<PointXYZ>::Ptr SampleConsensusModelCircle2DPtr;
typedef SampleConsensusModelCircle3D<PointXYZ>::Ptr SampleConsensusModelCircle3DPtr;
typedef SampleConsensusModelLine<PointXYZ>::Ptr SampleConsensusModelLinePtr;
typedef SampleConsensusModelNormalPlane<PointXYZ, Normal>::Ptr SampleConsensusModelNormalPlanePtr;
typedef SampleConsensusModelNormalSphere<PointXYZ, Normal>::Ptr SampleConsensusModelNormalSpherePtr;
typedef SampleConsensusModelParallelPlane<PointXYZ>::Ptr SampleConsensusModelParallelPlanePtr;
typedef SampleConsensusModelNormalParallelPlane<PointXYZ, Normal>::Ptr SampleConsensusModelNormalParallelPlanePtr;
PointCloud<PointXYZ>::Ptr cloud_ (new PointCloud<PointXYZ> ());
PointCloud<Normal>::Ptr normals_ (new PointCloud<Normal> ());
vector<int> 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_EQ (result, true);
std::vector<int> sample;
sac.getModel (sample);
EXPECT_EQ (int (sample.size ()), 3);
std::vector<int> inliers;
sac.getInliers (inliers);
EXPECT_GE (int (inliers.size ()), inlier_number);
Eigen::VectorXf coeff;
sac.getModelCoefficients (coeff);
EXPECT_EQ (int (coeff.size ()), 4);
EXPECT_NEAR (coeff[0]/coeff[3], plane_coeffs_[0], tol);
EXPECT_NEAR (coeff[1]/coeff[3], plane_coeffs_[1], tol);
EXPECT_NEAR (coeff[2]/coeff[3], plane_coeffs_[2], tol);
Eigen::VectorXf coeff_refined;
model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
EXPECT_EQ (int (coeff_refined.size ()), 4);
EXPECT_NEAR (coeff_refined[0]/coeff_refined[3], plane_coeffs_[0], refined_tol);
EXPECT_NEAR (coeff_refined[1]/coeff_refined[3], plane_coeffs_[1], 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_NEAR (proj_points.points[20].x, 1.1266, proj_tol);
EXPECT_NEAR (proj_points.points[20].y, 0.0152, proj_tol);
EXPECT_NEAR (proj_points.points[20].z, -0.0156, proj_tol);
EXPECT_NEAR (proj_points.points[30].x, 1.1843, proj_tol);
EXPECT_NEAR (proj_points.points[30].y, -0.0635, proj_tol);
EXPECT_NEAR (proj_points.points[30].z, -0.0201, proj_tol);
EXPECT_NEAR (proj_points.points[50].x, 1.0749, proj_tol);
EXPECT_NEAR (proj_points.points[50].y, -0.0586, proj_tol);
EXPECT_NEAR (proj_points.points[50].z, 0.0587, refined_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->points.size (), cloud_->points.size ());
model->setInputCloud (cloud);
cloud = model->getInputCloud ();
ASSERT_EQ (cloud->points.size (), cloud_->points.size ());
boost::shared_ptr<vector<int> > 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 (RANSAC, Base)
{
// Create a shared plane model pointer directly
SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
// Create the RANSAC object
RandomSampleConsensus<PointXYZ> sac (model, 0.03);
// Basic tests
ASSERT_EQ (sac.getDistanceThreshold (), 0.03);
sac.setDistanceThreshold (0.03);
ASSERT_EQ (sac.getDistanceThreshold (), 0.03);
sac.setProbability (0.99);
ASSERT_EQ (sac.getProbability (), 0.99);
sac.setMaxIterations (10000);
ASSERT_EQ (sac.getMaxIterations (), 10000);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (RANSAC, SampleConsensusModelPlane)
{
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 (LMedS, SampleConsensusModelPlane)
{
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 (MSAC, SampleConsensusModelPlane)
{
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 (RRANSAC, SampleConsensusModelPlane)
{
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 (10.0);
ASSERT_EQ (sac.getFractionNrPretest (), 10.0);
verifyPlaneSac(model, sac, 600, 1.0f, 1.0f, 0.01f);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (RMSAC, SampleConsensusModelPlane)
{
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 (sac.getFractionNrPretest (), 10.0);
verifyPlaneSac(model, sac, 600, 1.0f, 1.0f, 0.01f);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (RANSAC, SampleConsensusModelNormalParallelPlane)
{
srand (0);
// Use a custom point cloud for these tests until we need something better
PointCloud<PointXYZ> cloud;
PointCloud<Normal> normals;
cloud.points.resize (10);
normals.resize (10);
for (unsigned idx = 0; idx < cloud.size (); ++idx)
{
cloud.points[idx].x = static_cast<float> ((rand () % 200) - 100);
cloud.points[idx].y = static_cast<float> ((rand () % 200) - 100);
cloud.points[idx].z = 0.0f;
normals.points[idx].normal_x = 0.0f;
normals.points[idx].normal_y = 0.0f;
normals.points[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();
std::vector<int> inliers;
sac.getInliers (inliers);
ASSERT_EQ (inliers.size (), cloud.size ());
}
// test axis slightly in valid range
{
model->setAxis (Eigen::Vector3f(0, sin(max_angle_rad * (1 - angle_eps)), cos(max_angle_rad * (1 - angle_eps))));
RandomSampleConsensus<PointXYZ> sac (model, 0.03);
sac.computeModel();
std::vector<int> inliers;
sac.getInliers (inliers);
ASSERT_EQ (inliers.size (), cloud.size ());
}
// test axis slightly out of valid range
{
model->setAxis (Eigen::Vector3f(0, sin(max_angle_rad * (1 + angle_eps)), cos(max_angle_rad * (1 + angle_eps))));
RandomSampleConsensus<PointXYZ> sac (model, 0.03);
sac.computeModel();
std::vector<int> inliers;
sac.getInliers (inliers);
ASSERT_EQ (inliers.size (), 0);
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (MLESAC, SampleConsensusModelPlane)
{
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 (RANSAC, SampleConsensusModelSphere)
{
srand (0);
// Use a custom point cloud for these tests until we need something better
PointCloud<PointXYZ> cloud;
cloud.points.resize (10);
cloud.points[0].x = 1.7068f; cloud.points[0].y = 1.0684f; cloud.points[0].z = 2.2147f;
cloud.points[1].x = 2.4708f; cloud.points[1].y = 2.3081f; cloud.points[1].z = 1.1736f;
cloud.points[2].x = 2.7609f; cloud.points[2].y = 1.9095f; cloud.points[2].z = 1.3574f;
cloud.points[3].x = 2.8016f; cloud.points[3].y = 1.6704f; cloud.points[3].z = 1.5009f;
cloud.points[4].x = 1.8517f; cloud.points[4].y = 2.0276f; cloud.points[4].z = 1.0112f;
cloud.points[5].x = 1.8726f; cloud.points[5].y = 1.3539f; cloud.points[5].z = 2.7523f;
cloud.points[6].x = 2.5179f; cloud.points[6].y = 2.3218f; cloud.points[6].z = 1.2074f;
cloud.points[7].x = 2.4026f; cloud.points[7].y = 2.5114f; cloud.points[7].z = 2.7588f;
cloud.points[8].x = 2.6999f; cloud.points[8].y = 2.5606f; cloud.points[8].z = 1.5571f;
cloud.points[9].x = 0.0f; cloud.points[9].y = 0.0f; cloud.points[9].z = 0.0f;
// Create a shared sphere model pointer directly
SampleConsensusModelSpherePtr model (new SampleConsensusModelSphere<PointXYZ> (cloud.makeShared ()));
// Create the RANSAC object
RandomSampleConsensus<PointXYZ> sac (model, 0.03);
// Algorithm tests
bool result = sac.computeModel ();
ASSERT_EQ (result, true);
std::vector<int> sample;
sac.getModel (sample);
EXPECT_EQ (int (sample.size ()), 4);
std::vector<int> inliers;
sac.getInliers (inliers);
EXPECT_EQ (int (inliers.size ()), 9);
Eigen::VectorXf coeff;
sac.getModelCoefficients (coeff);
EXPECT_EQ (int (coeff.size ()), 4);
EXPECT_NEAR (coeff[0]/coeff[3], 2, 1e-2);
EXPECT_NEAR (coeff[1]/coeff[3], 2, 1e-2);
EXPECT_NEAR (coeff[2]/coeff[3], 2, 1e-2);
Eigen::VectorXf coeff_refined;
model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
EXPECT_EQ (int (coeff_refined.size ()), 4);
EXPECT_NEAR (coeff_refined[0]/coeff_refined[3], 2, 1e-2);
EXPECT_NEAR (coeff_refined[1]/coeff_refined[3], 2, 1e-2);
EXPECT_NEAR (coeff_refined[2]/coeff_refined[3], 2, 1e-2);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (RANSAC, SampleConsensusModelNormalSphere)
{
srand (0);
// Use a custom point cloud for these tests until we need something better
PointCloud<PointXYZ> cloud;
PointCloud<Normal> normals;
cloud.points.resize (27); normals.points.resize (27);
cloud.points[0].x = -0.014695f; cloud.points[0].y = 0.009549f; cloud.points[0].z = 0.954775f;
cloud.points[1].x = 0.014695f; cloud.points[1].y = 0.009549f; cloud.points[1].z = 0.954775f;
cloud.points[2].x = -0.014695f; cloud.points[2].y = 0.040451f; cloud.points[2].z = 0.954775f;
cloud.points[3].x = 0.014695f; cloud.points[3].y = 0.040451f; cloud.points[3].z = 0.954775f;
cloud.points[4].x = -0.009082f; cloud.points[4].y = -0.015451f; cloud.points[4].z = 0.972049f;
cloud.points[5].x = 0.009082f; cloud.points[5].y = -0.015451f; cloud.points[5].z = 0.972049f;
cloud.points[6].x = -0.038471f; cloud.points[6].y = 0.009549f; cloud.points[6].z = 0.972049f;
cloud.points[7].x = 0.038471f; cloud.points[7].y = 0.009549f; cloud.points[7].z = 0.972049f;
cloud.points[8].x = -0.038471f; cloud.points[8].y = 0.040451f; cloud.points[8].z = 0.972049f;
cloud.points[9].x = 0.038471f; cloud.points[9].y = 0.040451f; cloud.points[9].z = 0.972049f;
cloud.points[10].x = -0.009082f; cloud.points[10].y = 0.065451f; cloud.points[10].z = 0.972049f;
cloud.points[11].x = 0.009082f; cloud.points[11].y = 0.065451f; cloud.points[11].z = 0.972049f;
cloud.points[12].x = -0.023776f; cloud.points[12].y = -0.015451f; cloud.points[12].z = 0.982725f;
cloud.points[13].x = 0.023776f; cloud.points[13].y = -0.015451f; cloud.points[13].z = 0.982725f;
cloud.points[14].x = -0.023776f; cloud.points[14].y = 0.065451f; cloud.points[14].z = 0.982725f;
cloud.points[15].x = 0.023776f; cloud.points[15].y = 0.065451f; cloud.points[15].z = 0.982725f;
cloud.points[16].x = -0.000000f; cloud.points[16].y = -0.025000f; cloud.points[16].z = 1.000000f;
cloud.points[17].x = 0.000000f; cloud.points[17].y = -0.025000f; cloud.points[17].z = 1.000000f;
cloud.points[18].x = -0.029389f; cloud.points[18].y = -0.015451f; cloud.points[18].z = 1.000000f;
cloud.points[19].x = 0.029389f; cloud.points[19].y = -0.015451f; cloud.points[19].z = 1.000000f;
cloud.points[20].x = -0.047553f; cloud.points[20].y = 0.009549f; cloud.points[20].z = 1.000000f;
cloud.points[21].x = 0.047553f; cloud.points[21].y = 0.009549f; cloud.points[21].z = 1.000000f;
cloud.points[22].x = -0.047553f; cloud.points[22].y = 0.040451f; cloud.points[22].z = 1.000000f;
cloud.points[23].x = 0.047553f; cloud.points[23].y = 0.040451f; cloud.points[23].z = 1.000000f;
cloud.points[24].x = -0.029389f; cloud.points[24].y = 0.065451f; cloud.points[24].z = 1.000000f;
cloud.points[25].x = 0.029389f; cloud.points[25].y = 0.065451f; cloud.points[25].z = 1.000000f;
cloud.points[26].x = 0.000000f; cloud.points[26].y = 0.075000f; cloud.points[26].z = 1.000000f;
normals.points[0].normal[0] = -0.293893f; normals.points[0].normal[1] = -0.309017f; normals.points[0].normal[2] = -0.904509f;
normals.points[1].normal[0] = 0.293893f; normals.points[1].normal[1] = -0.309017f; normals.points[1].normal[2] = -0.904508f;
normals.points[2].normal[0] = -0.293893f; normals.points[2].normal[1] = 0.309017f; normals.points[2].normal[2] = -0.904509f;
normals.points[3].normal[0] = 0.293893f; normals.points[3].normal[1] = 0.309017f; normals.points[3].normal[2] = -0.904508f;
normals.points[4].normal[0] = -0.181636f; normals.points[4].normal[1] = -0.809017f; normals.points[4].normal[2] = -0.559017f;
normals.points[5].normal[0] = 0.181636f; normals.points[5].normal[1] = -0.809017f; normals.points[5].normal[2] = -0.559017f;
normals.points[6].normal[0] = -0.769421f; normals.points[6].normal[1] = -0.309017f; normals.points[6].normal[2] = -0.559017f;
normals.points[7].normal[0] = 0.769421f; normals.points[7].normal[1] = -0.309017f; normals.points[7].normal[2] = -0.559017f;
normals.points[8].normal[0] = -0.769421f; normals.points[8].normal[1] = 0.309017f; normals.points[8].normal[2] = -0.559017f;
normals.points[9].normal[0] = 0.769421f; normals.points[9].normal[1] = 0.309017f; normals.points[9].normal[2] = -0.559017f;
normals.points[10].normal[0] = -0.181636f; normals.points[10].normal[1] = 0.809017f; normals.points[10].normal[2] = -0.559017f;
normals.points[11].normal[0] = 0.181636f; normals.points[11].normal[1] = 0.809017f; normals.points[11].normal[2] = -0.559017f;
normals.points[12].normal[0] = -0.475528f; normals.points[12].normal[1] = -0.809017f; normals.points[12].normal[2] = -0.345491f;
normals.points[13].normal[0] = 0.475528f; normals.points[13].normal[1] = -0.809017f; normals.points[13].normal[2] = -0.345491f;
normals.points[14].normal[0] = -0.475528f; normals.points[14].normal[1] = 0.809017f; normals.points[14].normal[2] = -0.345491f;
normals.points[15].normal[0] = 0.475528f; normals.points[15].normal[1] = 0.809017f; normals.points[15].normal[2] = -0.345491f;
normals.points[16].normal[0] = -0.000000f; normals.points[16].normal[1] = -1.000000f; normals.points[16].normal[2] = 0.000000f;
normals.points[17].normal[0] = 0.000000f; normals.points[17].normal[1] = -1.000000f; normals.points[17].normal[2] = 0.000000f;
normals.points[18].normal[0] = -0.587785f; normals.points[18].normal[1] = -0.809017f; normals.points[18].normal[2] = 0.000000f;
normals.points[19].normal[0] = 0.587785f; normals.points[19].normal[1] = -0.809017f; normals.points[19].normal[2] = 0.000000f;
normals.points[20].normal[0] = -0.951057f; normals.points[20].normal[1] = -0.309017f; normals.points[20].normal[2] = 0.000000f;
normals.points[21].normal[0] = 0.951057f; normals.points[21].normal[1] = -0.309017f; normals.points[21].normal[2] = 0.000000f;
normals.points[22].normal[0] = -0.951057f; normals.points[22].normal[1] = 0.309017f; normals.points[22].normal[2] = 0.000000f;
normals.points[23].normal[0] = 0.951057f; normals.points[23].normal[1] = 0.309017f; normals.points[23].normal[2] = 0.000000f;
normals.points[24].normal[0] = -0.587785f; normals.points[24].normal[1] = 0.809017f; normals.points[24].normal[2] = 0.000000f;
normals.points[25].normal[0] = 0.587785f; normals.points[25].normal[1] = 0.809017f; normals.points[25].normal[2] = 0.000000f;
normals.points[26].normal[0] = 0.000000f; normals.points[26].normal[1] = 1.000000f; normals.points[26].normal[2] = 0.000000f;
// Create a shared sphere model pointer directly
SampleConsensusModelNormalSpherePtr model (new SampleConsensusModelNormalSphere<PointXYZ, Normal> (cloud.makeShared ()));
model->setInputNormals(normals.makeShared ());
// Create the RANSAC object
RandomSampleConsensus<PointXYZ> sac (model, 0.03);
// Algorithm tests
bool result = sac.computeModel ();
ASSERT_EQ (result, true);
std::vector<int> sample;
sac.getModel (sample);
EXPECT_EQ (int (sample.size ()), 4);
std::vector<int> inliers;
sac.getInliers (inliers);
EXPECT_EQ (int (inliers.size ()), 27);
Eigen::VectorXf coeff;
sac.getModelCoefficients (coeff);
EXPECT_EQ (int (coeff.size ()), 4);
EXPECT_NEAR (coeff[0], 0.0, 1e-2);
EXPECT_NEAR (coeff[1], 0.025, 1e-2);
EXPECT_NEAR (coeff[2], 1.0, 1e-2);
EXPECT_NEAR (coeff[3], 0.05, 1e-2);
Eigen::VectorXf coeff_refined;
model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
EXPECT_EQ (int (coeff_refined.size ()), 4);
EXPECT_NEAR (coeff_refined[0], 0.0, 1e-2);
EXPECT_NEAR (coeff_refined[1], 0.025, 1e-2);
EXPECT_NEAR (coeff_refined[2], 1.0, 1e-2);
EXPECT_NEAR (coeff_refined[3], 0.05, 1e-2);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (RANSAC, SampleConsensusModelCone)
{
srand (0);
// Use a custom point cloud for these tests until we need something better
PointCloud<PointXYZ> cloud;
PointCloud<Normal> normals;
cloud.points.resize (31); normals.points.resize (31);
cloud.points[0].x = -0.011247f; cloud.points[0].y = 0.200000f; cloud.points[0].z = 0.965384f;
cloud.points[1].x = 0.000000f; cloud.points[1].y = 0.200000f; cloud.points[1].z = 0.963603f;
cloud.points[2].x = 0.011247f; cloud.points[2].y = 0.200000f; cloud.points[2].z = 0.965384f;
cloud.points[3].x = -0.016045f; cloud.points[3].y = 0.175000f; cloud.points[3].z = 0.977916f;
cloud.points[4].x = -0.008435f; cloud.points[4].y = 0.175000f; cloud.points[4].z = 0.974038f;
cloud.points[5].x = 0.004218f; cloud.points[5].y = 0.175000f; cloud.points[5].z = 0.973370f;
cloud.points[6].x = 0.016045f; cloud.points[6].y = 0.175000f; cloud.points[6].z = 0.977916f;
cloud.points[7].x = -0.025420f; cloud.points[7].y = 0.200000f; cloud.points[7].z = 0.974580f;
cloud.points[8].x = 0.025420f; cloud.points[8].y = 0.200000f; cloud.points[8].z = 0.974580f;
cloud.points[9].x = -0.012710f; cloud.points[9].y = 0.150000f; cloud.points[9].z = 0.987290f;
cloud.points[10].x = -0.005624f; cloud.points[10].y = 0.150000f; cloud.points[10].z = 0.982692f;
cloud.points[11].x = 0.002812f; cloud.points[11].y = 0.150000f; cloud.points[11].z = 0.982247f;
cloud.points[12].x = 0.012710f; cloud.points[12].y = 0.150000f; cloud.points[12].z = 0.987290f;
cloud.points[13].x = -0.022084f; cloud.points[13].y = 0.175000f; cloud.points[13].z = 0.983955f;
cloud.points[14].x = 0.022084f; cloud.points[14].y = 0.175000f; cloud.points[14].z = 0.983955f;
cloud.points[15].x = -0.034616f; cloud.points[15].y = 0.200000f; cloud.points[15].z = 0.988753f;
cloud.points[16].x = 0.034616f; cloud.points[16].y = 0.200000f; cloud.points[16].z = 0.988753f;
cloud.points[17].x = -0.006044f; cloud.points[17].y = 0.125000f; cloud.points[17].z = 0.993956f;
cloud.points[18].x = 0.004835f; cloud.points[18].y = 0.125000f; cloud.points[18].z = 0.993345f;
cloud.points[19].x = -0.017308f; cloud.points[19].y = 0.150000f; cloud.points[19].z = 0.994376f;
cloud.points[20].x = 0.017308f; cloud.points[20].y = 0.150000f; cloud.points[20].z = 0.994376f;
cloud.points[21].x = -0.025962f; cloud.points[21].y = 0.175000f; cloud.points[21].z = 0.991565f;
cloud.points[22].x = 0.025962f; cloud.points[22].y = 0.175000f; cloud.points[22].z = 0.991565f;
cloud.points[23].x = -0.009099f; cloud.points[23].y = 0.125000f; cloud.points[23].z = 1.000000f;
cloud.points[24].x = 0.009099f; cloud.points[24].y = 0.125000f; cloud.points[24].z = 1.000000f;
cloud.points[25].x = -0.018199f; cloud.points[25].y = 0.150000f; cloud.points[25].z = 1.000000f;
cloud.points[26].x = 0.018199f; cloud.points[26].y = 0.150000f; cloud.points[26].z = 1.000000f;
cloud.points[27].x = -0.027298f; cloud.points[27].y = 0.175000f; cloud.points[27].z = 1.000000f;
cloud.points[28].x = 0.027298f; cloud.points[28].y = 0.175000f; cloud.points[28].z = 1.000000f;
cloud.points[29].x = -0.036397f; cloud.points[29].y = 0.200000f; cloud.points[29].z = 1.000000f;
cloud.points[30].x = 0.036397f; cloud.points[30].y = 0.200000f; cloud.points[30].z = 1.000000f;
normals.points[0].normal[0] = -0.290381f; normals.points[0].normal[1] = -0.342020f; normals.points[0].normal[2] = -0.893701f;
normals.points[1].normal[0] = 0.000000f; normals.points[1].normal[1] = -0.342020f; normals.points[1].normal[2] = -0.939693f;
normals.points[2].normal[0] = 0.290381f; normals.points[2].normal[1] = -0.342020f; normals.points[2].normal[2] = -0.893701f;
normals.points[3].normal[0] = -0.552338f; normals.points[3].normal[1] = -0.342020f; normals.points[3].normal[2] = -0.760227f;
normals.points[4].normal[0] = -0.290381f; normals.points[4].normal[1] = -0.342020f; normals.points[4].normal[2] = -0.893701f;
normals.points[5].normal[0] = 0.145191f; normals.points[5].normal[1] = -0.342020f; normals.points[5].normal[2] = -0.916697f;
normals.points[6].normal[0] = 0.552337f; normals.points[6].normal[1] = -0.342020f; normals.points[6].normal[2] = -0.760227f;
normals.points[7].normal[0] = -0.656282f; normals.points[7].normal[1] = -0.342020f; normals.points[7].normal[2] = -0.656283f;
normals.points[8].normal[0] = 0.656282f; normals.points[8].normal[1] = -0.342020f; normals.points[8].normal[2] = -0.656283f;
normals.points[9].normal[0] = -0.656283f; normals.points[9].normal[1] = -0.342020f; normals.points[9].normal[2] = -0.656282f;
normals.points[10].normal[0] = -0.290381f; normals.points[10].normal[1] = -0.342020f; normals.points[10].normal[2] = -0.893701f;
normals.points[11].normal[0] = 0.145191f; normals.points[11].normal[1] = -0.342020f; normals.points[11].normal[2] = -0.916697f;
normals.points[12].normal[0] = 0.656282f; normals.points[12].normal[1] = -0.342020f; normals.points[12].normal[2] = -0.656282f;
normals.points[13].normal[0] = -0.760228f; normals.points[13].normal[1] = -0.342020f; normals.points[13].normal[2] = -0.552337f;
normals.points[14].normal[0] = 0.760228f; normals.points[14].normal[1] = -0.342020f; normals.points[14].normal[2] = -0.552337f;
normals.points[15].normal[0] = -0.893701f; normals.points[15].normal[1] = -0.342020f; normals.points[15].normal[2] = -0.290380f;
normals.points[16].normal[0] = 0.893701f; normals.points[16].normal[1] = -0.342020f; normals.points[16].normal[2] = -0.290380f;
normals.points[17].normal[0] = -0.624162f; normals.points[17].normal[1] = -0.342020f; normals.points[17].normal[2] = -0.624162f;
normals.points[18].normal[0] = 0.499329f; normals.points[18].normal[1] = -0.342020f; normals.points[18].normal[2] = -0.687268f;
normals.points[19].normal[0] = -0.893701f; normals.points[19].normal[1] = -0.342020f; normals.points[19].normal[2] = -0.290380f;
normals.points[20].normal[0] = 0.893701f; normals.points[20].normal[1] = -0.342020f; normals.points[20].normal[2] = -0.290380f;
normals.points[21].normal[0] = -0.893701f; normals.points[21].normal[1] = -0.342020f; normals.points[21].normal[2] = -0.290381f;
normals.points[22].normal[0] = 0.893701f; normals.points[22].normal[1] = -0.342020f; normals.points[22].normal[2] = -0.290381f;
normals.points[23].normal[0] = -0.939693f; normals.points[23].normal[1] = -0.342020f; normals.points[23].normal[2] = 0.000000f;
normals.points[24].normal[0] = 0.939693f; normals.points[24].normal[1] = -0.342020f; normals.points[24].normal[2] = 0.000000f;
normals.points[25].normal[0] = -0.939693f; normals.points[25].normal[1] = -0.342020f; normals.points[25].normal[2] = 0.000000f;
normals.points[26].normal[0] = 0.939693f; normals.points[26].normal[1] = -0.342020f; normals.points[26].normal[2] = 0.000000f;
normals.points[27].normal[0] = -0.939693f; normals.points[27].normal[1] = -0.342020f; normals.points[27].normal[2] = 0.000000f;
normals.points[28].normal[0] = 0.939693f; normals.points[28].normal[1] = -0.342020f; normals.points[28].normal[2] = 0.000000f;
normals.points[29].normal[0] = -0.939693f; normals.points[29].normal[1] = -0.342020f; normals.points[29].normal[2] = 0.000000f;
normals.points[30].normal[0] = 0.939693f; normals.points[30].normal[1] = -0.342020f; normals.points[30].normal[2] = 0.000000f;
// Create a shared cylinder model pointer directly
SampleConsensusModelConePtr model (new SampleConsensusModelCone<PointXYZ, Normal> (cloud.makeShared ()));
model->setInputNormals (normals.makeShared ());
// Create the RANSAC object
RandomSampleConsensus<PointXYZ> sac (model, 0.03);
// Algorithm tests
bool result = sac.computeModel ();
ASSERT_EQ (result, true);
std::vector<int> sample;
sac.getModel (sample);
EXPECT_EQ (int (sample.size ()), 3);
std::vector<int> inliers;
sac.getInliers (inliers);
EXPECT_EQ (int (inliers.size ()), 31);
Eigen::VectorXf coeff;
sac.getModelCoefficients (coeff);
EXPECT_EQ (int (coeff.size ()), 7);
EXPECT_NEAR (coeff[0], 0, 1e-2);
EXPECT_NEAR (coeff[1], 0.1, 1e-2);
EXPECT_NEAR (coeff[6], 0.349066, 1e-2);
Eigen::VectorXf coeff_refined;
model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
EXPECT_EQ (int (coeff_refined.size ()), 7);
EXPECT_NEAR (coeff_refined[6], 0.349066 , 1e-2);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (RANSAC, SampleConsensusModelCylinder)
{
srand (0);
// Use a custom point cloud for these tests until we need something better
PointCloud<PointXYZ> cloud;
PointCloud<Normal> normals;
cloud.points.resize (20); normals.points.resize (20);
cloud.points[0].x = -0.499902f; cloud.points[0].y = 2.199701f; cloud.points[0].z = 0.000008f;
cloud.points[1].x = -0.875397f; cloud.points[1].y = 2.030177f; cloud.points[1].z = 0.050104f;
cloud.points[2].x = -0.995875f; cloud.points[2].y = 1.635973f; cloud.points[2].z = 0.099846f;
cloud.points[3].x = -0.779523f; cloud.points[3].y = 1.285527f; cloud.points[3].z = 0.149961f;
cloud.points[4].x = -0.373285f; cloud.points[4].y = 1.216488f; cloud.points[4].z = 0.199959f;
cloud.points[5].x = -0.052893f; cloud.points[5].y = 1.475973f; cloud.points[5].z = 0.250101f;
cloud.points[6].x = -0.036558f; cloud.points[6].y = 1.887591f; cloud.points[6].z = 0.299839f;
cloud.points[7].x = -0.335048f; cloud.points[7].y = 2.171994f; cloud.points[7].z = 0.350001f;
cloud.points[8].x = -0.745456f; cloud.points[8].y = 2.135528f; cloud.points[8].z = 0.400072f;
cloud.points[9].x = -0.989282f; cloud.points[9].y = 1.803311f; cloud.points[9].z = 0.449983f;
cloud.points[10].x = -0.900651f; cloud.points[10].y = 1.400701f; cloud.points[10].z = 0.500126f;
cloud.points[11].x = -0.539658f; cloud.points[11].y = 1.201468f; cloud.points[11].z = 0.550079f;
cloud.points[12].x = -0.151875f; cloud.points[12].y = 1.340951f; cloud.points[12].z = 0.599983f;
cloud.points[13].x = -0.000724f; cloud.points[13].y = 1.724373f; cloud.points[13].z = 0.649882f;
cloud.points[14].x = -0.188573f; cloud.points[14].y = 2.090983f; cloud.points[14].z = 0.699854f;
cloud.points[15].x = -0.587925f; cloud.points[15].y = 2.192257f; cloud.points[15].z = 0.749956f;
cloud.points[16].x = -0.927724f; cloud.points[16].y = 1.958846f; cloud.points[16].z = 0.800008f;
cloud.points[17].x = -0.976888f; cloud.points[17].y = 1.549655f; cloud.points[17].z = 0.849970f;
cloud.points[18].x = -0.702003f; cloud.points[18].y = 1.242707f; cloud.points[18].z = 0.899954f;
cloud.points[19].x = -0.289916f; cloud.points[19].y = 1.246296f; cloud.points[19].z = 0.950075f;
normals.points[0].normal[0] = 0.000098f; normals.points[0].normal[1] = 1.000098f; normals.points[0].normal[2] = 0.000008f;
normals.points[1].normal[0] = -0.750891f; normals.points[1].normal[1] = 0.660413f; normals.points[1].normal[2] = 0.000104f;
normals.points[2].normal[0] = -0.991765f; normals.points[2].normal[1] = -0.127949f; normals.points[2].normal[2] = -0.000154f;
normals.points[3].normal[0] = -0.558918f; normals.points[3].normal[1] = -0.829439f; normals.points[3].normal[2] = -0.000039f;
normals.points[4].normal[0] = 0.253627f; normals.points[4].normal[1] = -0.967447f; normals.points[4].normal[2] = -0.000041f;
normals.points[5].normal[0] = 0.894105f; normals.points[5].normal[1] = -0.447965f; normals.points[5].normal[2] = 0.000101f;
normals.points[6].normal[0] = 0.926852f; normals.points[6].normal[1] = 0.375543f; normals.points[6].normal[2] = -0.000161f;
normals.points[7].normal[0] = 0.329948f; normals.points[7].normal[1] = 0.943941f; normals.points[7].normal[2] = 0.000001f;
normals.points[8].normal[0] = -0.490966f; normals.points[8].normal[1] = 0.871203f; normals.points[8].normal[2] = 0.000072f;
normals.points[9].normal[0] = -0.978507f; normals.points[9].normal[1] = 0.206425f; normals.points[9].normal[2] = -0.000017f;
normals.points[10].normal[0] = -0.801227f; normals.points[10].normal[1] = -0.598534f; normals.points[10].normal[2] = 0.000126f;
normals.points[11].normal[0] = -0.079447f; normals.points[11].normal[1] = -0.996697f; normals.points[11].normal[2] = 0.000079f;
normals.points[12].normal[0] = 0.696154f; normals.points[12].normal[1] = -0.717889f; normals.points[12].normal[2] = -0.000017f;
normals.points[13].normal[0] = 0.998685f; normals.points[13].normal[1] = 0.048502f; normals.points[13].normal[2] = -0.000118f;
normals.points[14].normal[0] = 0.622933f; normals.points[14].normal[1] = 0.782133f; normals.points[14].normal[2] = -0.000146f;
normals.points[15].normal[0] = -0.175948f; normals.points[15].normal[1] = 0.984480f; normals.points[15].normal[2] = -0.000044f;
normals.points[16].normal[0] = -0.855476f; normals.points[16].normal[1] = 0.517824f; normals.points[16].normal[2] = 0.000008f;
normals.points[17].normal[0] = -0.953769f; normals.points[17].normal[1] = -0.300571f; normals.points[17].normal[2] = -0.000030f;
normals.points[18].normal[0] = -0.404035f; normals.points[18].normal[1] = -0.914700f; normals.points[18].normal[2] = -0.000046f;
normals.points[19].normal[0] = 0.420154f; normals.points[19].normal[1] = -0.907445f; normals.points[19].normal[2] = 0.000075f;
// Create a shared cylinder model pointer directly
SampleConsensusModelCylinderPtr model (new SampleConsensusModelCylinder<PointXYZ, Normal> (cloud.makeShared ()));
model->setInputNormals (normals.makeShared ());
// Create the RANSAC object
RandomSampleConsensus<PointXYZ> sac (model, 0.03);
// Algorithm tests
bool result = sac.computeModel ();
ASSERT_EQ (result, true);
std::vector<int> sample;
sac.getModel (sample);
EXPECT_EQ (int (sample.size ()), 2);
std::vector<int> inliers;
sac.getInliers (inliers);
EXPECT_EQ (int (inliers.size ()), 20);
Eigen::VectorXf coeff;
sac.getModelCoefficients (coeff);
EXPECT_EQ (int (coeff.size ()), 7);
EXPECT_NEAR (coeff[0], -0.5, 1e-3);
EXPECT_NEAR (coeff[1], 1.7, 1e-3);
EXPECT_NEAR (coeff[6], 0.5, 1e-3);
Eigen::VectorXf coeff_refined;
model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
EXPECT_EQ (int (coeff_refined.size ()), 7);
EXPECT_NEAR (coeff_refined[6], 0.5, 1e-3);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (RANSAC, SampleConsensusModelCircle2D)
{
srand (0);
// Use a custom point cloud for these tests until we need something better
PointCloud<PointXYZ> cloud;
cloud.points.resize (18);
cloud.points[0].x = 3.587751f; cloud.points[0].y = -4.190982f; cloud.points[0].z = 0.0f;
cloud.points[1].x = 3.808883f; cloud.points[1].y = -4.412265f; cloud.points[1].z = 0.0f;
cloud.points[2].x = 3.587525f; cloud.points[2].y = -5.809143f; cloud.points[2].z = 0.0f;
cloud.points[3].x = 2.999913f; cloud.points[3].y = -5.999980f; cloud.points[3].z = 0.0f;
cloud.points[4].x = 2.412224f; cloud.points[4].y = -5.809090f; cloud.points[4].z = 0.0f;
cloud.points[5].x = 2.191080f; cloud.points[5].y = -5.587682f; cloud.points[5].z = 0.0f;
cloud.points[6].x = 2.048941f; cloud.points[6].y = -5.309003f; cloud.points[6].z = 0.0f;
cloud.points[7].x = 2.000397f; cloud.points[7].y = -4.999944f; cloud.points[7].z = 0.0f;
cloud.points[8].x = 2.999953f; cloud.points[8].y = -6.000056f; cloud.points[8].z = 0.0f;
cloud.points[9].x = 2.691127f; cloud.points[9].y = -5.951136f; cloud.points[9].z = 0.0f;
cloud.points[10].x = 2.190892f; cloud.points[10].y = -5.587838f; cloud.points[10].z = 0.0f;
cloud.points[11].x = 2.048874f; cloud.points[11].y = -5.309052f; cloud.points[11].z = 0.0f;
cloud.points[12].x = 1.999990f; cloud.points[12].y = -5.000147f; cloud.points[12].z = 0.0f;
cloud.points[13].x = 2.049026f; cloud.points[13].y = -4.690918f; cloud.points[13].z = 0.0f;
cloud.points[14].x = 2.190956f; cloud.points[14].y = -4.412162f; cloud.points[14].z = 0.0f;
cloud.points[15].x = 2.412231f; cloud.points[15].y = -4.190918f; cloud.points[15].z = 0.0f;
cloud.points[16].x = 2.691027f; cloud.points[16].y = -4.049060f; cloud.points[16].z = 0.0f;
cloud.points[17].x = 2.0f; cloud.points[17].y = -3.0f; cloud.points[17].z = 0.0f;
// Create a shared 2d circle model pointer directly
SampleConsensusModelCircle2DPtr model (new SampleConsensusModelCircle2D<PointXYZ> (cloud.makeShared ()));
// Create the RANSAC object
RandomSampleConsensus<PointXYZ> sac (model, 0.03);
// Algorithm tests
bool result = sac.computeModel ();
ASSERT_EQ (result, true);
std::vector<int> sample;
sac.getModel (sample);
EXPECT_EQ (int (sample.size ()), 3);
std::vector<int> inliers;
sac.getInliers (inliers);
EXPECT_EQ (int (inliers.size ()), 17);
Eigen::VectorXf coeff;
sac.getModelCoefficients (coeff);
EXPECT_EQ (int (coeff.size ()), 3);
EXPECT_NEAR (coeff[0], 3, 1e-3);
EXPECT_NEAR (coeff[1], -5, 1e-3);
EXPECT_NEAR (coeff[2], 1, 1e-3);
Eigen::VectorXf coeff_refined;
model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
EXPECT_EQ (int (coeff_refined.size ()), 3);
EXPECT_NEAR (coeff_refined[0], 3, 1e-3);
EXPECT_NEAR (coeff_refined[1], -5, 1e-3);
EXPECT_NEAR (coeff_refined[2], 1, 1e-3);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (RANSAC, SampleConsensusModelCircle3D)
{
srand (0);
// Use a custom point cloud for these tests until we need something better
PointCloud<PointXYZ> cloud;
cloud.points.resize (20);
cloud.points[0].x = 1.0f; cloud.points[0].y = 5.0f; cloud.points[0].z = -2.9000001f;
cloud.points[1].x = 1.034202f; cloud.points[1].y = 5.0f; cloud.points[1].z = -2.9060307f;
cloud.points[2].x = 1.0642787f; cloud.points[2].y = 5.0f; cloud.points[2].z = -2.9233956f;
cloud.points[3].x = 1.0866026f; cloud.points[3].y = 5.0f; cloud.points[3].z = -2.95f;
cloud.points[4].x = 1.0984808f; cloud.points[4].y = 5.0f; cloud.points[4].z = -2.9826353f;
cloud.points[5].x = 1.0984808f; cloud.points[5].y = 5.0f; cloud.points[5].z = -3.0173647f;
cloud.points[6].x = 1.0866026f; cloud.points[6].y = 5.0f; cloud.points[6].z = -3.05f;
cloud.points[7].x = 1.0642787f; cloud.points[7].y = 5.0f; cloud.points[7].z = -3.0766044f;
cloud.points[8].x = 1.034202f; cloud.points[8].y = 5.0f; cloud.points[8].z = -3.0939693f;
cloud.points[9].x = 1.0f; cloud.points[9].y = 5.0f; cloud.points[9].z = -3.0999999f;
cloud.points[10].x = 0.96579796f; cloud.points[10].y = 5.0f; cloud.points[10].z = -3.0939693f;
cloud.points[11].x = 0.93572122f; cloud.points[11].y = 5.0f; cloud.points[11].z = -3.0766044f;
cloud.points[12].x = 0.91339743f; cloud.points[12].y = 5.0f; cloud.points[12].z = -3.05f;
cloud.points[13].x = 0.90151924f; cloud.points[13].y = 5.0f; cloud.points[13].z = -3.0173647f;
cloud.points[14].x = 0.90151924f; cloud.points[14].y = 5.0f; cloud.points[14].z = -2.9826353f;
cloud.points[15].x = 0.91339743f; cloud.points[15].y = 5.0f; cloud.points[15].z = -2.95f;
cloud.points[16].x = 0.93572122f; cloud.points[16].y = 5.0f; cloud.points[16].z = -2.9233956f;
cloud.points[17].x = 0.96579796f; cloud.points[17].y = 5.0; cloud.points[17].z = -2.9060307f;
cloud.points[18].x = 0.85000002f; cloud.points[18].y = 4.8499999f; cloud.points[18].z = -3.1500001f;
cloud.points[19].x = 1.15f; cloud.points[19].y = 5.1500001f; cloud.points[19].z = -2.8499999f;
// Create a shared 3d circle model pointer directly
SampleConsensusModelCircle3DPtr model (new SampleConsensusModelCircle3D<PointXYZ> (cloud.makeShared ()));
// Create the RANSAC object
RandomSampleConsensus<PointXYZ> sac (model, 0.03);
// Algorithm tests
bool result = sac.computeModel ();
ASSERT_EQ (result, true);
std::vector<int> sample;
sac.getModel (sample);
EXPECT_EQ (int (sample.size ()), 3);
std::vector<int> inliers;
sac.getInliers (inliers);
EXPECT_EQ (int (inliers.size ()), 18);
Eigen::VectorXf coeff;
sac.getModelCoefficients (coeff);
EXPECT_EQ (int (coeff.size ()), 7);
EXPECT_NEAR (coeff[0], 1, 1e-3);
EXPECT_NEAR (coeff[1], 5, 1e-3);
EXPECT_NEAR (coeff[2], -3, 1e-3);
EXPECT_NEAR (coeff[3],0.1, 1e-3);
EXPECT_NEAR (coeff[4], 0, 1e-3);
EXPECT_NEAR (coeff[5], -1, 1e-3);
EXPECT_NEAR (coeff[6], 0, 1e-3);
Eigen::VectorXf coeff_refined;
model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
EXPECT_EQ (int (coeff_refined.size ()), 7);
EXPECT_NEAR (coeff_refined[0], 1, 1e-3);
EXPECT_NEAR (coeff_refined[1], 5, 1e-3);
EXPECT_NEAR (coeff_refined[2], -3, 1e-3);
EXPECT_NEAR (coeff_refined[3],0.1, 1e-3);
EXPECT_NEAR (coeff_refined[4], 0, 1e-3);
EXPECT_NEAR (coeff_refined[5], -1, 1e-3);
EXPECT_NEAR (coeff_refined[6], 0, 1e-3);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (RANSAC, SampleConsensusModelLine)
{
srand (0);
// Use a custom point cloud for these tests until we need something better
PointCloud<PointXYZ> cloud;
cloud.points.resize (10);
cloud.points[0].x = 1.0f; cloud.points[0].y = 2.0f; cloud.points[0].z = 3.0f;
cloud.points[1].x = 4.0f; cloud.points[1].y = 5.0f; cloud.points[1].z = 6.0f;
cloud.points[2].x = 7.0f; cloud.points[2].y = 8.0f; cloud.points[2].z = 9.0f;
cloud.points[3].x = 10.0f; cloud.points[3].y = 11.0f; cloud.points[3].z = 12.0f;
cloud.points[4].x = 13.0f; cloud.points[4].y = 14.0f; cloud.points[4].z = 15.0f;
cloud.points[5].x = 16.0f; cloud.points[5].y = 17.0f; cloud.points[5].z = 18.0f;
cloud.points[6].x = 19.0f; cloud.points[6].y = 20.0f; cloud.points[6].z = 21.0f;
cloud.points[7].x = 22.0f; cloud.points[7].y = 23.0f; cloud.points[7].z = 24.0f;
cloud.points[8].x = -5.0f; cloud.points[8].y = 1.57f; cloud.points[8].z = 0.75f;
cloud.points[9].x = 4.0f; cloud.points[9].y = 2.0f; cloud.points[9].z = 3.0f;
// Create a shared line model pointer directly
SampleConsensusModelLinePtr model (new SampleConsensusModelLine<PointXYZ> (cloud.makeShared ()));
// Create the RANSAC object
RandomSampleConsensus<PointXYZ> sac (model, 0.001);
// Algorithm tests
bool result = sac.computeModel ();
ASSERT_EQ (result, true);
std::vector<int> sample;
sac.getModel (sample);
EXPECT_EQ (int (sample.size ()), 2);
std::vector<int> inliers;
sac.getInliers (inliers);
EXPECT_EQ (int (inliers.size ()), 8);
Eigen::VectorXf coeff;
sac.getModelCoefficients (coeff);
EXPECT_EQ (int (coeff.size ()), 6);
EXPECT_NEAR (coeff[4]/coeff[3], 1, 1e-4);
EXPECT_NEAR (coeff[5]/coeff[3], 1, 1e-4);
Eigen::VectorXf coeff_refined;
model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
EXPECT_EQ (int (coeff_refined.size ()), 6);
EXPECT_NEAR (coeff[4]/coeff[3], 1, 1e-4);
EXPECT_NEAR (coeff[5]/coeff[3], 1, 1e-4);
// Projection tests
PointCloud<PointXYZ> proj_points;
model->projectPoints (inliers, coeff_refined, proj_points);
EXPECT_NEAR (proj_points.points[2].x, 7.0, 1e-4);
EXPECT_NEAR (proj_points.points[2].y, 8.0, 1e-4);
EXPECT_NEAR (proj_points.points[2].z, 9.0, 1e-4);
EXPECT_NEAR (proj_points.points[3].x, 10.0, 1e-4);
EXPECT_NEAR (proj_points.points[3].y, 11.0, 1e-4);
EXPECT_NEAR (proj_points.points[3].z, 12.0, 1e-4);
EXPECT_NEAR (proj_points.points[5].x, 16.0, 1e-4);
EXPECT_NEAR (proj_points.points[5].y, 17.0, 1e-4);
EXPECT_NEAR (proj_points.points[5].z, 18.0, 1e-4);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (RANSAC, SampleConsensusModelNormalPlane)
{
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 if RANSAC finishes within a second.
TEST (SAC, InfiniteLoop)
{
const unsigned point_count = 100;
PointCloud<PointXYZ> cloud;
cloud.points.resize (point_count);
for (unsigned pIdx = 0; pIdx < point_count; ++pIdx)
{
cloud.points[pIdx].x = static_cast<float> (pIdx);
cloud.points[pIdx].y = 0.0;
cloud.points[pIdx].z = 0.0;
}
boost::posix_time::time_duration delay(0,0,1,0);
boost::function<bool ()> sac_function;
SampleConsensusModelSpherePtr model (new SampleConsensusModelSphere<PointXYZ> (cloud.makeShared ()));
// Create the RANSAC object
RandomSampleConsensus<PointXYZ> ransac (model, 0.03);
sac_function = boost::bind (&RandomSampleConsensus<PointXYZ>::computeModel, &ransac, 0);
boost::thread thread1 (sac_function);
ASSERT_TRUE(thread1.timed_join(delay));
// Create the LMSAC object
LeastMedianSquares<PointXYZ> lmsac (model, 0.03);
sac_function = boost::bind (&LeastMedianSquares<PointXYZ>::computeModel, &lmsac, 0);
boost::thread thread2 (sac_function);
ASSERT_TRUE(thread2.timed_join(delay));
// Create the MSAC object
MEstimatorSampleConsensus<PointXYZ> mesac (model, 0.03);
sac_function = boost::bind (&MEstimatorSampleConsensus<PointXYZ>::computeModel, &mesac, 0);
boost::thread thread3 (sac_function);
ASSERT_TRUE(thread3.timed_join(delay));
// Create the RRSAC object
RandomizedRandomSampleConsensus<PointXYZ> rrsac (model, 0.03);
sac_function = boost::bind (&RandomizedRandomSampleConsensus<PointXYZ>::computeModel, &rrsac, 0);
boost::thread thread4 (sac_function);
ASSERT_TRUE(thread4.timed_join(delay));
// Create the RMSAC object
RandomizedMEstimatorSampleConsensus<PointXYZ> rmsac (model, 0.03);
sac_function = boost::bind (&RandomizedMEstimatorSampleConsensus<PointXYZ>::computeModel, &rmsac, 0);
boost::thread thread5 (sac_function);
ASSERT_TRUE(thread5.timed_join(delay));
// Create the MLESAC object
MaximumLikelihoodSampleConsensus<PointXYZ> mlesac (model, 0.03);
sac_function = boost::bind (&MaximumLikelihoodSampleConsensus<PointXYZ>::computeModel, &mlesac, 0);
boost::thread thread6 (sac_function);
ASSERT_TRUE(thread6.timed_join(delay));
}
/* ---[ */
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_);
indices_.resize (cloud_->points.size ());
for (size_t i = 0; i < indices_.size (); ++i) { indices_[i] = int (i); }
// Estimate surface normals
NormalEstimation<PointXYZ, Normal> n;
search::Search<PointXYZ>::Ptr tree (new search::KdTree<PointXYZ>);
tree->setInputCloud (cloud_);
n.setInputCloud (cloud_);
boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices_));
n.setIndices (indicesptr);
n.setSearchMethod (tree);
n.setRadiusSearch (0.02); // Use 2cm radius to estimate the normals
n.compute (*normals_);
testing::InitGoogleTest (&argc, argv);
return (RUN_ALL_TESTS ());
}
/* ]--- */