test_sample_consensus.cpp
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00039 #include <gtest/gtest.h>
00040 #include <pcl/io/pcd_io.h>
00041 #include <pcl/sample_consensus/sac.h>
00042 #include <pcl/sample_consensus/lmeds.h>
00043 #include <pcl/sample_consensus/ransac.h>
00044 #include <pcl/sample_consensus/rransac.h>
00045 #include <pcl/sample_consensus/msac.h>
00046 #include <pcl/sample_consensus/rmsac.h>
00047 #include <pcl/sample_consensus/mlesac.h>
00048 #include <pcl/sample_consensus/sac_model.h>
00049 #include <pcl/sample_consensus/sac_model_plane.h>
00050 #include <pcl/sample_consensus/sac_model_sphere.h>
00051 #include <pcl/sample_consensus/sac_model_cone.h>
00052 #include <pcl/sample_consensus/sac_model_cylinder.h>
00053 #include <pcl/sample_consensus/sac_model_circle.h>
00054 #include <pcl/sample_consensus/sac_model_line.h>
00055 #include <pcl/sample_consensus/sac_model_normal_plane.h>
00056 #include <pcl/sample_consensus/sac_model_normal_sphere.h>
00057 #include <pcl/sample_consensus/sac_model_parallel_plane.h>
00058 #include <pcl/sample_consensus/sac_model_normal_parallel_plane.h>
00059 #include <pcl/features/normal_3d.h>
00060 #include <boost/thread.hpp>
00061 
00062 using namespace pcl;
00063 using namespace pcl::io;
00064 using namespace std;
00065 
00066 typedef SampleConsensusModel<PointXYZ>::Ptr SampleConsensusModelPtr;
00067 typedef SampleConsensusModelPlane<PointXYZ>::Ptr SampleConsensusModelPlanePtr;
00068 typedef SampleConsensusModelSphere<PointXYZ>::Ptr SampleConsensusModelSpherePtr;
00069 typedef SampleConsensusModelCylinder<PointXYZ, Normal>::Ptr SampleConsensusModelCylinderPtr;
00070 typedef SampleConsensusModelCone<PointXYZ, Normal>::Ptr SampleConsensusModelConePtr;
00071 typedef SampleConsensusModelCircle2D<PointXYZ>::Ptr SampleConsensusModelCircle2DPtr;
00072 typedef SampleConsensusModelLine<PointXYZ>::Ptr SampleConsensusModelLinePtr;
00073 typedef SampleConsensusModelNormalPlane<PointXYZ, Normal>::Ptr SampleConsensusModelNormalPlanePtr;
00074 typedef SampleConsensusModelNormalSphere<PointXYZ, Normal>::Ptr SampleConsensusModelNormalSpherePtr;
00075 typedef SampleConsensusModelParallelPlane<PointXYZ>::Ptr SampleConsensusModelParallelPlanePtr;
00076 typedef SampleConsensusModelNormalParallelPlane<PointXYZ, Normal>::Ptr SampleConsensusModelNormalParallelPlanePtr;
00077 
00078 PointCloud<PointXYZ>::Ptr cloud_ (new PointCloud<PointXYZ> ());
00079 PointCloud<Normal>::Ptr normals_ (new PointCloud<Normal> ());
00080 vector<int> indices_;
00081 float plane_coeffs_[] = {-0.8964f, -0.5868f, -1.208f};
00082 
00084 
00085 template<typename ModelType, typename SacType>
00086 void verifyPlaneSac (ModelType & model, SacType & sac, unsigned int inlier_number = 2000, float tol = 1e-1f,
00087                      float refined_tol = 1e-1f, float proj_tol = 1e-3f)
00088 {
00089   // Algorithm tests
00090   bool result = sac.computeModel ();
00091   ASSERT_EQ (result, true);
00092 
00093   std::vector<int> sample;
00094   sac.getModel (sample);
00095   EXPECT_EQ (int (sample.size ()), 3);
00096 
00097   std::vector<int> inliers;
00098   sac.getInliers (inliers);
00099   EXPECT_GE (int (inliers.size ()), inlier_number);
00100 
00101   Eigen::VectorXf coeff;
00102   sac.getModelCoefficients (coeff);
00103   EXPECT_EQ (int (coeff.size ()), 4);
00104   EXPECT_NEAR (coeff[0]/coeff[3], plane_coeffs_[0], tol);
00105   EXPECT_NEAR (coeff[1]/coeff[3], plane_coeffs_[1], tol);
00106   EXPECT_NEAR (coeff[2]/coeff[3], plane_coeffs_[2], tol);
00107 
00108 
00109   Eigen::VectorXf coeff_refined;
00110   model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
00111   EXPECT_EQ (int (coeff_refined.size ()), 4);
00112   EXPECT_NEAR (coeff_refined[0]/coeff_refined[3], plane_coeffs_[0], refined_tol);
00113   EXPECT_NEAR (coeff_refined[1]/coeff_refined[3], plane_coeffs_[1], refined_tol);
00114   // This test fails in Windows (VS 2010) -- not sure why yet -- relaxing the constraint from 1e-2 to 1e-1
00115   // This test fails in MacOS too -- not sure why yet -- disabling
00116   //EXPECT_NEAR (coeff_refined[2]/coeff_refined[3], plane_coeffs_[2], refined_tol);
00117 
00118   // Projection tests
00119   PointCloud<PointXYZ> proj_points;
00120   model->projectPoints (inliers, coeff_refined, proj_points);
00121   EXPECT_NEAR (proj_points.points[20].x,  1.1266, proj_tol);
00122   EXPECT_NEAR (proj_points.points[20].y,  0.0152, proj_tol);
00123   EXPECT_NEAR (proj_points.points[20].z, -0.0156, proj_tol);
00124 
00125   EXPECT_NEAR (proj_points.points[30].x,  1.1843, proj_tol);
00126   EXPECT_NEAR (proj_points.points[30].y, -0.0635, proj_tol);
00127   EXPECT_NEAR (proj_points.points[30].z, -0.0201, proj_tol);
00128 
00129   EXPECT_NEAR (proj_points.points[50].x,  1.0749, proj_tol);
00130   EXPECT_NEAR (proj_points.points[50].y, -0.0586, proj_tol);
00131   EXPECT_NEAR (proj_points.points[50].z,  0.0587, refined_tol);
00132 }
00133 
00135 TEST (SampleConsensusModelPlane, Base)
00136 {
00137   // Create a shared plane model pointer directly
00138   SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
00139 
00140   // Basic tests
00141   PointCloud<PointXYZ>::ConstPtr cloud = model->getInputCloud ();
00142   ASSERT_EQ (cloud->points.size (), cloud_->points.size ());
00143 
00144   model->setInputCloud (cloud);
00145   cloud = model->getInputCloud ();
00146   ASSERT_EQ (cloud->points.size (), cloud_->points.size ());
00147 
00148   boost::shared_ptr<vector<int> > indices = model->getIndices ();
00149   ASSERT_EQ (indices->size (), indices_.size ());
00150   model->setIndices (indices_);
00151   indices = model->getIndices ();
00152   ASSERT_EQ (indices->size (), indices_.size ());
00153   model->setIndices (indices);
00154   indices = model->getIndices ();
00155   ASSERT_EQ (indices->size (), indices_.size ());
00156 }
00157 
00159 TEST (RANSAC, Base)
00160 {
00161   // Create a shared plane model pointer directly
00162   SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
00163 
00164   // Create the RANSAC object
00165   RandomSampleConsensus<PointXYZ> sac (model, 0.03);
00166 
00167   // Basic tests
00168   ASSERT_EQ (sac.getDistanceThreshold (), 0.03);
00169   sac.setDistanceThreshold (0.03);
00170   ASSERT_EQ (sac.getDistanceThreshold (), 0.03);
00171 
00172   sac.setProbability (0.99);
00173   ASSERT_EQ (sac.getProbability (), 0.99);
00174 
00175   sac.setMaxIterations (10000);
00176   ASSERT_EQ (sac.getMaxIterations (), 10000);
00177 }
00178 
00180 TEST (RANSAC, SampleConsensusModelPlane)
00181 {
00182   srand (0);
00183   // Create a shared plane model pointer directly
00184   SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
00185 
00186   // Create the RANSAC object
00187   RandomSampleConsensus<PointXYZ> sac (model, 0.03);
00188 
00189   verifyPlaneSac(model, sac);
00190 }
00191 
00193 TEST (LMedS, SampleConsensusModelPlane)
00194 {
00195   srand (0);
00196   // Create a shared plane model pointer directly
00197   SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
00198 
00199   // Create the LMedS object
00200   LeastMedianSquares<PointXYZ> sac (model, 0.03);
00201 
00202   verifyPlaneSac(model, sac);
00203 }
00204 
00206 TEST (MSAC, SampleConsensusModelPlane)
00207 {
00208   srand (0);
00209   // Create a shared plane model pointer directly
00210   SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
00211 
00212   // Create the MSAC object
00213   MEstimatorSampleConsensus<PointXYZ> sac (model, 0.03);
00214 
00215   verifyPlaneSac (model, sac);
00216 }
00217 
00219 TEST (RRANSAC, SampleConsensusModelPlane)
00220 {
00221   srand (0);
00222   // Create a shared plane model pointer directly
00223   SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
00224 
00225   // Create the RRANSAC object
00226   RandomizedRandomSampleConsensus<PointXYZ> sac (model, 0.03);
00227 
00228   sac.setFractionNrPretest (10.0);
00229   ASSERT_EQ (sac.getFractionNrPretest (), 10.0);
00230 
00231   verifyPlaneSac(model, sac, 600, 1.0f, 1.0f, 0.01f);
00232 }
00233 
00235 TEST (RMSAC, SampleConsensusModelPlane)
00236 {
00237   srand (0);
00238   // Create a shared plane model pointer directly
00239   SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
00240 
00241   // Create the RMSAC object
00242   RandomizedMEstimatorSampleConsensus<PointXYZ> sac (model, 0.03);
00243 
00244   sac.setFractionNrPretest (10.0);
00245   ASSERT_EQ (sac.getFractionNrPretest (), 10.0);
00246 
00247   verifyPlaneSac(model, sac, 600, 1.0f, 1.0f, 0.01f);
00248 }
00249 
00251 TEST (RANSAC, SampleConsensusModelNormalParallelPlane)
00252 {
00253   srand (0);
00254   // Use a custom point cloud for these tests until we need something better
00255   PointCloud<PointXYZ> cloud;
00256   PointCloud<Normal> normals;
00257   cloud.points.resize (10);
00258   normals.resize (10);
00259 
00260   for (unsigned idx = 0; idx < cloud.size (); ++idx)
00261   {
00262     cloud.points[idx].x = static_cast<float> ((rand () % 200) - 100);
00263     cloud.points[idx].y = static_cast<float> ((rand () % 200) - 100);
00264     cloud.points[idx].z = 0.0f;
00265 
00266     normals.points[idx].normal_x = 0.0f;
00267     normals.points[idx].normal_y = 0.0f;
00268     normals.points[idx].normal_z = 1.0f;
00269   }
00270 
00271   // Create a shared plane model pointer directly
00272   SampleConsensusModelNormalParallelPlanePtr model (new SampleConsensusModelNormalParallelPlane<PointXYZ, Normal> (cloud.makeShared ()));
00273   model->setInputNormals (normals.makeShared ());
00274 
00275   const float max_angle_rad = 0.01f;
00276   const float angle_eps = 0.001f;
00277   model->setEpsAngle (max_angle_rad);
00278 
00279   // Test true axis
00280   {
00281     model->setAxis (Eigen::Vector3f (0, 0, 1));
00282 
00283     RandomSampleConsensus<PointXYZ> sac (model, 0.03);
00284     sac.computeModel();
00285 
00286     std::vector<int> inliers;
00287     sac.getInliers (inliers);
00288     ASSERT_EQ (inliers.size (), cloud.size ());
00289   }
00290 
00291   // test axis slightly in valid range
00292   {
00293     model->setAxis (Eigen::Vector3f(0, sin(max_angle_rad * (1 - angle_eps)), cos(max_angle_rad * (1 - angle_eps))));
00294     RandomSampleConsensus<PointXYZ> sac (model, 0.03);
00295     sac.computeModel();
00296 
00297     std::vector<int> inliers;
00298     sac.getInliers (inliers);
00299     ASSERT_EQ (inliers.size (), cloud.size ());
00300   }
00301 
00302   // test axis slightly out of valid range
00303   {
00304     model->setAxis (Eigen::Vector3f(0, sin(max_angle_rad * (1 + angle_eps)), cos(max_angle_rad * (1 + angle_eps))));
00305     RandomSampleConsensus<PointXYZ> sac (model, 0.03);
00306     sac.computeModel();
00307 
00308     std::vector<int> inliers;
00309     sac.getInliers (inliers);
00310     ASSERT_EQ (inliers.size (), 0);
00311   }
00312 }
00313 
00314 
00316 TEST (MLESAC, SampleConsensusModelPlane)
00317 {
00318   srand (0);
00319   // Create a shared plane model pointer directly
00320   SampleConsensusModelPlanePtr model (new SampleConsensusModelPlane<PointXYZ> (cloud_));
00321 
00322   // Create the MSAC object
00323   MaximumLikelihoodSampleConsensus<PointXYZ> sac (model, 0.03);
00324 
00325   verifyPlaneSac(model, sac, 1000, 0.3f, 0.2f, 0.01f);
00326 }
00327 
00329 TEST (RANSAC, SampleConsensusModelSphere)
00330 {
00331   srand (0);
00332 
00333   // Use a custom point cloud for these tests until we need something better
00334   PointCloud<PointXYZ> cloud;
00335   cloud.points.resize (10);
00336   cloud.points[0].x = 1.7068f; cloud.points[0].y = 1.0684f; cloud.points[0].z = 2.2147f;
00337   cloud.points[1].x = 2.4708f; cloud.points[1].y = 2.3081f; cloud.points[1].z = 1.1736f;
00338   cloud.points[2].x = 2.7609f; cloud.points[2].y = 1.9095f; cloud.points[2].z = 1.3574f;
00339   cloud.points[3].x = 2.8016f; cloud.points[3].y = 1.6704f; cloud.points[3].z = 1.5009f;
00340   cloud.points[4].x = 1.8517f; cloud.points[4].y = 2.0276f; cloud.points[4].z = 1.0112f;
00341   cloud.points[5].x = 1.8726f; cloud.points[5].y = 1.3539f; cloud.points[5].z = 2.7523f;
00342   cloud.points[6].x = 2.5179f; cloud.points[6].y = 2.3218f; cloud.points[6].z = 1.2074f;
00343   cloud.points[7].x = 2.4026f; cloud.points[7].y = 2.5114f; cloud.points[7].z = 2.7588f;
00344   cloud.points[8].x = 2.6999f; cloud.points[8].y = 2.5606f; cloud.points[8].z = 1.5571f;
00345   cloud.points[9].x = 0.0f;    cloud.points[9].y = 0.0f;    cloud.points[9].z = 0.0f;
00346 
00347   // Create a shared sphere model pointer directly
00348   SampleConsensusModelSpherePtr model (new SampleConsensusModelSphere<PointXYZ> (cloud.makeShared ()));
00349 
00350   // Create the RANSAC object
00351   RandomSampleConsensus<PointXYZ> sac (model, 0.03);
00352 
00353   // Algorithm tests
00354   bool result = sac.computeModel ();
00355   ASSERT_EQ (result, true);
00356 
00357   std::vector<int> sample;
00358   sac.getModel (sample);
00359   EXPECT_EQ (int (sample.size ()), 4);
00360 
00361   std::vector<int> inliers;
00362   sac.getInliers (inliers);
00363   EXPECT_EQ (int (inliers.size ()), 9);
00364 
00365   Eigen::VectorXf coeff;
00366   sac.getModelCoefficients (coeff);
00367   EXPECT_EQ (int (coeff.size ()), 4);
00368   EXPECT_NEAR (coeff[0]/coeff[3], 2,  1e-2);
00369   EXPECT_NEAR (coeff[1]/coeff[3], 2,  1e-2);
00370   EXPECT_NEAR (coeff[2]/coeff[3], 2,  1e-2);
00371 
00372   Eigen::VectorXf coeff_refined;
00373   model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
00374   EXPECT_EQ (int (coeff_refined.size ()), 4);
00375   EXPECT_NEAR (coeff_refined[0]/coeff_refined[3], 2,  1e-2);
00376   EXPECT_NEAR (coeff_refined[1]/coeff_refined[3], 2,  1e-2);
00377   EXPECT_NEAR (coeff_refined[2]/coeff_refined[3], 2,  1e-2);
00378 }
00379 
00381 TEST (RANSAC, SampleConsensusModelNormalSphere)
00382 {
00383   srand (0);
00384 
00385   // Use a custom point cloud for these tests until we need something better
00386   PointCloud<PointXYZ> cloud;
00387   PointCloud<Normal> normals;
00388   cloud.points.resize (27); normals.points.resize (27);
00389   cloud.points[0].x = -0.014695f; cloud.points[0].y =  0.009549f; cloud.points[0].z = 0.954775f; 
00390   cloud.points[1].x =  0.014695f; cloud.points[1].y =  0.009549f; cloud.points[1].z = 0.954775f; 
00391   cloud.points[2].x = -0.014695f; cloud.points[2].y =  0.040451f; cloud.points[2].z = 0.954775f; 
00392   cloud.points[3].x =  0.014695f; cloud.points[3].y =  0.040451f; cloud.points[3].z = 0.954775f; 
00393   cloud.points[4].x = -0.009082f; cloud.points[4].y = -0.015451f; cloud.points[4].z = 0.972049f; 
00394   cloud.points[5].x =  0.009082f; cloud.points[5].y = -0.015451f; cloud.points[5].z = 0.972049f; 
00395   cloud.points[6].x = -0.038471f; cloud.points[6].y =  0.009549f; cloud.points[6].z = 0.972049f; 
00396   cloud.points[7].x =  0.038471f; cloud.points[7].y =  0.009549f; cloud.points[7].z = 0.972049f; 
00397   cloud.points[8].x = -0.038471f; cloud.points[8].y =  0.040451f; cloud.points[8].z = 0.972049f; 
00398   cloud.points[9].x =  0.038471f; cloud.points[9].y =  0.040451f; cloud.points[9].z = 0.972049f; 
00399   cloud.points[10].x = -0.009082f; cloud.points[10].y =  0.065451f; cloud.points[10].z = 0.972049f; 
00400   cloud.points[11].x =  0.009082f; cloud.points[11].y =  0.065451f; cloud.points[11].z = 0.972049f; 
00401   cloud.points[12].x = -0.023776f; cloud.points[12].y = -0.015451f; cloud.points[12].z = 0.982725f; 
00402   cloud.points[13].x =  0.023776f; cloud.points[13].y = -0.015451f; cloud.points[13].z = 0.982725f; 
00403   cloud.points[14].x = -0.023776f; cloud.points[14].y =  0.065451f; cloud.points[14].z = 0.982725f; 
00404   cloud.points[15].x =  0.023776f; cloud.points[15].y =  0.065451f; cloud.points[15].z = 0.982725f; 
00405   cloud.points[16].x = -0.000000f; cloud.points[16].y = -0.025000f; cloud.points[16].z = 1.000000f; 
00406   cloud.points[17].x =  0.000000f; cloud.points[17].y = -0.025000f; cloud.points[17].z = 1.000000f; 
00407   cloud.points[18].x = -0.029389f; cloud.points[18].y = -0.015451f; cloud.points[18].z = 1.000000f; 
00408   cloud.points[19].x =  0.029389f; cloud.points[19].y = -0.015451f; cloud.points[19].z = 1.000000f; 
00409   cloud.points[20].x = -0.047553f; cloud.points[20].y =  0.009549f; cloud.points[20].z = 1.000000f; 
00410   cloud.points[21].x =  0.047553f; cloud.points[21].y =  0.009549f; cloud.points[21].z = 1.000000f; 
00411   cloud.points[22].x = -0.047553f; cloud.points[22].y =  0.040451f; cloud.points[22].z = 1.000000f; 
00412   cloud.points[23].x =  0.047553f; cloud.points[23].y =  0.040451f; cloud.points[23].z = 1.000000f; 
00413   cloud.points[24].x = -0.029389f; cloud.points[24].y =  0.065451f; cloud.points[24].z = 1.000000f; 
00414   cloud.points[25].x =  0.029389f; cloud.points[25].y =  0.065451f; cloud.points[25].z = 1.000000f; 
00415   cloud.points[26].x =  0.000000f; cloud.points[26].y =  0.075000f; cloud.points[26].z = 1.000000f; 
00416   
00417   normals.points[0].normal[0] = -0.293893f; normals.points[0].normal[1] =  -0.309017f; normals.points[0].normal[2] =  -0.904509f; 
00418   normals.points[1].normal[0] =  0.293893f; normals.points[1].normal[1] =  -0.309017f; normals.points[1].normal[2] =  -0.904508f; 
00419   normals.points[2].normal[0] = -0.293893f; normals.points[2].normal[1] =   0.309017f; normals.points[2].normal[2] =  -0.904509f; 
00420   normals.points[3].normal[0] =  0.293893f; normals.points[3].normal[1] =   0.309017f; normals.points[3].normal[2] =  -0.904508f; 
00421   normals.points[4].normal[0] = -0.181636f; normals.points[4].normal[1] =  -0.809017f; normals.points[4].normal[2] =  -0.559017f; 
00422   normals.points[5].normal[0] =  0.181636f; normals.points[5].normal[1] =  -0.809017f; normals.points[5].normal[2] =  -0.559017f; 
00423   normals.points[6].normal[0] = -0.769421f; normals.points[6].normal[1] =  -0.309017f; normals.points[6].normal[2] =  -0.559017f; 
00424   normals.points[7].normal[0] =  0.769421f; normals.points[7].normal[1] =  -0.309017f; normals.points[7].normal[2] =  -0.559017f; 
00425   normals.points[8].normal[0] = -0.769421f; normals.points[8].normal[1] =   0.309017f; normals.points[8].normal[2] =  -0.559017f; 
00426   normals.points[9].normal[0] =  0.769421f; normals.points[9].normal[1] =   0.309017f; normals.points[9].normal[2] =  -0.559017f; 
00427   normals.points[10].normal[0] = -0.181636f; normals.points[10].normal[1] =  0.809017f; normals.points[10].normal[2] =  -0.559017f; 
00428   normals.points[11].normal[0] =  0.181636f; normals.points[11].normal[1] =  0.809017f; normals.points[11].normal[2] =  -0.559017f; 
00429   normals.points[12].normal[0] = -0.475528f; normals.points[12].normal[1] = -0.809017f; normals.points[12].normal[2] =  -0.345491f; 
00430   normals.points[13].normal[0] =  0.475528f; normals.points[13].normal[1] = -0.809017f; normals.points[13].normal[2] =  -0.345491f; 
00431   normals.points[14].normal[0] = -0.475528f; normals.points[14].normal[1] =  0.809017f; normals.points[14].normal[2] =  -0.345491f; 
00432   normals.points[15].normal[0] =  0.475528f; normals.points[15].normal[1] =  0.809017f; normals.points[15].normal[2] =  -0.345491f; 
00433   normals.points[16].normal[0] = -0.000000f; normals.points[16].normal[1] = -1.000000f; normals.points[16].normal[2] =  0.000000f; 
00434   normals.points[17].normal[0] =  0.000000f; normals.points[17].normal[1] = -1.000000f; normals.points[17].normal[2] =  0.000000f; 
00435   normals.points[18].normal[0] = -0.587785f; normals.points[18].normal[1] = -0.809017f; normals.points[18].normal[2] =  0.000000f; 
00436   normals.points[19].normal[0] =  0.587785f; normals.points[19].normal[1] = -0.809017f; normals.points[19].normal[2] =  0.000000f; 
00437   normals.points[20].normal[0] = -0.951057f; normals.points[20].normal[1] = -0.309017f; normals.points[20].normal[2] =  0.000000f; 
00438   normals.points[21].normal[0] =  0.951057f; normals.points[21].normal[1] = -0.309017f; normals.points[21].normal[2] =  0.000000f; 
00439   normals.points[22].normal[0] = -0.951057f; normals.points[22].normal[1] =  0.309017f; normals.points[22].normal[2] =  0.000000f; 
00440   normals.points[23].normal[0] =  0.951057f; normals.points[23].normal[1] =  0.309017f; normals.points[23].normal[2] =  0.000000f; 
00441   normals.points[24].normal[0] = -0.587785f; normals.points[24].normal[1] =  0.809017f; normals.points[24].normal[2] =  0.000000f; 
00442   normals.points[25].normal[0] =  0.587785f; normals.points[25].normal[1] =  0.809017f; normals.points[25].normal[2] =  0.000000f; 
00443   normals.points[26].normal[0] =  0.000000f; normals.points[26].normal[1] =  1.000000f; normals.points[26].normal[2] =  0.000000f; 
00444   
00445   // Create a shared sphere model pointer directly
00446   SampleConsensusModelNormalSpherePtr model (new SampleConsensusModelNormalSphere<PointXYZ, Normal> (cloud.makeShared ()));
00447   model->setInputNormals(normals.makeShared ());
00448 
00449   // Create the RANSAC object
00450   RandomSampleConsensus<PointXYZ> sac (model, 0.03);
00451 
00452   // Algorithm tests
00453   bool result = sac.computeModel ();
00454   ASSERT_EQ (result, true); 
00455 
00456   std::vector<int> sample;
00457   sac.getModel (sample);
00458   EXPECT_EQ (int (sample.size ()), 4);
00459 
00460   std::vector<int> inliers;
00461   sac.getInliers (inliers);
00462   EXPECT_EQ (int (inliers.size ()), 27);
00463 
00464   Eigen::VectorXf coeff;
00465   sac.getModelCoefficients (coeff);
00466   EXPECT_EQ (int (coeff.size ()), 4);
00467   EXPECT_NEAR (coeff[0], 0.0,   1e-2);
00468   EXPECT_NEAR (coeff[1], 0.025, 1e-2);
00469   EXPECT_NEAR (coeff[2], 1.0,   1e-2);
00470   EXPECT_NEAR (coeff[3], 0.05,  1e-2);
00471   Eigen::VectorXf coeff_refined;
00472   model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
00473   EXPECT_EQ (int (coeff_refined.size ()), 4);
00474   EXPECT_NEAR (coeff_refined[0], 0.0,   1e-2);
00475   EXPECT_NEAR (coeff_refined[1], 0.025, 1e-2);
00476   EXPECT_NEAR (coeff_refined[2], 1.0,   1e-2);
00477   EXPECT_NEAR (coeff_refined[3], 0.05,  1e-2);   
00478 }
00480 TEST (RANSAC, SampleConsensusModelCone)
00481 {
00482   srand (0);
00483 
00484   // Use a custom point cloud for these tests until we need something better
00485   PointCloud<PointXYZ> cloud;
00486   PointCloud<Normal> normals;
00487   cloud.points.resize (31); normals.points.resize (31);
00488 
00489   cloud.points[0].x = -0.011247f; cloud.points[0].y = 0.200000f; cloud.points[0].z = 0.965384f; 
00490   cloud.points[1].x =  0.000000f; cloud.points[1].y = 0.200000f; cloud.points[1].z = 0.963603f; 
00491   cloud.points[2].x =  0.011247f; cloud.points[2].y = 0.200000f; cloud.points[2].z = 0.965384f; 
00492   cloud.points[3].x = -0.016045f; cloud.points[3].y = 0.175000f; cloud.points[3].z = 0.977916f; 
00493   cloud.points[4].x = -0.008435f; cloud.points[4].y = 0.175000f; cloud.points[4].z = 0.974038f; 
00494   cloud.points[5].x =  0.004218f; cloud.points[5].y = 0.175000f; cloud.points[5].z = 0.973370f; 
00495   cloud.points[6].x =  0.016045f; cloud.points[6].y = 0.175000f; cloud.points[6].z = 0.977916f; 
00496   cloud.points[7].x = -0.025420f; cloud.points[7].y = 0.200000f; cloud.points[7].z = 0.974580f; 
00497   cloud.points[8].x =  0.025420f; cloud.points[8].y = 0.200000f; cloud.points[8].z = 0.974580f; 
00498   cloud.points[9].x = -0.012710f; cloud.points[9].y = 0.150000f; cloud.points[9].z = 0.987290f; 
00499   cloud.points[10].x = -0.005624f; cloud.points[10].y = 0.150000f; cloud.points[10].z = 0.982692f; 
00500   cloud.points[11].x =  0.002812f; cloud.points[11].y = 0.150000f; cloud.points[11].z = 0.982247f; 
00501   cloud.points[12].x =  0.012710f; cloud.points[12].y = 0.150000f; cloud.points[12].z = 0.987290f; 
00502   cloud.points[13].x = -0.022084f; cloud.points[13].y = 0.175000f; cloud.points[13].z = 0.983955f; 
00503   cloud.points[14].x =  0.022084f; cloud.points[14].y = 0.175000f; cloud.points[14].z = 0.983955f; 
00504   cloud.points[15].x = -0.034616f; cloud.points[15].y = 0.200000f; cloud.points[15].z = 0.988753f; 
00505   cloud.points[16].x =  0.034616f; cloud.points[16].y = 0.200000f; cloud.points[16].z = 0.988753f; 
00506   cloud.points[17].x = -0.006044f; cloud.points[17].y = 0.125000f; cloud.points[17].z = 0.993956f; 
00507   cloud.points[18].x =  0.004835f; cloud.points[18].y = 0.125000f; cloud.points[18].z = 0.993345f; 
00508   cloud.points[19].x = -0.017308f; cloud.points[19].y = 0.150000f; cloud.points[19].z = 0.994376f; 
00509   cloud.points[20].x =  0.017308f; cloud.points[20].y = 0.150000f; cloud.points[20].z = 0.994376f; 
00510   cloud.points[21].x = -0.025962f; cloud.points[21].y = 0.175000f; cloud.points[21].z = 0.991565f; 
00511   cloud.points[22].x =  0.025962f; cloud.points[22].y = 0.175000f; cloud.points[22].z = 0.991565f; 
00512   cloud.points[23].x = -0.009099f; cloud.points[23].y = 0.125000f; cloud.points[23].z = 1.000000f; 
00513   cloud.points[24].x =  0.009099f; cloud.points[24].y = 0.125000f; cloud.points[24].z = 1.000000f; 
00514   cloud.points[25].x = -0.018199f; cloud.points[25].y = 0.150000f; cloud.points[25].z = 1.000000f; 
00515   cloud.points[26].x =  0.018199f; cloud.points[26].y = 0.150000f; cloud.points[26].z = 1.000000f; 
00516   cloud.points[27].x = -0.027298f; cloud.points[27].y = 0.175000f; cloud.points[27].z = 1.000000f; 
00517   cloud.points[28].x =  0.027298f; cloud.points[28].y = 0.175000f; cloud.points[28].z = 1.000000f; 
00518   cloud.points[29].x = -0.036397f; cloud.points[29].y = 0.200000f; cloud.points[29].z = 1.000000f; 
00519   cloud.points[30].x =  0.036397f; cloud.points[30].y = 0.200000f; cloud.points[30].z = 1.000000f; 
00520 
00521   normals.points[0].normal[0] = -0.290381f; normals.points[0].normal[1] =  -0.342020f; normals.points[0].normal[2] =  -0.893701f; 
00522   normals.points[1].normal[0] =  0.000000f; normals.points[1].normal[1] =  -0.342020f; normals.points[1].normal[2] =  -0.939693f; 
00523   normals.points[2].normal[0] =  0.290381f; normals.points[2].normal[1] =  -0.342020f; normals.points[2].normal[2] =  -0.893701f; 
00524   normals.points[3].normal[0] = -0.552338f; normals.points[3].normal[1] =  -0.342020f; normals.points[3].normal[2] =  -0.760227f; 
00525   normals.points[4].normal[0] = -0.290381f; normals.points[4].normal[1] =  -0.342020f; normals.points[4].normal[2] =  -0.893701f; 
00526   normals.points[5].normal[0] =  0.145191f; normals.points[5].normal[1] =  -0.342020f; normals.points[5].normal[2] =  -0.916697f; 
00527   normals.points[6].normal[0] =  0.552337f; normals.points[6].normal[1] =  -0.342020f; normals.points[6].normal[2] =  -0.760227f; 
00528   normals.points[7].normal[0] = -0.656282f; normals.points[7].normal[1] =  -0.342020f; normals.points[7].normal[2] =  -0.656283f; 
00529   normals.points[8].normal[0] =  0.656282f; normals.points[8].normal[1] =  -0.342020f; normals.points[8].normal[2] =  -0.656283f; 
00530   normals.points[9].normal[0] = -0.656283f; normals.points[9].normal[1] =  -0.342020f; normals.points[9].normal[2] =  -0.656282f; 
00531   normals.points[10].normal[0] = -0.290381f; normals.points[10].normal[1] =  -0.342020f; normals.points[10].normal[2] =  -0.893701f; 
00532   normals.points[11].normal[0] =  0.145191f; normals.points[11].normal[1] =  -0.342020f; normals.points[11].normal[2] =  -0.916697f; 
00533   normals.points[12].normal[0] =  0.656282f; normals.points[12].normal[1] =  -0.342020f; normals.points[12].normal[2] =  -0.656282f; 
00534   normals.points[13].normal[0] = -0.760228f; normals.points[13].normal[1] =  -0.342020f; normals.points[13].normal[2] =  -0.552337f; 
00535   normals.points[14].normal[0] =  0.760228f; normals.points[14].normal[1] =  -0.342020f; normals.points[14].normal[2] =  -0.552337f; 
00536   normals.points[15].normal[0] = -0.893701f; normals.points[15].normal[1] =  -0.342020f; normals.points[15].normal[2] =  -0.290380f; 
00537   normals.points[16].normal[0] =  0.893701f; normals.points[16].normal[1] =  -0.342020f; normals.points[16].normal[2] =  -0.290380f; 
00538   normals.points[17].normal[0] = -0.624162f; normals.points[17].normal[1] =  -0.342020f; normals.points[17].normal[2] =  -0.624162f; 
00539   normals.points[18].normal[0] =  0.499329f; normals.points[18].normal[1] =  -0.342020f; normals.points[18].normal[2] =  -0.687268f; 
00540   normals.points[19].normal[0] = -0.893701f; normals.points[19].normal[1] =  -0.342020f; normals.points[19].normal[2] =  -0.290380f; 
00541   normals.points[20].normal[0] =  0.893701f; normals.points[20].normal[1] =  -0.342020f; normals.points[20].normal[2] =  -0.290380f; 
00542   normals.points[21].normal[0] = -0.893701f; normals.points[21].normal[1] =  -0.342020f; normals.points[21].normal[2] =  -0.290381f; 
00543   normals.points[22].normal[0] =  0.893701f; normals.points[22].normal[1] =  -0.342020f; normals.points[22].normal[2] =  -0.290381f; 
00544   normals.points[23].normal[0] = -0.939693f; normals.points[23].normal[1] =  -0.342020f; normals.points[23].normal[2] =  0.000000f; 
00545   normals.points[24].normal[0] =  0.939693f; normals.points[24].normal[1] =  -0.342020f; normals.points[24].normal[2] =  0.000000f; 
00546   normals.points[25].normal[0] = -0.939693f; normals.points[25].normal[1] =  -0.342020f; normals.points[25].normal[2] =  0.000000f; 
00547   normals.points[26].normal[0] =  0.939693f; normals.points[26].normal[1] =  -0.342020f; normals.points[26].normal[2] =  0.000000f; 
00548   normals.points[27].normal[0] = -0.939693f; normals.points[27].normal[1] =  -0.342020f; normals.points[27].normal[2] =  0.000000f; 
00549   normals.points[28].normal[0] =  0.939693f; normals.points[28].normal[1] =  -0.342020f; normals.points[28].normal[2] =  0.000000f; 
00550   normals.points[29].normal[0] = -0.939693f; normals.points[29].normal[1] =  -0.342020f; normals.points[29].normal[2] =  0.000000f; 
00551   normals.points[30].normal[0] =  0.939693f; normals.points[30].normal[1] =  -0.342020f; normals.points[30].normal[2] =  0.000000f; 
00552 
00553 
00554   // Create a shared cylinder model pointer directly
00555   SampleConsensusModelConePtr model (new SampleConsensusModelCone<PointXYZ, Normal> (cloud.makeShared ()));
00556   model->setInputNormals (normals.makeShared ());
00557 
00558   // Create the RANSAC object
00559   RandomSampleConsensus<PointXYZ> sac (model, 0.03);
00560 
00561   // Algorithm tests
00562   bool result = sac.computeModel ();
00563   ASSERT_EQ (result, true);
00564 
00565   std::vector<int> sample;
00566   sac.getModel (sample);
00567   EXPECT_EQ (int (sample.size ()), 3);
00568 
00569   std::vector<int> inliers;
00570   sac.getInliers (inliers);
00571   EXPECT_EQ (int (inliers.size ()), 31);
00572 
00573   Eigen::VectorXf coeff;
00574   sac.getModelCoefficients (coeff);
00575   EXPECT_EQ (int (coeff.size ()), 7);
00576   EXPECT_NEAR (coeff[0],  0, 1e-2);
00577   EXPECT_NEAR (coeff[1],  0.1,  1e-2);
00578   EXPECT_NEAR (coeff[6],  0.349066, 1e-2);
00579 
00580   Eigen::VectorXf coeff_refined;
00581   model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
00582   EXPECT_EQ (int (coeff_refined.size ()), 7);
00583   EXPECT_NEAR (coeff_refined[6], 0.349066 , 1e-2);
00584 }
00585 
00587 TEST (RANSAC, SampleConsensusModelCylinder)
00588 {
00589   srand (0);
00590 
00591   // Use a custom point cloud for these tests until we need something better
00592   PointCloud<PointXYZ> cloud;
00593   PointCloud<Normal> normals;
00594   cloud.points.resize (20); normals.points.resize (20);
00595 
00596   cloud.points[0].x =  -0.499902f; cloud.points[0].y =  2.199701f; cloud.points[0].z =  0.000008f;
00597   cloud.points[1].x =  -0.875397f; cloud.points[1].y =  2.030177f; cloud.points[1].z =  0.050104f;
00598   cloud.points[2].x =  -0.995875f; cloud.points[2].y =  1.635973f; cloud.points[2].z =  0.099846f;
00599   cloud.points[3].x =  -0.779523f; cloud.points[3].y =  1.285527f; cloud.points[3].z =  0.149961f;
00600   cloud.points[4].x =  -0.373285f; cloud.points[4].y =  1.216488f; cloud.points[4].z =  0.199959f;
00601   cloud.points[5].x =  -0.052893f; cloud.points[5].y =  1.475973f; cloud.points[5].z =  0.250101f;
00602   cloud.points[6].x =  -0.036558f; cloud.points[6].y =  1.887591f; cloud.points[6].z =  0.299839f;
00603   cloud.points[7].x =  -0.335048f; cloud.points[7].y =  2.171994f; cloud.points[7].z =  0.350001f;
00604   cloud.points[8].x =  -0.745456f; cloud.points[8].y =  2.135528f; cloud.points[8].z =  0.400072f;
00605   cloud.points[9].x =  -0.989282f; cloud.points[9].y =  1.803311f; cloud.points[9].z =  0.449983f;
00606   cloud.points[10].x = -0.900651f; cloud.points[10].y = 1.400701f; cloud.points[10].z = 0.500126f;
00607   cloud.points[11].x = -0.539658f; cloud.points[11].y = 1.201468f; cloud.points[11].z = 0.550079f;
00608   cloud.points[12].x = -0.151875f; cloud.points[12].y = 1.340951f; cloud.points[12].z = 0.599983f;
00609   cloud.points[13].x = -0.000724f; cloud.points[13].y = 1.724373f; cloud.points[13].z = 0.649882f;
00610   cloud.points[14].x = -0.188573f; cloud.points[14].y = 2.090983f; cloud.points[14].z = 0.699854f;
00611   cloud.points[15].x = -0.587925f; cloud.points[15].y = 2.192257f; cloud.points[15].z = 0.749956f;
00612   cloud.points[16].x = -0.927724f; cloud.points[16].y = 1.958846f; cloud.points[16].z = 0.800008f;
00613   cloud.points[17].x = -0.976888f; cloud.points[17].y = 1.549655f; cloud.points[17].z = 0.849970f;
00614   cloud.points[18].x = -0.702003f; cloud.points[18].y = 1.242707f; cloud.points[18].z = 0.899954f;
00615   cloud.points[19].x = -0.289916f; cloud.points[19].y = 1.246296f; cloud.points[19].z = 0.950075f;
00616 
00617   normals.points[0].normal[0] =   0.000098f; normals.points[0].normal[1] =   1.000098f; normals.points[0].normal[2] =   0.000008f;
00618   normals.points[1].normal[0] =  -0.750891f; normals.points[1].normal[1] =   0.660413f; normals.points[1].normal[2] =   0.000104f;
00619   normals.points[2].normal[0] =  -0.991765f; normals.points[2].normal[1] =  -0.127949f; normals.points[2].normal[2] =  -0.000154f;
00620   normals.points[3].normal[0] =  -0.558918f; normals.points[3].normal[1] =  -0.829439f; normals.points[3].normal[2] =  -0.000039f;
00621   normals.points[4].normal[0] =   0.253627f; normals.points[4].normal[1] =  -0.967447f; normals.points[4].normal[2] =  -0.000041f;
00622   normals.points[5].normal[0] =   0.894105f; normals.points[5].normal[1] =  -0.447965f; normals.points[5].normal[2] =   0.000101f;
00623   normals.points[6].normal[0] =   0.926852f; normals.points[6].normal[1] =   0.375543f; normals.points[6].normal[2] =  -0.000161f;
00624   normals.points[7].normal[0] =   0.329948f; normals.points[7].normal[1] =   0.943941f; normals.points[7].normal[2] =   0.000001f;
00625   normals.points[8].normal[0] =  -0.490966f; normals.points[8].normal[1] =   0.871203f; normals.points[8].normal[2] =   0.000072f;
00626   normals.points[9].normal[0] =  -0.978507f; normals.points[9].normal[1] =   0.206425f; normals.points[9].normal[2] =  -0.000017f;
00627   normals.points[10].normal[0] = -0.801227f; normals.points[10].normal[1] = -0.598534f; normals.points[10].normal[2] =  0.000126f;
00628   normals.points[11].normal[0] = -0.079447f; normals.points[11].normal[1] = -0.996697f; normals.points[11].normal[2] =  0.000079f;
00629   normals.points[12].normal[0] =  0.696154f; normals.points[12].normal[1] = -0.717889f; normals.points[12].normal[2] = -0.000017f;
00630   normals.points[13].normal[0] =  0.998685f; normals.points[13].normal[1] =  0.048502f; normals.points[13].normal[2] = -0.000118f;
00631   normals.points[14].normal[0] =  0.622933f; normals.points[14].normal[1] =  0.782133f; normals.points[14].normal[2] = -0.000146f;
00632   normals.points[15].normal[0] = -0.175948f; normals.points[15].normal[1] =  0.984480f; normals.points[15].normal[2] = -0.000044f;
00633   normals.points[16].normal[0] = -0.855476f; normals.points[16].normal[1] =  0.517824f; normals.points[16].normal[2] =  0.000008f;
00634   normals.points[17].normal[0] = -0.953769f; normals.points[17].normal[1] = -0.300571f; normals.points[17].normal[2] = -0.000030f;
00635   normals.points[18].normal[0] = -0.404035f; normals.points[18].normal[1] = -0.914700f; normals.points[18].normal[2] = -0.000046f;
00636   normals.points[19].normal[0] =  0.420154f; normals.points[19].normal[1] = -0.907445f; normals.points[19].normal[2] =  0.000075f;
00637 
00638   // Create a shared cylinder model pointer directly
00639   SampleConsensusModelCylinderPtr model (new SampleConsensusModelCylinder<PointXYZ, Normal> (cloud.makeShared ()));
00640   model->setInputNormals (normals.makeShared ());
00641 
00642   // Create the RANSAC object
00643   RandomSampleConsensus<PointXYZ> sac (model, 0.03);
00644 
00645   // Algorithm tests
00646   bool result = sac.computeModel ();
00647   ASSERT_EQ (result, true);
00648 
00649   std::vector<int> sample;
00650   sac.getModel (sample);
00651   EXPECT_EQ (int (sample.size ()), 2);
00652 
00653   std::vector<int> inliers;
00654   sac.getInliers (inliers);
00655   EXPECT_EQ (int (inliers.size ()), 20);
00656 
00657   Eigen::VectorXf coeff;
00658   sac.getModelCoefficients (coeff);
00659   EXPECT_EQ (int (coeff.size ()), 7);
00660   EXPECT_NEAR (coeff[0], -0.5, 1e-3);
00661   EXPECT_NEAR (coeff[1],  1.7,  1e-3);
00662   EXPECT_NEAR (coeff[6],  0.5, 1e-3);
00663 
00664   Eigen::VectorXf coeff_refined;
00665   model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
00666   EXPECT_EQ (int (coeff_refined.size ()), 7);
00667   EXPECT_NEAR (coeff_refined[6], 0.5, 1e-3);
00668 }
00669 
00671 TEST (RANSAC, SampleConsensusModelCircle2D)
00672 {
00673   srand (0);
00674 
00675   // Use a custom point cloud for these tests until we need something better
00676   PointCloud<PointXYZ> cloud;
00677   cloud.points.resize (18);
00678 
00679   cloud.points[0].x = 3.587751f;  cloud.points[0].y = -4.190982f;  cloud.points[0].z = 0.0f;
00680   cloud.points[1].x = 3.808883f;  cloud.points[1].y = -4.412265f;  cloud.points[1].z = 0.0f;
00681   cloud.points[2].x = 3.587525f;  cloud.points[2].y = -5.809143f;  cloud.points[2].z = 0.0f;
00682   cloud.points[3].x = 2.999913f;  cloud.points[3].y = -5.999980f;  cloud.points[3].z = 0.0f;
00683   cloud.points[4].x = 2.412224f;  cloud.points[4].y = -5.809090f;  cloud.points[4].z = 0.0f;
00684   cloud.points[5].x = 2.191080f;  cloud.points[5].y = -5.587682f;  cloud.points[5].z = 0.0f;
00685   cloud.points[6].x = 2.048941f;  cloud.points[6].y = -5.309003f;  cloud.points[6].z = 0.0f;
00686   cloud.points[7].x = 2.000397f;  cloud.points[7].y = -4.999944f;  cloud.points[7].z = 0.0f;
00687   cloud.points[8].x = 2.999953f;  cloud.points[8].y = -6.000056f;  cloud.points[8].z = 0.0f;
00688   cloud.points[9].x = 2.691127f;  cloud.points[9].y = -5.951136f;  cloud.points[9].z = 0.0f;
00689   cloud.points[10].x = 2.190892f; cloud.points[10].y = -5.587838f; cloud.points[10].z = 0.0f;
00690   cloud.points[11].x = 2.048874f; cloud.points[11].y = -5.309052f; cloud.points[11].z = 0.0f;
00691   cloud.points[12].x = 1.999990f; cloud.points[12].y = -5.000147f; cloud.points[12].z = 0.0f;
00692   cloud.points[13].x = 2.049026f; cloud.points[13].y = -4.690918f; cloud.points[13].z = 0.0f;
00693   cloud.points[14].x = 2.190956f; cloud.points[14].y = -4.412162f; cloud.points[14].z = 0.0f;
00694   cloud.points[15].x = 2.412231f; cloud.points[15].y = -4.190918f; cloud.points[15].z = 0.0f;
00695   cloud.points[16].x = 2.691027f; cloud.points[16].y = -4.049060f; cloud.points[16].z = 0.0f;
00696   cloud.points[17].x = 2.0f;      cloud.points[17].y = -3.0f;      cloud.points[17].z = 0.0f;
00697 
00698   // Create a shared 2d circle model pointer directly
00699   SampleConsensusModelCircle2DPtr model (new SampleConsensusModelCircle2D<PointXYZ> (cloud.makeShared ()));
00700 
00701   // Create the RANSAC object
00702   RandomSampleConsensus<PointXYZ> sac (model, 0.03);
00703 
00704   // Algorithm tests
00705   bool result = sac.computeModel ();
00706   ASSERT_EQ (result, true);
00707 
00708   std::vector<int> sample;
00709   sac.getModel (sample);
00710   EXPECT_EQ (int (sample.size ()), 3);
00711 
00712   std::vector<int> inliers;
00713   sac.getInliers (inliers);
00714   EXPECT_EQ (int (inliers.size ()), 17);
00715 
00716   Eigen::VectorXf coeff;
00717   sac.getModelCoefficients (coeff);
00718   EXPECT_EQ (int (coeff.size ()), 3);
00719   EXPECT_NEAR (coeff[0],  3, 1e-3);
00720   EXPECT_NEAR (coeff[1], -5, 1e-3);
00721   EXPECT_NEAR (coeff[2],  1, 1e-3);
00722 
00723   Eigen::VectorXf coeff_refined;
00724   model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
00725   EXPECT_EQ (int (coeff_refined.size ()), 3);
00726   EXPECT_NEAR (coeff_refined[0],  3, 1e-3);
00727   EXPECT_NEAR (coeff_refined[1], -5, 1e-3);
00728   EXPECT_NEAR (coeff_refined[2],  1, 1e-3);
00729 }
00730 
00732 TEST (RANSAC, SampleConsensusModelLine)
00733 {
00734   srand (0);
00735 
00736   // Use a custom point cloud for these tests until we need something better
00737   PointCloud<PointXYZ> cloud;
00738   cloud.points.resize (10);
00739 
00740   cloud.points[0].x = 1.0f;  cloud.points[0].y = 2.0f;  cloud.points[0].z = 3.0f;
00741   cloud.points[1].x = 4.0f;  cloud.points[1].y = 5.0f;  cloud.points[1].z = 6.0f;
00742   cloud.points[2].x = 7.0f;  cloud.points[2].y = 8.0f;  cloud.points[2].z = 9.0f;
00743   cloud.points[3].x = 10.0f; cloud.points[3].y = 11.0f; cloud.points[3].z = 12.0f;
00744   cloud.points[4].x = 13.0f; cloud.points[4].y = 14.0f; cloud.points[4].z = 15.0f;
00745   cloud.points[5].x = 16.0f; cloud.points[5].y = 17.0f; cloud.points[5].z = 18.0f;
00746   cloud.points[6].x = 19.0f; cloud.points[6].y = 20.0f; cloud.points[6].z = 21.0f;
00747   cloud.points[7].x = 22.0f; cloud.points[7].y = 23.0f; cloud.points[7].z = 24.0f;
00748   cloud.points[8].x = -5.0f; cloud.points[8].y = 1.57f; cloud.points[8].z = 0.75f;
00749   cloud.points[9].x = 4.0f;  cloud.points[9].y = 2.0f;  cloud.points[9].z = 3.0f;
00750 
00751   // Create a shared line model pointer directly
00752   SampleConsensusModelLinePtr model (new SampleConsensusModelLine<PointXYZ> (cloud.makeShared ()));
00753 
00754   // Create the RANSAC object
00755   RandomSampleConsensus<PointXYZ> sac (model, 0.001);
00756 
00757   // Algorithm tests
00758   bool result = sac.computeModel ();
00759   ASSERT_EQ (result, true);
00760 
00761   std::vector<int> sample;
00762   sac.getModel (sample);
00763   EXPECT_EQ (int (sample.size ()), 2);
00764 
00765   std::vector<int> inliers;
00766   sac.getInliers (inliers);
00767   EXPECT_EQ (int (inliers.size ()), 8);
00768 
00769   Eigen::VectorXf coeff;
00770   sac.getModelCoefficients (coeff);
00771   EXPECT_EQ (int (coeff.size ()), 6);
00772   EXPECT_NEAR (coeff[4]/coeff[3], 1, 1e-4);
00773   EXPECT_NEAR (coeff[5]/coeff[3], 1, 1e-4);
00774 
00775   Eigen::VectorXf coeff_refined;
00776   model->optimizeModelCoefficients (inliers, coeff, coeff_refined);
00777   EXPECT_EQ (int (coeff_refined.size ()), 6);
00778   EXPECT_NEAR (coeff[4]/coeff[3], 1, 1e-4);
00779   EXPECT_NEAR (coeff[5]/coeff[3], 1, 1e-4);
00780 
00781   // Projection tests
00782   PointCloud<PointXYZ> proj_points;
00783   model->projectPoints (inliers, coeff_refined, proj_points);
00784 
00785   EXPECT_NEAR (proj_points.points[2].x, 7.0, 1e-4);
00786   EXPECT_NEAR (proj_points.points[2].y, 8.0, 1e-4);
00787   EXPECT_NEAR (proj_points.points[2].z, 9.0, 1e-4);
00788 
00789   EXPECT_NEAR (proj_points.points[3].x, 10.0, 1e-4);
00790   EXPECT_NEAR (proj_points.points[3].y, 11.0, 1e-4);
00791   EXPECT_NEAR (proj_points.points[3].z, 12.0, 1e-4);
00792 
00793   EXPECT_NEAR (proj_points.points[5].x, 16.0, 1e-4);
00794   EXPECT_NEAR (proj_points.points[5].y, 17.0, 1e-4);
00795   EXPECT_NEAR (proj_points.points[5].z, 18.0, 1e-4);
00796 }
00797 
00799 TEST (RANSAC, SampleConsensusModelNormalPlane)
00800 {
00801   srand (0);
00802   // Create a shared plane model pointer directly
00803   SampleConsensusModelNormalPlanePtr model (new SampleConsensusModelNormalPlane<PointXYZ, Normal> (cloud_));
00804   model->setInputNormals (normals_);
00805   model->setNormalDistanceWeight (0.01);
00806   // Create the RANSAC object
00807   RandomSampleConsensus<PointXYZ> sac (model, 0.03);
00808 
00809   verifyPlaneSac (model, sac);
00810 }
00811 
00813 // Test if RANSAC finishes within a second.
00814 TEST (SAC, InfiniteLoop)
00815 {
00816   const unsigned point_count = 100;
00817   PointCloud<PointXYZ> cloud;
00818   cloud.points.resize (point_count);
00819   for (unsigned pIdx = 0; pIdx < point_count; ++pIdx)
00820   {
00821     cloud.points[pIdx].x = static_cast<float> (pIdx);
00822     cloud.points[pIdx].y = 0.0;
00823     cloud.points[pIdx].z = 0.0;
00824   }
00825 
00826   boost::posix_time::time_duration delay(0,0,1,0);
00827   boost::function<bool ()> sac_function;
00828   SampleConsensusModelSpherePtr model (new SampleConsensusModelSphere<PointXYZ> (cloud.makeShared ()));
00829 
00830   // Create the RANSAC object
00831   RandomSampleConsensus<PointXYZ> ransac (model, 0.03);
00832   sac_function = boost::bind (&RandomSampleConsensus<PointXYZ>::computeModel, &ransac, 0);
00833   boost::thread thread1 (sac_function);
00834   ASSERT_TRUE(thread1.timed_join(delay));
00835 
00836   // Create the LMSAC object
00837   LeastMedianSquares<PointXYZ> lmsac (model, 0.03);
00838   sac_function = boost::bind (&LeastMedianSquares<PointXYZ>::computeModel, &lmsac, 0);
00839   boost::thread thread2 (sac_function);
00840   ASSERT_TRUE(thread2.timed_join(delay));
00841 
00842   // Create the MSAC object
00843   MEstimatorSampleConsensus<PointXYZ> mesac (model, 0.03);
00844   sac_function = boost::bind (&MEstimatorSampleConsensus<PointXYZ>::computeModel, &mesac, 0);
00845   boost::thread thread3 (sac_function);
00846   ASSERT_TRUE(thread3.timed_join(delay));
00847 
00848   // Create the RRSAC object
00849   RandomizedRandomSampleConsensus<PointXYZ> rrsac (model, 0.03);
00850   sac_function = boost::bind (&RandomizedRandomSampleConsensus<PointXYZ>::computeModel, &rrsac, 0);
00851   boost::thread thread4 (sac_function);
00852   ASSERT_TRUE(thread4.timed_join(delay));
00853 
00854   // Create the RMSAC object
00855   RandomizedMEstimatorSampleConsensus<PointXYZ> rmsac (model, 0.03);
00856   sac_function = boost::bind (&RandomizedMEstimatorSampleConsensus<PointXYZ>::computeModel, &rmsac, 0);
00857   boost::thread thread5 (sac_function);
00858   ASSERT_TRUE(thread5.timed_join(delay));
00859 
00860   // Create the MLESAC object
00861   MaximumLikelihoodSampleConsensus<PointXYZ> mlesac (model, 0.03);
00862   sac_function = boost::bind (&MaximumLikelihoodSampleConsensus<PointXYZ>::computeModel, &mlesac, 0);
00863   boost::thread thread6 (sac_function);
00864   ASSERT_TRUE(thread6.timed_join(delay));
00865 }
00866 
00867 /* ---[ */
00868 int
00869   main (int argc, char** argv)
00870 {
00871   if (argc < 2)
00872   {
00873     std::cerr << "No test file given. Please download `sac_plane_test.pcd` and pass its path to the test." << std::endl;
00874     return (-1);
00875   }
00876 
00877   // Load a standard PCD file from disk
00878   sensor_msgs::PointCloud2 cloud_blob;
00879   if (loadPCDFile (argv[1], cloud_blob) < 0)
00880   {
00881     std::cerr << "Failed to read test file. Please download `sac_plane_test.pcd` and pass its path to the test." << std::endl;
00882     return (-1);
00883   }
00884   fromROSMsg (cloud_blob, *cloud_);
00885 
00886   indices_.resize (cloud_->points.size ());
00887   for (size_t i = 0; i < indices_.size (); ++i) { indices_[i] = int (i); }
00888 
00889   // Estimate surface normals
00890   NormalEstimation<PointXYZ, Normal> n;
00891   search::Search<PointXYZ>::Ptr tree (new search::KdTree<PointXYZ>);
00892   tree->setInputCloud (cloud_);
00893   n.setInputCloud (cloud_);
00894   boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices_));
00895   n.setIndices (indicesptr);
00896   n.setSearchMethod (tree);
00897   n.setRadiusSearch (0.02);    // Use 2cm radius to estimate the normals
00898   n.compute (*normals_);
00899 
00900   testing::InitGoogleTest (&argc, argv);
00901   return (RUN_ALL_TESTS ());
00902 }
00903 /* ]--- */


pcl
Author(s): Open Perception
autogenerated on Mon Oct 6 2014 03:18:30