Remove unused variables (#3621)

Remove unneeded variables

examples/segmentation/example_cpc_segmentation.cpp

@@ -452,8 +452,6 @@ CPCSegmentation Parameters: \n\
     using AdjacencyIterator = LCCPSegmentation<PointT>::AdjacencyIterator;
     using EdgeID = LCCPSegmentation<PointT>::EdgeID;
 
-    std::set<EdgeID> edge_drawn;
-
     const unsigned char black_color   [3] = {0, 0, 0};
     const unsigned char white_color   [3] = {255, 255, 255};
     const unsigned char concave_color [3] = {255,  0,  0};

examples/segmentation/example_extract_clusters_normals.cpp

@@ -79,7 +79,6 @@ main (int, char **argv)
   tree_ec->setInputCloud (cloud_ptr);
   
   // Extracting Euclidean clusters using cloud and its normals
-  std::vector<int> indices;
   std::vector<pcl::PointIndices> cluster_indices;
   const float tolerance = 0.5f; // 50cm tolerance in (x, y, z) coordinate system
   const double eps_angle = 5 * (M_PI / 180.0); // 5degree tolerance in normals

examples/segmentation/example_lccp_segmentation.cpp

@@ -377,8 +377,6 @@ LCCPSegmentation Parameters: \n\
     using AdjacencyIterator = LCCPSegmentation<PointT>::AdjacencyIterator;
     using EdgeID = LCCPSegmentation<PointT>::EdgeID;
 
-    std::set<EdgeID> edge_drawn;
-
     const unsigned char convex_color [3] = {255, 255, 255};
     const unsigned char concave_color [3] = {255, 0, 0};
     const unsigned char* color;

io/src/ifs_io.cpp

@@ -60,7 +60,6 @@ pcl::IFSReader::readHeader (const std::string &file_name, pcl::PCLPointCloud2 &c
 
   std::uint32_t nr_points = 0;
   std::ifstream fs;
-  std::string line;
 
   if (file_name.empty() || !boost::filesystem::exists (file_name))
   {

outofcore/src/visualization/outofcore_cloud.cpp

@@ -52,8 +52,6 @@ OutofcoreCloud::PcdQueue OutofcoreCloud::pcd_queue;
 void
 OutofcoreCloud::pcdReaderThread ()
 {
-
-  std::string pcd_file;
   std::size_t timestamp = 0;
 
   while (true)

recognition/include/pcl/recognition/impl/hv/hv_go.hpp

@@ -564,7 +564,6 @@ bool pcl::GlobalHypothesesVerification<ModelT, SceneT>::addModel(typename pcl::P
   std::vector<int> explained_indices;
   std::vector<float> outliers_weight;
   std::vector<float> explained_indices_distances;
-  std::vector<float> unexplained_indices_weights;
 
   std::vector<int> nn_indices;
   std::vector<float> nn_distances;

registration/include/pcl/registration/impl/correspondence_rejection_sample_consensus.hpp

@@ -74,9 +74,6 @@ pcl::registration::CorrespondenceRejectorSampleConsensus<PointT>::getRemainingCo
     target_indices[i] = original_correspondences[i].index_match;
   }
 
-   // from pcl/registration/icp.hpp:
-   std::vector<int> source_indices_good;
-   std::vector<int> target_indices_good;
    {
      // From the set of correspondences found, attempt to remove outliers
      // Create the registration model

registration/include/pcl/registration/impl/correspondence_rejection_sample_consensus_2d.hpp

@@ -78,10 +78,6 @@ pcl::registration::CorrespondenceRejectorSampleConsensus2D<PointT>::getRemaining
     target_indices[i] = original_correspondences[i].index_match;
   }
 
-  // from pcl/registration/icp.hpp:
-  std::vector<int> source_indices_good;
-  std::vector<int> target_indices_good;
-
   // From the set of correspondences found, attempt to remove outliers
   typename pcl::SampleConsensusModelRegistration2D<PointT>::Ptr model (new pcl::SampleConsensusModelRegistration2D<PointT> (input_, source_indices));
   // Pass the target_indices

sample_consensus/include/pcl/sample_consensus/impl/lmeds.hpp

@@ -57,7 +57,6 @@ pcl::LeastMedianSquares<PointT>::computeModel (int debug_verbosity_level)
   iterations_ = 0;
   double d_best_penalty = std::numeric_limits<double>::max();
 
-  std::vector<int> best_model;
   std::vector<int> selection;
   Eigen::VectorXf model_coefficients;
   std::vector<double> distances;

sample_consensus/include/pcl/sample_consensus/impl/mlesac.hpp

@@ -59,7 +59,6 @@ pcl::MaximumLikelihoodSampleConsensus<PointT>::computeModel (int debug_verbosity
   double d_best_penalty = std::numeric_limits<double>::max();
   double k = 1.0;
 
-  std::vector<int> best_model;
   std::vector<int> selection;
   Eigen::VectorXf model_coefficients;
   std::vector<double> distances;

sample_consensus/include/pcl/sample_consensus/impl/msac.hpp

@@ -58,7 +58,6 @@ pcl::MEstimatorSampleConsensus<PointT>::computeModel (int debug_verbosity_level)
   double d_best_penalty = std::numeric_limits<double>::max();
   double k = 1.0;
 
-  std::vector<int> best_model;
   std::vector<int> selection;
   Eigen::VectorXf model_coefficients;
   std::vector<double> distances;

sample_consensus/include/pcl/sample_consensus/impl/rmsac.hpp

@@ -58,7 +58,6 @@ pcl::RandomizedMEstimatorSampleConsensus<PointT>::computeModel (int debug_verbos
   double d_best_penalty = std::numeric_limits<double>::max();
   double k = 1.0;
 
-  std::vector<int> best_model;
   std::vector<int> selection;
   Eigen::VectorXf model_coefficients;
   std::vector<double> distances;

segmentation/include/pcl/segmentation/impl/supervoxel_clustering.hpp

@@ -585,8 +585,6 @@ pcl::SupervoxelClustering<PointT>::getLabeledCloud () const
   pcl::copyPointCloud (*input_,*labeled_cloud);
   
   typename pcl::PointCloud <PointT>::const_iterator i_input = input_->begin ();
-  std::vector <int> indices;
-  std::vector <float> sqr_distances;
   for (auto i_labeled = labeled_cloud->begin (); i_labeled != labeled_cloud->end (); ++i_labeled,++i_input)
   {
     if ( !pcl::isFinite<PointT> (*i_input))

surface/include/pcl/surface/impl/concave_hull.hpp

@@ -469,7 +469,6 @@ pcl::ConcaveHull<PointInT>::performReconstruction (PointCloud &alpha_shape, std:
 
     alpha_shape.points.resize (vertices);
 
-    std::vector<std::vector<int> > connected;
     PointCloud alpha_shape_sorted;
     alpha_shape_sorted.points.resize (vertices);
 

surface/include/pcl/surface/impl/texture_mapping.hpp

@@ -302,9 +302,6 @@ pcl::TextureMapping<PointInT>::mapMultipleTexturesToMeshUV (pcl::TextureMesh &te
   // convert mesh's cloud to pcl format for ease
   pcl::fromPCLPointCloud2 (tex_mesh.cloud, *originalCloud);
 
-  // texture coordinates for each mesh
-  std::vector<std::vector<Eigen::Vector2f, Eigen::aligned_allocator<Eigen::Vector2f> > > texture_map;
-
   for (std::size_t m = 0; m < cams.size (); ++m)
   {
     // get current camera parameters

surface/src/on_nurbs/closing_boundary.cpp

@@ -390,7 +390,6 @@ ClosingBoundary::optimizeBoundary (std::vector<ON_NurbsSurface> &nurbs_list, std
     FittingSurface fit (&data_list[n1], nurbs_list[n1]);
     FittingSurface::Parameter paramFP (1.0, param.smoothness, 0.0, 1.0, param.smoothness, 0.0);
 
-    std::vector<double> wBnd, wInt;
     for (std::size_t i = 0; i < data_list[n1].boundary.size (); i++)
       data_list[n1].boundary_weight.push_back (param.boundary_weight);
     for (std::size_t i = 0; i < data_list[n1].interior.size (); i++)

surface/src/on_nurbs/sparse_mat.cpp

@@ -207,9 +207,6 @@ SparseMat::nonzeros ()
 void
 SparseMat::printLong ()
 {
-  std::map<int, std::map<int, double> >::iterator it_row;
-  std::map<int, double>::iterator it_col;
-
   int si, sj;
   size (si, sj);
 

test/search/test_organized.cpp

@@ -189,12 +189,6 @@ TEST (PCL, Organized_Neighbor_Pointcloud_Neighbours_Within_Radius_Search)
 
   search::OrganizedNeighbor<PointXYZ> organizedNeighborSearch;
 
-  std::vector<int> k_indices;
-  std::vector<float> k_sqr_distances;
-
-  std::vector<int> k_indices_bruteforce;
-  std::vector<float> k_sqr_distances_bruteforce;
-
   // typical focal length from kinect
   constexpr double oneOverFocalLength = 0.0018;
 

test/search/test_organized_index.cpp

@@ -477,12 +477,6 @@ TEST (PCL, Organized_Neighbor_Search_Pointcloud_Neighbours_Within_Radius_Search_
 
   pcl::search::Search<PointXYZ>* organizedNeighborSearch = new pcl::search::OrganizedNeighbor<PointXYZ>();
 
-  std::vector<int> k_indices;
-  std::vector<float> k_sqr_distances;
-
-  std::vector<int> k_indices_bruteforce;
-  std::vector<float> k_sqr_distances_bruteforce;
-
   // typical focal length from kinect
   unsigned int randomIdx;
 

tools/virtual_scanner.cpp

@@ -174,7 +174,7 @@ main (int argc, char** argv)
 
   int subdiv_level = 1;
   double scan_dist = 3;
-  std::string fname, base;
+  std::string fname;
   char seq[256];
 
   // Compute start/stop for vertical and horizontal

tracking/include/pcl/tracking/impl/particle_filter.hpp

@@ -91,7 +91,6 @@ template <typename PointInT, typename StateT> void
 pcl::tracking::ParticleFilterTracker<PointInT, StateT>::initParticles (bool reset)
 {
   particles_.reset (new PointCloudState ());
-  std::vector<double> initial_noise_mean;
   if (reset)
   {
     representative_state_.zero ();