Point Cloud Library (PCL)  1.3.1
ppf.hpp
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00001 /*
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00035  * $Id: ppf.hpp 3022 2011-11-01 03:42:22Z rusu $
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00037 
00038 #ifndef PCL_FEATURES_IMPL_PPF_H_
00039 #define PCL_FEATURES_IMPL_PPF_H_
00040 
00041 #include "pcl/features/ppf.h"
00042 #include "pcl/features/pfh.h"
00043 
00045 template <typename PointInT, typename PointNT, typename PointOutT>
00046 pcl::PPFEstimation<PointInT, PointNT, PointOutT>::PPFEstimation ()
00047     : FeatureFromNormals <PointInT, PointNT, PointOutT> ()
00048 {
00049   feature_name_ = "PPFEstimation";
00050   // Slight hack in order to pass the check for the presence of a search method in Feature::initCompute ()
00051   Feature<PointInT, PointOutT>::tree_.reset (new pcl::search::KdTree <PointInT> ());
00052   Feature<PointInT, PointOutT>::search_radius_ = 1.0f;
00053 };
00054 
00055 
00057 template <typename PointInT, typename PointNT, typename PointOutT> void
00058 pcl::PPFEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
00059 {
00060   // Initialize output container - overwrite the sizes done by Feature::initCompute ()
00061   output.points.resize (indices_->size () * input_->points.size ());
00062   output.height = 1;
00063   output.width = output.points.size ();
00064 
00065   // Compute point pair features for every pair of points in the cloud
00066   for (size_t index_i = 0; index_i < indices_->size (); ++index_i)
00067   {
00068     size_t i = (*indices_)[index_i];
00069     for (size_t j = 0 ; j < input_->points.size (); ++j)
00070     {
00071       PointOutT p;
00072       if (i != j)
00073       {
00074         if (//pcl::computePPFPairFeature
00075             pcl::computePairFeatures
00076             (input_->points[i].getVector4fMap (),
00077                                    normals_->points[i].getNormalVector4fMap (),
00078                                    input_->points[j].getVector4fMap (),
00079                                    normals_->points[j].getNormalVector4fMap (),
00080                                    p.f1, p.f2, p.f3, p.f4))
00081         {
00082           // Calculate alpha_m angle
00083           Eigen::Vector3f model_reference_point = input_->points[i].getVector3fMap (),
00084                           model_reference_normal = normals_->points[i].getNormalVector3fMap (),
00085                           model_point = input_->points[j].getVector3fMap ();
00086           Eigen::AngleAxisf rotation_mg (acos (model_reference_normal.dot (Eigen::Vector3f::UnitX ())),
00087                                          model_reference_normal.cross (Eigen::Vector3f::UnitX ()).normalized ());
00088           Eigen::Affine3f transform_mg = Eigen::Translation3f ( rotation_mg * ((-1) * model_reference_point)) * rotation_mg;
00089 
00090           Eigen::Vector3f model_point_transformed = transform_mg * model_point;
00091           float angle = atan2f ( -model_point_transformed(2), model_point_transformed(1));
00092           if (sin (angle) * model_point_transformed(2) < 0.0f)
00093             angle *= (-1);
00094           p.alpha_m = -angle;
00095         }
00096         else
00097         {
00098           PCL_ERROR ("[pcl::%s::computeFeature] Computing pair feature vector between points %lu and %lu went wrong.\n", getClassName ().c_str (), (unsigned long) i, (unsigned long) j);
00099           p.f1 = p.f2 = p.f3 = p.f4 = p.alpha_m = 0.0;
00100         }
00101       }
00102       // Do not calculate the feature for identity pairs (i, i) as they are not used
00103       // in the following computations
00104       else
00105         p.f1 = p.f2 = p.f3 = p.f4 = p.alpha_m = 0.0;
00106 
00107       output.points[index_i*input_->points.size () + j] = p;
00108     }
00109   }
00110 }
00111 
00112 #define PCL_INSTANTIATE_PPFEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PPFEstimation<T,NT,OutT>;
00113 
00114 
00115 #endif // PCL_FEATURES_IMPL_PPF_H_
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