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typedef Feature< PointInT,
PointOutT >::PointCloudOut | PointCloudOut |
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typedef Feature< PointInT,
PointOutT >::PointCloudIn | PointCloudIn |
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| typedef pcl::PointCloud< PointNT > | PointCloudN |
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| typedef PointCloudN::Ptr | PointCloudNPtr |
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| typedef PointCloudN::ConstPtr | PointCloudNConstPtr |
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typedef boost::shared_ptr
< FeatureFromNormals< PointInT,
PointNT, PointOutT > > | Ptr |
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typedef boost::shared_ptr
< const FeatureFromNormals
< PointInT, PointNT, PointOutT > > | ConstPtr |
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| typedef PCLBase< PointInT > | BaseClass |
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typedef pcl::search::Search
< PointInT > | KdTree |
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typedef pcl::search::Search
< PointInT >::Ptr | KdTreePtr |
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typedef boost::function< int(size_t,
double, std::vector< int >
&, std::vector< float > &)> | SearchMethod |
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typedef boost::function< int(const
PointCloudIn &cloud, size_t
index, double, std::vector
< int > &, std::vector< float > &)> | SearchMethodSurface |
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| typedef pcl::PointCloud< PointInT > | PointCloud |
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| typedef PointCloud::Ptr | PointCloudPtr |
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| typedef PointCloud::ConstPtr | PointCloudConstPtr |
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| typedef PointIndices::Ptr | PointIndicesPtr |
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| typedef PointIndices::ConstPtr | PointIndicesConstPtr |
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|
| | PFHEstimation () |
| | Empty constructor. More...
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| void | setMaximumCacheSize (unsigned int cache_size) |
| | Set the maximum internal cache size. More...
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| unsigned int | getMaximumCacheSize () |
| | Get the maximum internal cache size. More...
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| |
| void | setUseInternalCache (bool use_cache) |
| | Set whether to use an internal cache mechanism for removing redundant calculations or not. More...
|
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| bool | getUseInternalCache () |
| | Get whether the internal cache is used or not for computing the PFH features. More...
|
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| bool | computePairFeatures (const pcl::PointCloud< PointInT > &cloud, const pcl::PointCloud< PointNT > &normals, int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4) |
| | Compute the 4-tuple representation containing the three angles and one distance between two points represented by Cartesian coordinates and normals. More...
|
| |
| void | computePointPFHSignature (const pcl::PointCloud< PointInT > &cloud, const pcl::PointCloud< PointNT > &normals, const std::vector< int > &indices, int nr_split, Eigen::VectorXf &pfh_histogram) |
| | Estimate the PFH (Point Feature Histograms) individual signatures of the three angular (f1, f2, f3) features for a given point based on its spatial neighborhood of 3D points with normals. More...
|
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| void | setInputNormals (const PointCloudNConstPtr &normals) |
| | Provide a pointer to the input dataset that contains the point normals of the XYZ dataset. More...
|
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| PointCloudNConstPtr | getInputNormals () const |
| | Get a pointer to the normals of the input XYZ point cloud dataset. More...
|
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| void | setSearchSurface (const PointCloudInConstPtr &cloud) |
| | Provide a pointer to a dataset to add additional information to estimate the features for every point in the input dataset. More...
|
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| PointCloudInConstPtr | getSearchSurface () const |
| | Get a pointer to the surface point cloud dataset. More...
|
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| void | setSearchMethod (const KdTreePtr &tree) |
| | Provide a pointer to the search object. More...
|
| |
| KdTreePtr | getSearchMethod () const |
| | Get a pointer to the search method used. More...
|
| |
| double | getSearchParameter () const |
| | Get the internal search parameter. More...
|
| |
| void | setKSearch (int k) |
| | Set the number of k nearest neighbors to use for the feature estimation. More...
|
| |
| int | getKSearch () const |
| | get the number of k nearest neighbors used for the feature estimation. More...
|
| |
| void | setRadiusSearch (double radius) |
| | Set the sphere radius that is to be used for determining the nearest neighbors used for the feature estimation. More...
|
| |
| double | getRadiusSearch () const |
| | Get the sphere radius used for determining the neighbors. More...
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| |
| void | compute (PointCloudOut &output) |
| | Base method for feature estimation for all points given in <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in setSearchMethod () More...
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| void | computeEigen (pcl::PointCloud< Eigen::MatrixXf > &output) |
| | Base method for feature estimation for all points given in <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in setSearchMethod () More...
|
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| virtual void | setInputCloud (const PointCloudConstPtr &cloud) |
| | Provide a pointer to the input dataset. More...
|
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| PointCloudConstPtr const | getInputCloud () |
| | Get a pointer to the input point cloud dataset. More...
|
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| void | setIndices (const IndicesPtr &indices) |
| | Provide a pointer to the vector of indices that represents the input data. More...
|
| |
| void | setIndices (const IndicesConstPtr &indices) |
| | Provide a pointer to the vector of indices that represents the input data. More...
|
| |
| void | setIndices (const PointIndicesConstPtr &indices) |
| | Provide a pointer to the vector of indices that represents the input data. More...
|
| |
| void | setIndices (size_t row_start, size_t col_start, size_t nb_rows, size_t nb_cols) |
| | Set the indices for the points laying within an interest region of the point cloud. More...
|
| |
| IndicesPtr const | getIndices () |
| | Get a pointer to the vector of indices used. More...
|
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| const PointInT & | operator[] (size_t pos) |
| | Override PointCloud operator[] to shorten code. More...
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| |
template<typename PointInT, typename PointNT, typename PointOutT = pcl::PFHSignature125>
class pcl::PFHEstimation< PointInT, PointNT, PointOutT >
PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals.
A commonly used type for PointOutT is pcl::PFHSignature125.
- Note
- If you use this code in any academic work, please cite:
- R.B. Rusu, N. Blodow, Z.C. Marton, M. Beetz. Aligning Point Cloud Views using Persistent Feature Histograms. In Proceedings of the 21st IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nice, France, September 22-26 2008.
- R.B. Rusu, Z.C. Marton, N. Blodow, M. Beetz. Learning Informative Point Classes for the Acquisition of Object Model Maps. In Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision (ICARCV), Hanoi, Vietnam, December 17-20 2008.
- Attention
- The convention for PFH features is:
- if a query point's nearest neighbors cannot be estimated, the PFH feature will be set to NaN (not a number)
- it is impossible to estimate a PFH descriptor for a point that doesn't have finite 3D coordinates. Therefore, any point that contains NaN data on x, y, or z, will have its PFH feature property set to NaN.
- Note
- The code is stateful as we do not expect this class to be multicore parallelized. Please look at FPFHEstimationOMP for examples on parallel implementations of the FPFH (Fast Point Feature Histogram).
- Author
- Radu B. Rusu
Definition at line 101 of file pfh.h.
template<typename PointInT, typename PointNT, typename PointOutT>
Provide a pointer to the input dataset that contains the point normals of the XYZ dataset.
In case of search surface is set to be different from the input cloud, normals should correspond to the search surface, not the input cloud!
- Parameters
-
| [in] | normals | the const boost shared pointer to a PointCloud of normals. By convention, L2 norm of each normal should be 1. |
Definition at line 351 of file feature.h.
template<typename PointInT, typename PointOutT>
Provide a pointer to a dataset to add additional information to estimate the features for every point in the input dataset.
This is optional, if this is not set, it will only use the data in the input cloud to estimate the features. This is useful when you only need to compute the features for a downsampled cloud.
- Parameters
-
Definition at line 144 of file feature.h.
1.8.4