Classes | |
| struct | pcl::_PointXYZI |
| A point structure representing Euclidean xyz coordinates, and the intensity value. More... | |
| struct | pcl::_ReferenceFrame |
| A structure representing the Local Reference Frame of a point. More... | |
| class | mets::abstract_search< move_manager_type > |
| An abstract search. More... | |
| struct | pcl::Axis |
| A point structure representing an Axis using its normal coordinates. (SSE friendly) More... | |
| class | mets::best_ever_solution |
| The best ever solution recorder can be used as a simple solution recorder that just records the best copyable solution found during its lifetime. More... | |
| class | pcl::BivariatePolynomialT< real > |
| This represents a bivariate polynomial and provides some functionality for it. More... | |
| struct | pcl::BorderDescription |
| A structure to store if a point in a range image lies on a border between an obstacle and the background. More... | |
| struct | pcl::Boundary |
| A point structure representing a description of whether a point is lying on a surface boundary or not. More... | |
| struct | pcl::Correspondence |
| Correspondence represents a match between two entities (e.g., points, descriptors, etc). This is represesented via the indices of a source point and a target point, and the distance between them. More... | |
| struct | pcl::ESFSignature640 |
| A point structure representing the Ensemble of Shape Functions (ESF). More... | |
| class | mets::forever |
| struct | pcl::FPFHSignature33 |
| A point structure representing the Fast Point Feature Histogram (FPFH). More... | |
| class | pcl::GaussianKernel |
| struct | pcl::GFPFHSignature16 |
| A point structure representing the GFPFH descriptor with 16 bins. More... | |
| struct | pcl::GradientXY |
| A point structure representing Euclidean xyz coordinates, and the intensity value. More... | |
| struct | pcl::Histogram< N > |
| A point structure representing an N-D histogram. More... | |
| struct | mets::improvement_logger< neighborhood_t > |
| struct | pcl::Intensity |
| A point structure representing the grayscale intensity in single-channel images. Intensity is represented as a float value. More... | |
| struct | pcl::Intensity32u |
| A point structure representing the grayscale intensity in single-channel images. Intensity is represented as a uint8_t value. More... | |
| struct | pcl::Intensity8u |
| A point structure representing the grayscale intensity in single-channel images. Intensity is represented as a uint8_t value. More... | |
| struct | pcl::IntensityGradient |
| A point structure representing the intensity gradient of an XYZI point cloud. More... | |
| struct | pcl::InterestPoint |
| A point structure representing an interest point with Euclidean xyz coordinates, and an interest value. More... | |
| struct | mets::iteration_logger< neighborhood_t > |
| class | mets::iteration_termination_criteria |
| Termination criteria based on the number of iterations. More... | |
| struct | pcl::MomentInvariants |
| A point structure representing the three moment invariants. More... | |
| struct | pcl::Narf36 |
| A point structure representing the Narf descriptor. More... | |
| struct | pcl::NdConcatenateFunctor< PointInT, PointOutT > |
| Helper functor structure for concatenate. More... | |
| class | mets::noimprove_termination_criteria |
| Termination criteria based on the number of iterations without an improvement. More... | |
| struct | pcl::Normal |
| A point structure representing normal coordinates and the surface curvature estimate. (SSE friendly) More... | |
| struct | pcl::NormalBasedSignature12 |
| A point structure representing the Normal Based Signature for a feature matrix of 4-by-3. More... | |
| class | pcl::PCA< PointT > |
| class | pcl::PCLBase< PointT > |
| PCL base class. Implements methods that are used by most PCL algorithms. More... | |
| struct | pcl::PFHRGBSignature250 |
| A point structure representing the Point Feature Histogram with colors (PFHRGB). More... | |
| struct | pcl::PFHSignature125 |
| A point structure representing the Point Feature Histogram (PFH). More... | |
| class | pcl::PiecewiseLinearFunction |
| This provides functionalities to efficiently return values for piecewise linear function. More... | |
| struct | pcl::PointCorrespondence3D |
| Representation of a (possible) correspondence between two 3D points in two different coordinate frames (e.g. from feature matching) More... | |
| struct | pcl::PointCorrespondence6D |
| Representation of a (possible) correspondence between two points (e.g. from feature matching), that encode complete 6DOF transoformations. More... | |
| struct | pcl::PointNormal |
| A point structure representing Euclidean xyz coordinates, together with normal coordinates and the surface curvature estimate. (SSE friendly) More... | |
| struct | pcl::PointRGB |
| A point structure for representing RGB color. More... | |
| struct | pcl::PointSurfel |
| A surfel, that is, a point structure representing Euclidean xyz coordinates, together with normal coordinates, a RGBA color, a radius, a confidence value and the surface curvature estimate. More... | |
| struct | pcl::PointUV |
| A 2D point structure representing pixel image coordinates. More... | |
| struct | pcl::PointWithRange |
| A point structure representing Euclidean xyz coordinates, padded with an extra range float. More... | |
| struct | pcl::PointWithScale |
| A point structure representing a 3-D position and scale. More... | |
| struct | pcl::PointWithViewpoint |
| A point structure representing Euclidean xyz coordinates together with the viewpoint from which it was seen. More... | |
| struct | pcl::PointXY |
| A 2D point structure representing Euclidean xy coordinates. More... | |
| struct | pcl::PointXYZ |
| A point structure representing Euclidean xyz coordinates. (SSE friendly) More... | |
| struct | pcl::PointXYZINormal |
| A point structure representing Euclidean xyz coordinates, intensity, together with normal coordinates and the surface curvature estimate. More... | |
| struct | pcl::PointXYZRGB |
| A point structure representing Euclidean xyz coordinates, and the RGB color. More... | |
| struct | pcl::PointXYZRGBA |
| A point structure representing Euclidean xyz coordinates, and the RGBA color. More... | |
| struct | pcl::PointXYZRGBNormal |
| A point structure representing Euclidean xyz coordinates, and the RGB color, together with normal coordinates and the surface curvature estimate. Due to historical reasons (PCL was first developed as a ROS package), the RGB information is packed into an integer and casted to a float. This is something we wish to remove in the near future, but in the meantime, the following code snippet should help you pack and unpack RGB colors in your PointXYZRGB structure: More... | |
| class | pcl::PolynomialCalculationsT< real > |
| This provides some functionality for polynomials, like finding roots or approximating bivariate polynomials. More... | |
| class | pcl::PosesFromMatches |
| calculate 3D transformation based on point correspondencdes More... | |
| struct | pcl::PPFRGBSignature |
| A point structure for storing the Point Pair Color Feature (PPFRGB) values. More... | |
| struct | pcl::PPFSignature |
| A point structure for storing the Point Pair Feature (PPF) values. More... | |
| struct | pcl::PrincipalCurvatures |
| A point structure representing the principal curvatures and their magnitudes. More... | |
| struct | pcl::PrincipalRadiiRSD |
| A point structure representing the minimum and maximum surface radii (in meters) computed using RSD. More... | |
| class | pcl::ScopeTime |
| Class to measure the time spent in a scope. More... | |
| class | mets::search_listener< move_manager_type > |
| An object that is called back during the search progress. More... | |
| struct | pcl::ShapeContext1980 |
| A point structure representing a Shape Context. More... | |
| struct | pcl::SHOT1344 |
| A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape+color. More... | |
| struct | pcl::SHOT352 |
| A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape only. More... | |
| class | mets::solution_recorder |
| The solution recorder is used by search algorithm, at the end of each iteration, to record the best seen solution. More... | |
| class | pcl::StopWatch |
| Simple stopwatch. More... | |
| class | mets::termination_criteria_chain |
| Function object expressing a termination criteria. More... | |
| class | mets::threshold_termination_criteria |
| Termination criteria based on cost value. More... | |
| class | pcl::TimeTrigger |
| Timer class that invokes registered callback methods periodically. More... | |
| class | pcl::TransformationFromCorrespondences |
| Calculates a transformation based on corresponding 3D points. More... | |
| class | pcl::VectorAverage< real, dimension > |
| Calculates the weighted average and the covariance matrix. More... | |
| struct | pcl::VFHSignature308 |
| A point structure representing the Viewpoint Feature Histogram (VFH). More... | |
Files | |
| file | angles.h |
| file | centroid.h |
| file | common/include/pcl/common/common.h |
| file | common/include/pcl/common/distances.h |
| file | common/include/pcl/common/file_io.h |
| file | common/include/pcl/common/geometry.h |
| file | common/include/pcl/common/geometry.h |
| file | intersections.h |
| file | norms.h |
| file | common/include/pcl/point_types.h |
| file | random.h |
CloudGenerator class generates a point cloud using some randoom number generator. Generators can be found in and easily extensible. | |
| file | common/time.h |
Typedefs | |
| typedef std::bitset< 32 > | pcl::BorderTraits |
| Data type to store extended information about a transition from foreground to backgroundSpecification of the fields for BorderDescription::traits. | |
Enumerations | |
| enum | pcl::BorderTrait { pcl::BORDER_TRAIT__OBSTACLE_BORDER, pcl::BORDER_TRAIT__SHADOW_BORDER, pcl::BORDER_TRAIT__VEIL_POINT, pcl::BORDER_TRAIT__SHADOW_BORDER_TOP, pcl::BORDER_TRAIT__SHADOW_BORDER_RIGHT, pcl::BORDER_TRAIT__SHADOW_BORDER_BOTTOM, pcl::BORDER_TRAIT__SHADOW_BORDER_LEFT, pcl::BORDER_TRAIT__OBSTACLE_BORDER_TOP, pcl::BORDER_TRAIT__OBSTACLE_BORDER_RIGHT, pcl::BORDER_TRAIT__OBSTACLE_BORDER_BOTTOM, pcl::BORDER_TRAIT__OBSTACLE_BORDER_LEFT, pcl::BORDER_TRAIT__VEIL_POINT_TOP, pcl::BORDER_TRAIT__VEIL_POINT_RIGHT, pcl::BORDER_TRAIT__VEIL_POINT_BOTTOM, pcl::BORDER_TRAIT__VEIL_POINT_LEFT } |
| Specification of the fields for BorderDescription::traits. More... | |
| enum | pcl::NormType { pcl::L1, pcl::L2_SQR, pcl::L2, pcl::LINF, pcl::JM, pcl::B, pcl::SUBLINEAR, pcl::CS, pcl::DIV, pcl::PF, pcl::K, pcl::KL, pcl::HIK } |
| Enum that defines all the types of norms available. More... | |
Functions | |
| template<typename FloatVectorT > | |
| float | pcl::B_Norm (FloatVectorT A, FloatVectorT B, int dim) |
| Compute the B norm of the vector between two points. | |
| template<typename PointT > | |
| float | pcl::calculatePolygonArea (const pcl::PointCloud< PointT > &polygon) |
| Calculate the area of a polygon given a point cloud that defines the polygon. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::compute3DCentroid (ConstCloudIterator< PointT > &cloud_iterator, Eigen::Matrix< Scalar, 4, 1 > ¢roid) |
| Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::compute3DCentroid (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, 4, 1 > ¢roid) |
| Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::compute3DCentroid (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Matrix< Scalar, 4, 1 > ¢roid) |
| Compute the 3D (X-Y-Z) centroid of a set of points using their indices and return it as a 3D vector. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::compute3DCentroid (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Matrix< Scalar, 4, 1 > ¢roid) |
| Compute the 3D (X-Y-Z) centroid of a set of points using their indices and return it as a 3D vector. | |
| template<typename Matrix , typename Vector > | |
| void | pcl::computeCorrespondingEigenVector (const Matrix &mat, const typename Matrix::Scalar &eigenvalue, Vector &eigenvector) |
| determines the corresponding eigenvector to the given eigenvalue of the symmetric positive semi definite input matrix | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const Eigen::Matrix< Scalar, 4, 1 > ¢roid, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix) |
| Compute the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, const Eigen::Matrix< Scalar, 4, 1 > ¢roid, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix) |
| Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, const Eigen::Matrix< Scalar, 4, 1 > ¢roid, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix) |
| Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix) |
| Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix) |
| Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix) |
| Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const Eigen::Matrix< Scalar, 4, 1 > ¢roid, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix) |
| Compute normalized the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of points in the point cloud. For small number of points, or if you want explicitely the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, const Eigen::Matrix< Scalar, 4, 1 > ¢roid, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix) |
| Compute the normalized 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, const Eigen::Matrix< Scalar, 4, 1 > ¢roid, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix) |
| Compute the normalized 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix, Eigen::Matrix< Scalar, 4, 1 > ¢roid) |
| Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix, Eigen::Matrix< Scalar, 4, 1 > ¢roid) |
| Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function. | |
| template<typename PointT , typename Scalar > | |
| unsigned int | pcl::computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix, Eigen::Matrix< Scalar, 4, 1 > ¢roid) |
| Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::computeNDCentroid (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > ¢roid) |
| General, all purpose nD centroid estimation for a set of points using their indices. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::computeNDCentroid (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > ¢roid) |
| General, all purpose nD centroid estimation for a set of points using their indices. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::computeNDCentroid (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > ¢roid) |
| General, all purpose nD centroid estimation for a set of points using their indices. | |
| template<typename PointIn1T , typename PointIn2T , typename PointOutT > | |
| void | pcl::concatenateFields (const pcl::PointCloud< PointIn1T > &cloud1_in, const pcl::PointCloud< PointIn2T > &cloud2_in, pcl::PointCloud< PointOutT > &cloud_out) |
| Concatenate two datasets representing different fields. | |
| PCL_EXPORTS bool | pcl::concatenateFields (const pcl::PCLPointCloud2 &cloud1_in, const pcl::PCLPointCloud2 &cloud2_in, pcl::PCLPointCloud2 &cloud_out) |
| Concatenate two datasets representing different fields. | |
| PCL_EXPORTS bool | pcl::concatenatePointCloud (const pcl::PCLPointCloud2 &cloud1, const pcl::PCLPointCloud2 &cloud2, pcl::PCLPointCloud2 &cloud_out) |
| Concatenate two pcl::PCLPointCloud2. | |
| PCL_EXPORTS void | pcl::copyPointCloud (const pcl::PCLPointCloud2 &cloud_in, const std::vector< int > &indices, pcl::PCLPointCloud2 &cloud_out) |
| Extract the indices of a given point cloud as a new point cloud. | |
| PCL_EXPORTS void | pcl::copyPointCloud (const pcl::PCLPointCloud2 &cloud_in, const std::vector< int, Eigen::aligned_allocator< int > > &indices, pcl::PCLPointCloud2 &cloud_out) |
| Extract the indices of a given point cloud as a new point cloud. | |
| PCL_EXPORTS void | pcl::copyPointCloud (const pcl::PCLPointCloud2 &cloud_in, pcl::PCLPointCloud2 &cloud_out) |
| Copy fields and point cloud data from cloud_in to cloud_out. | |
| template<typename PointT > | |
| void | pcl::copyPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointT > &cloud_out) |
| Extract the indices of a given point cloud as a new point cloud. | |
| template<typename PointT > | |
| void | pcl::copyPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int, Eigen::aligned_allocator< int > > &indices, pcl::PointCloud< PointT > &cloud_out) |
| Extract the indices of a given point cloud as a new point cloud. | |
| template<typename PointT > | |
| void | pcl::copyPointCloud (const pcl::PointCloud< PointT > &cloud_in, const PointIndices &indices, pcl::PointCloud< PointT > &cloud_out) |
| Extract the indices of a given point cloud as a new point cloud. | |
| template<typename PointT > | |
| void | pcl::copyPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< pcl::PointIndices > &indices, pcl::PointCloud< PointT > &cloud_out) |
| Extract the indices of a given point cloud as a new point cloud. | |
| template<typename PointInT , typename PointOutT > | |
| void | pcl::copyPointCloud (const pcl::PointCloud< PointInT > &cloud_in, pcl::PointCloud< PointOutT > &cloud_out) |
| Copy all the fields from a given point cloud into a new point cloud. | |
| template<typename PointInT , typename PointOutT > | |
| void | pcl::copyPointCloud (const pcl::PointCloud< PointInT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointOutT > &cloud_out) |
| Extract the indices of a given point cloud as a new point cloud. | |
| template<typename PointInT , typename PointOutT > | |
| void | pcl::copyPointCloud (const pcl::PointCloud< PointInT > &cloud_in, const std::vector< int, Eigen::aligned_allocator< int > > &indices, pcl::PointCloud< PointOutT > &cloud_out) |
| Extract the indices of a given point cloud as a new point cloud. | |
| template<typename PointInT , typename PointOutT > | |
| void | pcl::copyPointCloud (const pcl::PointCloud< PointInT > &cloud_in, const PointIndices &indices, pcl::PointCloud< PointOutT > &cloud_out) |
| Extract the indices of a given point cloud as a new point cloud. | |
| template<typename PointInT , typename PointOutT > | |
| void | pcl::copyPointCloud (const pcl::PointCloud< PointInT > &cloud_in, const std::vector< pcl::PointIndices > &indices, pcl::PointCloud< PointOutT > &cloud_out) |
| Extract the indices of a given point cloud as a new point cloud. | |
| template<typename FloatVectorT > | |
| float | pcl::CS_Norm (FloatVectorT A, FloatVectorT B, int dim) |
| Compute the CS norm of the vector between two points. | |
| float | pcl::deg2rad (float alpha) |
| Convert an angle from degrees to radians. | |
| double | pcl::deg2rad (double alpha) |
| Convert an angle from degrees to radians. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::demeanPointCloud (ConstCloudIterator< PointT > &cloud_iterator, const Eigen::Matrix< Scalar, 4, 1 > ¢roid, pcl::PointCloud< PointT > &cloud_out, int npts=0) |
| Subtract a centroid from a point cloud and return the de-meaned representation. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const Eigen::Matrix< Scalar, 4, 1 > ¢roid, pcl::PointCloud< PointT > &cloud_out) |
| Subtract a centroid from a point cloud and return the de-meaned representation. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, const Eigen::Matrix< Scalar, 4, 1 > ¢roid, pcl::PointCloud< PointT > &cloud_out) |
| Subtract a centroid from a point cloud and return the de-meaned representation. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, const Eigen::Matrix< Scalar, 4, 1 > ¢roid, pcl::PointCloud< PointT > &cloud_out) |
| Subtract a centroid from a point cloud and return the de-meaned representation. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::demeanPointCloud (ConstCloudIterator< PointT > &cloud_iterator, const Eigen::Matrix< Scalar, 4, 1 > ¢roid, Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > &cloud_out, int npts=0) |
| Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const Eigen::Matrix< Scalar, 4, 1 > ¢roid, Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > &cloud_out) |
| Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, const Eigen::Matrix< Scalar, 4, 1 > ¢roid, Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > &cloud_out) |
| Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, const Eigen::Matrix< Scalar, 4, 1 > ¢roid, Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > &cloud_out) |
| Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix. | |
| template<typename FloatVectorT > | |
| float | pcl::Div_Norm (FloatVectorT A, FloatVectorT B, int dim) |
| Compute the div norm of the vector between two points. | |
| template<typename Matrix , typename Vector > | |
| void | pcl::eigen22 (const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector) |
| determine the smallest eigenvalue and its corresponding eigenvector | |
| template<typename Matrix , typename Vector > | |
| void | pcl::eigen22 (const Matrix &mat, Matrix &eigenvectors, Vector &eigenvalues) |
| determine the smallest eigenvalue and its corresponding eigenvector | |
| template<typename Matrix , typename Vector > | |
| void | pcl::eigen33 (const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector) |
| determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi definite input matrix | |
| template<typename Matrix , typename Vector > | |
| void | pcl::eigen33 (const Matrix &mat, Vector &evals) |
| determines the eigenvalues of the symmetric positive semi definite input matrix | |
| template<typename Matrix , typename Vector > | |
| void | pcl::eigen33 (const Matrix &mat, Matrix &evecs, Vector &evals) |
| determines the eigenvalues and corresponding eigenvectors of the symmetric positive semi definite input matrix | |
| double | pcl::getAngle3D (const Eigen::Vector4f &v1, const Eigen::Vector4f &v2) |
| Compute the smallest angle between two vectors in the [ 0, PI ) interval in 3D. | |
| template<typename PointT > | |
| double | pcl::getCircumcircleRadius (const PointT &pa, const PointT &pb, const PointT &pc) |
| Compute the radius of a circumscribed circle for a triangle formed of three points pa, pb, and pc. | |
| PCL_EXPORTS bool | pcl::getEigenAsPointCloud (Eigen::MatrixXf &in, pcl::PCLPointCloud2 &out) |
| Copy the XYZ dimensions from an Eigen MatrixXf into a pcl::PCLPointCloud2 message. | |
| void | pcl::getEulerAngles (const Eigen::Affine3f &t, float &roll, float &pitch, float &yaw) |
| Extract the Euler angles (XYZ-convention) from the given transformation. | |
| int | pcl::getFieldIndex (const pcl::PCLPointCloud2 &cloud, const std::string &field_name) |
| Get the index of a specified field (i.e., dimension/channel) | |
| template<typename PointT > | |
| int | pcl::getFieldIndex (const pcl::PointCloud< PointT > &cloud, const std::string &field_name, std::vector< pcl::PCLPointField > &fields) |
| Get the index of a specified field (i.e., dimension/channel) | |
| template<typename PointT > | |
| int | pcl::getFieldIndex (const std::string &field_name, std::vector< pcl::PCLPointField > &fields) |
| Get the index of a specified field (i.e., dimension/channel) | |
| template<typename PointT > | |
| void | pcl::getFields (const pcl::PointCloud< PointT > &cloud, std::vector< pcl::PCLPointField > &fields) |
| Get the list of available fields (i.e., dimension/channel) | |
| template<typename PointT > | |
| void | pcl::getFields (std::vector< pcl::PCLPointField > &fields) |
| Get the list of available fields (i.e., dimension/channel) | |
| int | pcl::getFieldSize (const int datatype) |
| Obtains the size of a specific field data type in bytes. | |
| template<typename PointT > | |
| std::string | pcl::getFieldsList (const pcl::PointCloud< PointT > &cloud) |
| Get the list of all fields available in a given cloud. | |
| std::string | pcl::getFieldsList (const pcl::PCLPointCloud2 &cloud) |
| Get the available point cloud fields as a space separated string. | |
| int | pcl::getFieldType (const int size, char type) |
| Obtains the type of the PCLPointField from a specific size and type. | |
| char | pcl::getFieldType (const int type) |
| Obtains the type of the PCLPointField from a specific PCLPointField as a char. | |
| template<typename PointT > | |
| void | pcl::getMaxDistance (const pcl::PointCloud< PointT > &cloud, const Eigen::Vector4f &pivot_pt, Eigen::Vector4f &max_pt) |
| Get the point at maximum distance from a given point and a given pointcloud. | |
| template<typename PointT > | |
| void | pcl::getMaxDistance (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, const Eigen::Vector4f &pivot_pt, Eigen::Vector4f &max_pt) |
| Get the point at maximum distance from a given point and a given pointcloud. | |
| template<typename PointT > | |
| double | pcl::getMaxSegment (const pcl::PointCloud< PointT > &cloud, PointT &pmin, PointT &pmax) |
| Obtain the maximum segment in a given set of points, and return the minimum and maximum points. | |
| template<typename PointT > | |
| double | pcl::getMaxSegment (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, PointT &pmin, PointT &pmax) |
| Obtain the maximum segment in a given set of points, and return the minimum and maximum points. | |
| void | pcl::getMeanStd (const std::vector< float > &values, double &mean, double &stddev) |
| Compute both the mean and the standard deviation of an array of values. | |
| PCL_EXPORTS void | pcl::getMeanStdDev (const std::vector< float > &values, double &mean, double &stddev) |
| Compute both the mean and the standard deviation of an array of values. | |
| template<typename PointT > | |
| void | pcl::getMinMax (const PointT &histogram, int len, float &min_p, float &max_p) |
| Get the minimum and maximum values on a point histogram. | |
| PCL_EXPORTS void | pcl::getMinMax (const pcl::PCLPointCloud2 &cloud, int idx, const std::string &field_name, float &min_p, float &max_p) |
| Get the minimum and maximum values on a point histogram. | |
| template<typename PointT > | |
| void | pcl::getMinMax3D (const pcl::PointCloud< PointT > &cloud, PointT &min_pt, PointT &max_pt) |
| Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud. | |
| template<typename PointT > | |
| void | pcl::getMinMax3D (const pcl::PointCloud< PointT > &cloud, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt) |
| Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud. | |
| template<typename PointT > | |
| void | pcl::getMinMax3D (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt) |
| Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud. | |
| template<typename PointT > | |
| void | pcl::getMinMax3D (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt) |
| Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud. | |
| PCL_EXPORTS bool | pcl::getPointCloudAsEigen (const pcl::PCLPointCloud2 &in, Eigen::MatrixXf &out) |
| Copy the XYZ dimensions of a pcl::PCLPointCloud2 into Eigen format. | |
| template<typename PointT > | |
| void | pcl::getPointsInBox (const pcl::PointCloud< PointT > &cloud, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt, std::vector< int > &indices) |
| Get a set of points residing in a box given its bounds. | |
| template<typename Scalar > | |
| void | pcl::getTransformation (Scalar x, Scalar y, Scalar z, Scalar roll, Scalar pitch, Scalar yaw, Eigen::Transform< Scalar, 3, Eigen::Affine > &t) |
| Create a transformation from the given translation and Euler angles (XYZ-convention) | |
| Eigen::Affine3f | pcl::getTransformation (float x, float y, float z, float roll, float pitch, float yaw) |
| Create a transformation from the given translation and Euler angles (XYZ-convention) | |
| void | pcl::getTransformationFromTwoUnitVectors (const Eigen::Vector3f &y_direction, const Eigen::Vector3f &z_axis, Eigen::Affine3f &transformation) |
| Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis) | |
| Eigen::Affine3f | pcl::getTransformationFromTwoUnitVectors (const Eigen::Vector3f &y_direction, const Eigen::Vector3f &z_axis) |
| Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis) | |
| void | pcl::getTransformationFromTwoUnitVectorsAndOrigin (const Eigen::Vector3f &y_direction, const Eigen::Vector3f &z_axis, const Eigen::Vector3f &origin, Eigen::Affine3f &transformation) |
| Get the transformation that will translate orign to (0,0,0) and rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis) | |
| void | pcl::getTransFromUnitVectorsXY (const Eigen::Vector3f &x_axis, const Eigen::Vector3f &y_direction, Eigen::Affine3f &transformation) |
| Get the unique 3D rotation that will rotate x_axis into (1,0,0) and y_direction into a vector with z=0 (or into (0,1,0) should y_direction be orthogonal to z_axis) | |
| Eigen::Affine3f | pcl::getTransFromUnitVectorsXY (const Eigen::Vector3f &x_axis, const Eigen::Vector3f &y_direction) |
| Get the unique 3D rotation that will rotate x_axis into (1,0,0) and y_direction into a vector with z=0 (or into (0,1,0) should y_direction be orthogonal to z_axis) | |
| void | pcl::getTransFromUnitVectorsZY (const Eigen::Vector3f &z_axis, const Eigen::Vector3f &y_direction, Eigen::Affine3f &transformation) |
| Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis) | |
| Eigen::Affine3f | pcl::getTransFromUnitVectorsZY (const Eigen::Vector3f &z_axis, const Eigen::Vector3f &y_direction) |
| Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis) | |
| void | pcl::getTranslationAndEulerAngles (const Eigen::Affine3f &t, float &x, float &y, float &z, float &roll, float &pitch, float &yaw) |
| template<typename FloatVectorT > | |
| float | pcl::HIK_Norm (FloatVectorT A, FloatVectorT B, int dim) |
| Compute the HIK norm of the vector between two points. | |
| template<typename Matrix > | |
| Matrix::Scalar | pcl::invert2x2 (const Matrix &matrix, Matrix &inverse) |
| Calculate the inverse of a 2x2 matrix. | |
| template<typename Matrix > | |
| Matrix::Scalar | pcl::invert3x3Matrix (const Matrix &matrix, Matrix &inverse) |
| Calculate the inverse of a general 3x3 matrix. | |
| template<typename Matrix > | |
| Matrix::Scalar | pcl::invert3x3SymMatrix (const Matrix &matrix, Matrix &inverse) |
| Calculate the inverse of a 3x3 symmetric matrix. | |
| bool | pcl::isBetterCorrespondence (const Correspondence &pc1, const Correspondence &pc2) |
| Comparator to enable us to sort a vector of PointCorrespondences according to their scores using std::sort (begin(), end(), isBetterCorrespondence);. | |
| template<typename FloatVectorT > | |
| float | pcl::JM_Norm (FloatVectorT A, FloatVectorT B, int dim) |
| Compute the JM norm of the vector between two points. | |
| template<typename FloatVectorT > | |
| float | pcl::K_Norm (FloatVectorT A, FloatVectorT B, int dim, float P1, float P2) |
| Compute the K norm of the vector between two points. | |
| template<typename FloatVectorT > | |
| float | pcl::KL_Norm (FloatVectorT A, FloatVectorT B, int dim) |
| Compute the KL between two discrete probability density functions. | |
| template<typename FloatVectorT > | |
| float | pcl::L1_Norm (FloatVectorT A, FloatVectorT B, int dim) |
| Compute the L1 norm of the vector between two points. | |
| template<typename FloatVectorT > | |
| float | pcl::L2_Norm (FloatVectorT A, FloatVectorT B, int dim) |
| Compute the L2 norm of the vector between two points. | |
| template<typename FloatVectorT > | |
| float | pcl::L2_Norm_SQR (FloatVectorT A, FloatVectorT B, int dim) |
| Compute the squared L2 norm of the vector between two points. | |
| PCL_EXPORTS void | pcl::lineToLineSegment (const Eigen::VectorXf &line_a, const Eigen::VectorXf &line_b, Eigen::Vector4f &pt1_seg, Eigen::Vector4f &pt2_seg) |
| Get the shortest 3D segment between two 3D lines. | |
| PCL_EXPORTS bool | pcl::lineWithLineIntersection (const Eigen::VectorXf &line_a, const Eigen::VectorXf &line_b, Eigen::Vector4f &point, double sqr_eps=1e-4) |
| Get the intersection of a two 3D lines in space as a 3D point. | |
| PCL_EXPORTS bool | pcl::lineWithLineIntersection (const pcl::ModelCoefficients &line_a, const pcl::ModelCoefficients &line_b, Eigen::Vector4f &point, double sqr_eps=1e-4) |
| Get the intersection of a two 3D lines in space as a 3D point. | |
| template<typename FloatVectorT > | |
| float | pcl::Linf_Norm (FloatVectorT A, FloatVectorT B, int dim) |
| Compute the L-infinity norm of the vector between two points. | |
| template<typename Derived > | |
| void | pcl::loadBinary (Eigen::MatrixBase< Derived > const &matrix, std::istream &file) |
| Read a matrix from an input stream. | |
| float | pcl::normAngle (float alpha) |
| Normalize an angle to (-PI, PI]. | |
| template<typename FloatVectorT > | |
| float | pcl::PF_Norm (FloatVectorT A, FloatVectorT B, int dim, float P1, float P2) |
| Compute the PF norm of the vector between two points. | |
| float | pcl::rad2deg (float alpha) |
| Convert an angle from radians to degrees. | |
| double | pcl::rad2deg (double alpha) |
| Convert an angle from radians to degrees. | |
| template<typename Derived > | |
| void | pcl::saveBinary (const Eigen::MatrixBase< Derived > &matrix, std::ostream &file) |
| Write a matrix to an output stream. | |
| template<typename FloatVectorT > | |
| float | pcl::selectNorm (FloatVectorT A, FloatVectorT B, int dim, NormType norm_type) |
| Method that calculates any norm type available, based on the norm_type variable. | |
| double | pcl::sqrPointToLineDistance (const Eigen::Vector4f &pt, const Eigen::Vector4f &line_pt, const Eigen::Vector4f &line_dir) |
| Get the square distance from a point to a line (represented by a point and a direction) | |
| double | pcl::sqrPointToLineDistance (const Eigen::Vector4f &pt, const Eigen::Vector4f &line_pt, const Eigen::Vector4f &line_dir, const double sqr_length) |
| Get the square distance from a point to a line (represented by a point and a direction) | |
| template<typename FloatVectorT > | |
| float | pcl::Sublinear_Norm (FloatVectorT A, FloatVectorT B, int dim) |
| Compute the sublinear norm of the vector between two points. | |
| template<std::size_t N> | |
| void | pcl::io::swapByte (char *bytes) |
| swap bytes order of a char array of length N | |
| template<typename PointT , typename Scalar > | |
| PointT | pcl::transformPoint (const PointT &point, const Eigen::Transform< Scalar, 3, Eigen::Affine > &transform) |
| Transform a point with members x,y,z. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Transform< Scalar, 3, Eigen::Affine > &transform) |
| Apply an affine transform defined by an Eigen Transform. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Transform< Scalar, 3, Eigen::Affine > &transform) |
| Apply an affine transform defined by an Eigen Transform. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Transform< Scalar, 3, Eigen::Affine > &transform) |
| Apply an affine transform defined by an Eigen Transform. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform) |
| Apply a rigid transform defined by a 4x4 matrix. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform) |
| Apply a rigid transform defined by a 4x4 matrix. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform) |
| Apply a rigid transform defined by a 4x4 matrix. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 3, 1 > &offset, const Eigen::Quaternion< Scalar > &rotation) |
| Apply a rigid transform defined by a 3D offset and a quaternion. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform) |
| Transform a point cloud and rotate its normals using an Eigen transform. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform) |
| Transform a point cloud and rotate its normals using an Eigen transform. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform) |
| Transform a point cloud and rotate its normals using an Eigen transform. | |
| template<typename PointT , typename Scalar > | |
| void | pcl::transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 3, 1 > &offset, const Eigen::Quaternion< Scalar > &rotation) |
| Transform a point cloud and rotate its normals using an Eigen transform. | |
| typedef std::bitset<32> pcl::BorderTraits |
Data type to store extended information about a transition from foreground to backgroundSpecification of the fields for BorderDescription::traits.
Definition at line 263 of file common/include/pcl/point_types.h.
| enum pcl::BorderTrait |
Specification of the fields for BorderDescription::traits.
Definition at line 273 of file common/include/pcl/point_types.h.
| enum pcl::NormType |
| float pcl::B_Norm | ( | FloatVectorT | A, |
| FloatVectorT | B, | ||
| int | dim | ||
| ) | [inline] |
| float pcl::calculatePolygonArea | ( | const pcl::PointCloud< PointT > & | polygon | ) | [inline] |
Calculate the area of a polygon given a point cloud that defines the polygon.
| polygon | point cloud that contains those vertices that comprises the polygon. Vertices are stored in counterclockwise. |
Definition at line 391 of file common/include/pcl/common/impl/common.hpp.
| unsigned int pcl::compute3DCentroid | ( | ConstCloudIterator< PointT > & | cloud_iterator, |
| Eigen::Matrix< Scalar, 4, 1 > & | centroid | ||
| ) | [inline] |
Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
| [in] | cloud_iterator | an iterator over the input point cloud |
| [out] | centroid | the output centroid |
Definition at line 50 of file centroid.hpp.
| unsigned int pcl::compute3DCentroid | ( | const pcl::PointCloud< PointT > & | cloud, |
| Eigen::Matrix< Scalar, 4, 1 > & | centroid | ||
| ) | [inline] |
Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
| [in] | cloud | the input point cloud |
| [out] | centroid | the output centroid |
Definition at line 80 of file centroid.hpp.
| unsigned int pcl::compute3DCentroid | ( | const pcl::PointCloud< PointT > & | cloud, |
| const std::vector< int > & | indices, | ||
| Eigen::Matrix< Scalar, 4, 1 > & | centroid | ||
| ) | [inline] |
Compute the 3D (X-Y-Z) centroid of a set of points using their indices and return it as a 3D vector.
| [in] | cloud | the input point cloud |
| [in] | indices | the point cloud indices that need to be used |
| [out] | centroid | the output centroid |
Definition at line 127 of file centroid.hpp.
| unsigned int pcl::compute3DCentroid | ( | const pcl::PointCloud< PointT > & | cloud, |
| const pcl::PointIndices & | indices, | ||
| Eigen::Matrix< Scalar, 4, 1 > & | centroid | ||
| ) | [inline] |
Compute the 3D (X-Y-Z) centroid of a set of points using their indices and return it as a 3D vector.
| [in] | cloud | the input point cloud |
| [in] | indices | the point cloud indices that need to be used |
| [out] | centroid | the output centroid |
Definition at line 172 of file centroid.hpp.
| void pcl::computeCorrespondingEigenVector | ( | const Matrix & | mat, |
| const typename Matrix::Scalar & | eigenvalue, | ||
| Vector & | eigenvector | ||
| ) | [inline] |
determines the corresponding eigenvector to the given eigenvalue of the symmetric positive semi definite input matrix
| [in] | mat | symmetric positive semi definite input matrix |
| [in] | eigenvalue | the eigenvalue which corresponding eigenvector is to be computed |
| [out] | eigenvector | the corresponding eigenvector for the input eigenvalue |
Definition at line 266 of file common/include/pcl/common/eigen.h.
| unsigned pcl::computeCovarianceMatrix | ( | const pcl::PointCloud< PointT > & | cloud, |
| const Eigen::Matrix< Scalar, 4, 1 > & | centroid, | ||
| Eigen::Matrix< Scalar, 3, 3 > & | covariance_matrix | ||
| ) | [inline] |
Compute the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix.
| [in] | cloud | the input point cloud |
| [in] | centroid | the centroid of the set of points in the cloud |
| [out] | covariance_matrix | the resultant 3x3 covariance matrix |
Definition at line 181 of file centroid.hpp.
| unsigned int pcl::computeCovarianceMatrix | ( | const pcl::PointCloud< PointT > & | cloud, |
| const std::vector< int > & | indices, | ||
| const Eigen::Matrix< Scalar, 4, 1 > & | centroid, | ||
| Eigen::Matrix< Scalar, 3, 3 > & | covariance_matrix | ||
| ) | [inline] |
Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix.
| [in] | cloud | the input point cloud |
| [in] | indices | the point cloud indices that need to be used |
| [in] | centroid | the centroid of the set of points in the cloud |
| [out] | covariance_matrix | the resultant 3x3 covariance matrix |
Definition at line 264 of file centroid.hpp.
| unsigned int pcl::computeCovarianceMatrix | ( | const pcl::PointCloud< PointT > & | cloud, |
| const pcl::PointIndices & | indices, | ||
| const Eigen::Matrix< Scalar, 4, 1 > & | centroid, | ||
| Eigen::Matrix< Scalar, 3, 3 > & | covariance_matrix | ||
| ) | [inline] |
Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix.
| [in] | cloud | the input point cloud |
| [in] | indices | the point cloud indices that need to be used |
| [in] | centroid | the centroid of the set of points in the cloud |
| [out] | covariance_matrix | the resultant 3x3 covariance matrix |
Definition at line 335 of file centroid.hpp.
| unsigned int pcl::computeCovarianceMatrix | ( | const pcl::PointCloud< PointT > & | cloud, |
| Eigen::Matrix< Scalar, 3, 3 > & | covariance_matrix | ||
| ) | [inline] |
Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
| [in] | cloud | the input point cloud |
| [out] | covariance_matrix | the resultant 3x3 covariance matrix |
Definition at line 373 of file centroid.hpp.
| unsigned int pcl::computeCovarianceMatrix | ( | const pcl::PointCloud< PointT > & | cloud, |
| const std::vector< int > & | indices, | ||
| Eigen::Matrix< Scalar, 3, 3 > & | covariance_matrix | ||
| ) | [inline] |
Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
| [in] | cloud | the input point cloud |
| [in] | indices | subset of points given by their indices |
| [out] | covariance_matrix | the resultant 3x3 covariance matrix |
Definition at line 427 of file centroid.hpp.
| unsigned int pcl::computeCovarianceMatrix | ( | const pcl::PointCloud< PointT > & | cloud, |
| const pcl::PointIndices & | indices, | ||
| Eigen::Matrix< Scalar, 3, 3 > & | covariance_matrix | ||
| ) | [inline] |
Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
| [in] | cloud | the input point cloud |
| [in] | indices | subset of points given by their indices |
| [out] | covariance_matrix | the resultant 3x3 covariance matrix |
Definition at line 481 of file centroid.hpp.
| unsigned int pcl::computeCovarianceMatrixNormalized | ( | const pcl::PointCloud< PointT > & | cloud, |
| const Eigen::Matrix< Scalar, 4, 1 > & | centroid, | ||
| Eigen::Matrix< Scalar, 3, 3 > & | covariance_matrix | ||
| ) | [inline] |
Compute normalized the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of points in the point cloud. For small number of points, or if you want explicitely the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function.
| [in] | cloud | the input point cloud |
| [in] | centroid | the centroid of the set of points in the cloud |
| [out] | covariance_matrix | the resultant 3x3 covariance matrix |
Definition at line 252 of file centroid.hpp.
| unsigned int pcl::computeCovarianceMatrixNormalized | ( | const pcl::PointCloud< PointT > & | cloud, |
| const std::vector< int > & | indices, | ||
| const Eigen::Matrix< Scalar, 4, 1 > & | centroid, | ||
| Eigen::Matrix< Scalar, 3, 3 > & | covariance_matrix | ||
| ) | [inline] |
Compute the normalized 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function.
| [in] | cloud | the input point cloud |
| [in] | indices | the point cloud indices that need to be used |
| [in] | centroid | the centroid of the set of points in the cloud |
| [out] | covariance_matrix | the resultant 3x3 covariance matrix |
Definition at line 345 of file centroid.hpp.
| unsigned int pcl::computeCovarianceMatrixNormalized | ( | const pcl::PointCloud< PointT > & | cloud, |
| const pcl::PointIndices & | indices, | ||
| const Eigen::Matrix< Scalar, 4, 1 > & | centroid, | ||
| Eigen::Matrix< Scalar, 3, 3 > & | covariance_matrix | ||
| ) | [inline] |
Compute the normalized 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function.
| [in] | cloud | the input point cloud |
| [in] | indices | the point cloud indices that need to be used |
| [in] | centroid | the centroid of the set of points in the cloud |
| [out] | covariance_matrix | the resultant 3x3 covariance matrix |
Definition at line 359 of file centroid.hpp.
| unsigned int pcl::computeMeanAndCovarianceMatrix | ( | const pcl::PointCloud< PointT > & | cloud, |
| Eigen::Matrix< Scalar, 3, 3 > & | covariance_matrix, | ||
| Eigen::Matrix< Scalar, 4, 1 > & | centroid | ||
| ) | [inline] |
Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
| [in] | cloud | the input point cloud |
| [out] | covariance_matrix | the resultant 3x3 covariance matrix |
| [out] | centroid | the centroid of the set of points in the cloud |
Definition at line 490 of file centroid.hpp.
| unsigned int pcl::computeMeanAndCovarianceMatrix | ( | const pcl::PointCloud< PointT > & | cloud, |
| const std::vector< int > & | indices, | ||
| Eigen::Matrix< Scalar, 3, 3 > & | covariance_matrix, | ||
| Eigen::Matrix< Scalar, 4, 1 > & | centroid | ||
| ) | [inline] |
Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
| [in] | cloud | the input point cloud |
| [in] | indices | subset of points given by their indices |
| [out] | covariance_matrix | the resultant 3x3 covariance matrix |
| [out] | centroid | the centroid of the set of points in the cloud |
Definition at line 555 of file centroid.hpp.
| unsigned int pcl::computeMeanAndCovarianceMatrix | ( | const pcl::PointCloud< PointT > & | cloud, |
| const pcl::PointIndices & | indices, | ||
| Eigen::Matrix< Scalar, 3, 3 > & | covariance_matrix, | ||
| Eigen::Matrix< Scalar, 4, 1 > & | centroid | ||
| ) | [inline] |
Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
| [in] | cloud | the input point cloud |
| [in] | indices | subset of points given by their indices |
| [out] | centroid | the centroid of the set of points in the cloud |
| [out] | covariance_matrix | the resultant 3x3 covariance matrix |
Definition at line 622 of file centroid.hpp.
| void pcl::computeNDCentroid | ( | const pcl::PointCloud< PointT > & | cloud, |
| Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > & | centroid | ||
| ) | [inline] |
General, all purpose nD centroid estimation for a set of points using their indices.
| cloud | the input point cloud |
| centroid | the output centroid |
Definition at line 810 of file centroid.hpp.
| void pcl::computeNDCentroid | ( | const pcl::PointCloud< PointT > & | cloud, |
| const std::vector< int > & | indices, | ||
| Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > & | centroid | ||
| ) | [inline] |
General, all purpose nD centroid estimation for a set of points using their indices.
| cloud | the input point cloud |
| indices | the point cloud indices that need to be used |
| centroid | the output centroid |
Definition at line 832 of file centroid.hpp.
| void pcl::computeNDCentroid | ( | const pcl::PointCloud< PointT > & | cloud, |
| const pcl::PointIndices & | indices, | ||
| Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > & | centroid | ||
| ) | [inline] |
General, all purpose nD centroid estimation for a set of points using their indices.
| cloud | the input point cloud |
| indices | the point cloud indices that need to be used |
| centroid | the output centroid |
Definition at line 855 of file centroid.hpp.
| void pcl::concatenateFields | ( | const pcl::PointCloud< PointIn1T > & | cloud1_in, |
| const pcl::PointCloud< PointIn2T > & | cloud2_in, | ||
| pcl::PointCloud< PointOutT > & | cloud_out | ||
| ) |
Concatenate two datasets representing different fields.
| [in] | cloud1_in | the first input dataset |
| [in] | cloud2_in | the second input dataset (overwrites the fields of the first dataset for those that are shared) |
| [out] | cloud_out | the resultant output dataset created by the concatenation of all the fields in the input datasets |
Definition at line 636 of file common/include/pcl/common/impl/io.hpp.
| bool pcl::concatenateFields | ( | const pcl::PCLPointCloud2 & | cloud1_in, |
| const pcl::PCLPointCloud2 & | cloud2_in, | ||
| pcl::PCLPointCloud2 & | cloud_out | ||
| ) |
Concatenate two datasets representing different fields.
| [in] | cloud1_in | the first input dataset |
| [in] | cloud2_in | the second input dataset (overwrites the fields of the first dataset for those that are shared) |
| [out] | cloud_out | the output dataset created by concatenating all the fields in the input datasets |
Definition at line 70 of file common/src/io.cpp.
| bool pcl::concatenatePointCloud | ( | const pcl::PCLPointCloud2 & | cloud1, |
| const pcl::PCLPointCloud2 & | cloud2, | ||
| pcl::PCLPointCloud2 & | cloud_out | ||
| ) |
Concatenate two pcl::PCLPointCloud2.
| [in] | cloud1 | the first input point cloud dataset |
| [in] | cloud2 | the second input point cloud dataset |
| [out] | cloud_out | the resultant output point cloud dataset |
Definition at line 219 of file common/src/io.cpp.
| void pcl::copyPointCloud | ( | const pcl::PCLPointCloud2 & | cloud_in, |
| const std::vector< int > & | indices, | ||
| pcl::PCLPointCloud2 & | cloud_out | ||
| ) |
Extract the indices of a given point cloud as a new point cloud.
| [in] | cloud_in | the input point cloud dataset |
| [in] | indices | the vector of indices representing the points to be copied from cloud_in |
| [out] | cloud_out | the resultant output point cloud dataset |
Definition at line 417 of file common/src/io.cpp.
| void pcl::copyPointCloud | ( | const pcl::PCLPointCloud2 & | cloud_in, |
| const std::vector< int, Eigen::aligned_allocator< int > > & | indices, | ||
| pcl::PCLPointCloud2 & | cloud_out | ||
| ) |
Extract the indices of a given point cloud as a new point cloud.
| [in] | cloud_in | the input point cloud dataset |
| [in] | indices | the vector of indices representing the points to be copied from cloud_in |
| [out] | cloud_out | the resultant output point cloud dataset |
Definition at line 440 of file common/src/io.cpp.
| void pcl::copyPointCloud | ( | const pcl::PCLPointCloud2 & | cloud_in, |
| pcl::PCLPointCloud2 & | cloud_out | ||
| ) |
Copy fields and point cloud data from cloud_in to cloud_out.
| [in] | cloud_in | the input point cloud dataset |
| [out] | cloud_out | the resultant output point cloud dataset |
Definition at line 463 of file common/src/io.cpp.
| void pcl::copyPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const std::vector< int > & | indices, | ||
| pcl::PointCloud< PointT > & | cloud_out | ||
| ) |
Extract the indices of a given point cloud as a new point cloud.
| [in] | cloud_in | the input point cloud dataset |
| [in] | indices | the vector of indices representing the points to be copied from cloud_in |
| [out] | cloud_out | the resultant output point cloud dataset |
Definition at line 181 of file common/include/pcl/common/impl/io.hpp.
| void pcl::copyPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const std::vector< int, Eigen::aligned_allocator< int > > & | indices, | ||
| pcl::PointCloud< PointT > & | cloud_out | ||
| ) |
Extract the indices of a given point cloud as a new point cloud.
| [in] | cloud_in | the input point cloud dataset |
| [in] | indices | the vector of indices representing the points to be copied from cloud_in |
| [out] | cloud_out | the resultant output point cloud dataset |
Definition at line 208 of file common/include/pcl/common/impl/io.hpp.
| void pcl::copyPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const PointIndices & | indices, | ||
| pcl::PointCloud< PointT > & | cloud_out | ||
| ) |
Extract the indices of a given point cloud as a new point cloud.
| [in] | cloud_in | the input point cloud dataset |
| [in] | indices | the PointIndices structure representing the points to be copied from cloud_in |
| [out] | cloud_out | the resultant output point cloud dataset |
Definition at line 385 of file common/include/pcl/common/impl/io.hpp.
| void pcl::copyPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const std::vector< pcl::PointIndices > & | indices, | ||
| pcl::PointCloud< PointT > & | cloud_out | ||
| ) |
Extract the indices of a given point cloud as a new point cloud.
| [in] | cloud_in | the input point cloud dataset |
| [in] | indices | the vector of indices representing the points to be copied from cloud_in |
| [out] | cloud_out | the resultant output point cloud dataset |
Definition at line 487 of file common/include/pcl/common/impl/io.hpp.
| void pcl::copyPointCloud | ( | const pcl::PointCloud< PointInT > & | cloud_in, |
| pcl::PointCloud< PointOutT > & | cloud_out | ||
| ) |
Copy all the fields from a given point cloud into a new point cloud.
| [in] | cloud_in | the input point cloud dataset |
| [out] | cloud_out | the resultant output point cloud dataset |
Definition at line 110 of file common/include/pcl/common/impl/io.hpp.
| void pcl::copyPointCloud | ( | const pcl::PointCloud< PointInT > & | cloud_in, |
| const std::vector< int > & | indices, | ||
| pcl::PointCloud< PointOutT > & | cloud_out | ||
| ) |
Extract the indices of a given point cloud as a new point cloud.
| [in] | cloud_in | the input point cloud dataset |
| [in] | indices | the vector of indices representing the points to be copied from cloud_in |
| [out] | cloud_out | the resultant output point cloud dataset |
Definition at line 235 of file common/include/pcl/common/impl/io.hpp.
| void pcl::copyPointCloud | ( | const pcl::PointCloud< PointInT > & | cloud_in, |
| const std::vector< int, Eigen::aligned_allocator< int > > & | indices, | ||
| pcl::PointCloud< PointOutT > & | cloud_out | ||
| ) |
Extract the indices of a given point cloud as a new point cloud.
| [in] | cloud_in | the input point cloud dataset |
| [in] | indices | the vector of indices representing the points to be copied from cloud_in |
| [out] | cloud_out | the resultant output point cloud dataset |
Definition at line 310 of file common/include/pcl/common/impl/io.hpp.
| void pcl::copyPointCloud | ( | const pcl::PointCloud< PointInT > & | cloud_in, |
| const PointIndices & | indices, | ||
| pcl::PointCloud< PointOutT > & | cloud_out | ||
| ) |
Extract the indices of a given point cloud as a new point cloud.
| [in] | cloud_in | the input point cloud dataset |
| [in] | indices | the PointIndices structure representing the points to be copied from cloud_in |
| [out] | cloud_out | the resultant output point cloud dataset |
Definition at line 412 of file common/include/pcl/common/impl/io.hpp.
| void pcl::copyPointCloud | ( | const pcl::PointCloud< PointInT > & | cloud_in, |
| const std::vector< pcl::PointIndices > & | indices, | ||
| pcl::PointCloud< PointOutT > & | cloud_out | ||
| ) |
Extract the indices of a given point cloud as a new point cloud.
| [in] | cloud_in | the input point cloud dataset |
| [in] | indices | the vector of indices representing the points to be copied from cloud_in |
| [out] | cloud_out | the resultant output point cloud dataset |
Definition at line 527 of file common/include/pcl/common/impl/io.hpp.
| float pcl::CS_Norm | ( | FloatVectorT | A, |
| FloatVectorT | B, | ||
| int | dim | ||
| ) | [inline] |
| float pcl::deg2rad | ( | float | alpha | ) | [inline] |
Convert an angle from degrees to radians.
| alpha | the input angle (in degrees) |
Definition at line 67 of file angles.hpp.
| double pcl::deg2rad | ( | double | alpha | ) | [inline] |
Convert an angle from degrees to radians.
| alpha | the input angle (in degrees) |
Definition at line 79 of file angles.hpp.
| void pcl::demeanPointCloud | ( | ConstCloudIterator< PointT > & | cloud_iterator, |
| const Eigen::Matrix< Scalar, 4, 1 > & | centroid, | ||
| pcl::PointCloud< PointT > & | cloud_out, | ||
| int | npts = 0 |
||
| ) |
Subtract a centroid from a point cloud and return the de-meaned representation.
| [in] | cloud_iterator | an iterator over the input point cloud |
| [in] | centroid | the centroid of the point cloud |
| [out] | cloud_out | the resultant output point cloud |
| [in] | npts | the number of samples guaranteed to be left in the input cloud, accessible by the iterator. If not given, it will be calculated. |
Definition at line 632 of file centroid.hpp.
| void pcl::demeanPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const Eigen::Matrix< Scalar, 4, 1 > & | centroid, | ||
| pcl::PointCloud< PointT > & | cloud_out | ||
| ) |
Subtract a centroid from a point cloud and return the de-meaned representation.
| [in] | cloud_in | the input point cloud |
| [in] | centroid | the centroid of the point cloud |
| [out] | cloud_out | the resultant output point cloud |
Definition at line 665 of file centroid.hpp.
| void pcl::demeanPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const std::vector< int > & | indices, | ||
| const Eigen::Matrix< Scalar, 4, 1 > & | centroid, | ||
| pcl::PointCloud< PointT > & | cloud_out | ||
| ) |
Subtract a centroid from a point cloud and return the de-meaned representation.
| [in] | cloud_in | the input point cloud |
| [in] | indices | the set of point indices to use from the input point cloud |
| [out] | centroid | the centroid of the point cloud |
| cloud_out | the resultant output point cloud |
Definition at line 682 of file centroid.hpp.
| void pcl::demeanPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const pcl::PointIndices & | indices, | ||
| const Eigen::Matrix< Scalar, 4, 1 > & | centroid, | ||
| pcl::PointCloud< PointT > & | cloud_out | ||
| ) |
Subtract a centroid from a point cloud and return the de-meaned representation.
| [in] | cloud_in | the input point cloud |
| [in] | indices | the set of point indices to use from the input point cloud |
| [out] | centroid | the centroid of the point cloud |
| cloud_out | the resultant output point cloud |
Definition at line 712 of file centroid.hpp.
| void pcl::demeanPointCloud | ( | ConstCloudIterator< PointT > & | cloud_iterator, |
| const Eigen::Matrix< Scalar, 4, 1 > & | centroid, | ||
| Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > & | cloud_out, | ||
| int | npts = 0 |
||
| ) |
Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.
| [in] | cloud_iterator | an iterator over the input point cloud |
| [in] | centroid | the centroid of the point cloud |
| [out] | cloud_out | the resultant output XYZ0 dimensions of cloud_in as an Eigen matrix (4 rows, N pts columns) |
| [in] | npts | the number of samples guaranteed to be left in the input cloud, accessible by the iterator. If not given, it will be calculated. |
Definition at line 722 of file centroid.hpp.
| void pcl::demeanPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const Eigen::Matrix< Scalar, 4, 1 > & | centroid, | ||
| Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > & | cloud_out | ||
| ) |
Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.
| [in] | cloud_in | the input point cloud |
| [in] | centroid | the centroid of the point cloud |
| [out] | cloud_out | the resultant output XYZ0 dimensions of cloud_in as an Eigen matrix (4 rows, N pts columns) |
Definition at line 753 of file centroid.hpp.
| void pcl::demeanPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const std::vector< int > & | indices, | ||
| const Eigen::Matrix< Scalar, 4, 1 > & | centroid, | ||
| Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > & | cloud_out | ||
| ) |
Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.
| [in] | cloud_in | the input point cloud |
| [in] | indices | the set of point indices to use from the input point cloud |
| [in] | centroid | the centroid of the point cloud |
| [out] | cloud_out | the resultant output XYZ0 dimensions of cloud_in as an Eigen matrix (4 rows, N pts columns) |
Definition at line 776 of file centroid.hpp.
| void pcl::demeanPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const pcl::PointIndices & | indices, | ||
| const Eigen::Matrix< Scalar, 4, 1 > & | centroid, | ||
| Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > & | cloud_out | ||
| ) |
Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.
| [in] | cloud_in | the input point cloud |
| [in] | indices | the set of point indices to use from the input point cloud |
| [in] | centroid | the centroid of the point cloud |
| [out] | cloud_out | the resultant output XYZ0 dimensions of cloud_in as an Eigen matrix (4 rows, N pts columns) |
Definition at line 800 of file centroid.hpp.
| float pcl::Div_Norm | ( | FloatVectorT | A, |
| FloatVectorT | B, | ||
| int | dim | ||
| ) | [inline] |
| void pcl::eigen22 | ( | const Matrix & | mat, |
| typename Matrix::Scalar & | eigenvalue, | ||
| Vector & | eigenvector | ||
| ) | [inline] |
determine the smallest eigenvalue and its corresponding eigenvector
| [in] | mat | input matrix that needs to be symmetric and positive semi definite |
| [out] | eigenvalue | the smallest eigenvalue of the input matrix |
| [out] | eigenvector | the corresponding eigenvector to the smallest eigenvalue of the input matrix |
Definition at line 163 of file common/include/pcl/common/eigen.h.
| void pcl::eigen22 | ( | const Matrix & | mat, |
| Matrix & | eigenvectors, | ||
| Vector & | eigenvalues | ||
| ) | [inline] |
determine the smallest eigenvalue and its corresponding eigenvector
| [in] | mat | input matrix that needs to be symmetric and positive semi definite |
| [out] | eigenvectors | the corresponding eigenvector to the smallest eigenvalue of the input matrix |
| [out] | eigenvalues | the smallest eigenvalue of the input matrix |
Definition at line 207 of file common/include/pcl/common/eigen.h.
| void pcl::eigen33 | ( | const Matrix & | mat, |
| typename Matrix::Scalar & | eigenvalue, | ||
| Vector & | eigenvector | ||
| ) | [inline] |
determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi definite input matrix
| [in] | mat | symmetric positive semi definite input matrix |
| [out] | eigenvalue | smallest eigenvalue of the input matrix |
| [out] | eigenvector | the corresponding eigenvector for the input eigenvalue |
Definition at line 304 of file common/include/pcl/common/eigen.h.
| void pcl::eigen33 | ( | const Matrix & | mat, |
| Vector & | evals | ||
| ) | [inline] |
determines the eigenvalues of the symmetric positive semi definite input matrix
| [in] | mat | symmetric positive semi definite input matrix |
| [out] | evals | resulting eigenvalues in ascending order |
Definition at line 345 of file common/include/pcl/common/eigen.h.
| void pcl::eigen33 | ( | const Matrix & | mat, |
| Matrix & | evecs, | ||
| Vector & | evals | ||
| ) | [inline] |
determines the eigenvalues and corresponding eigenvectors of the symmetric positive semi definite input matrix
| [in] | mat | symmetric positive semi definite input matrix |
| [out] | evecs | resulting eigenvalues in ascending order |
| [out] | evals | corresponding eigenvectors in correct order according to eigenvalues |
Definition at line 364 of file common/include/pcl/common/eigen.h.
| double pcl::getAngle3D | ( | const Eigen::Vector4f & | v1, |
| const Eigen::Vector4f & | v2 | ||
| ) | [inline] |
Compute the smallest angle between two vectors in the [ 0, PI ) interval in 3D.
| v1 | the first 3D vector (represented as a Eigen::Vector4f) |
| v2 | the second 3D vector (represented as a Eigen::Vector4f) |
Definition at line 46 of file common/include/pcl/common/impl/common.hpp.
| double pcl::getCircumcircleRadius | ( | const PointT & | pa, |
| const PointT & | pb, | ||
| const PointT & | pc | ||
| ) | [inline] |
Compute the radius of a circumscribed circle for a triangle formed of three points pa, pb, and pc.
| pa | the first point |
| pb | the second point |
| pc | the third point |
Definition at line 360 of file common/include/pcl/common/impl/common.hpp.
| bool pcl::getEigenAsPointCloud | ( | Eigen::MatrixXf & | in, |
| pcl::PCLPointCloud2 & | out | ||
| ) |
Copy the XYZ dimensions from an Eigen MatrixXf into a pcl::PCLPointCloud2 message.
| [in] | in | the Eigen MatrixXf format containing XYZ0 / point |
| [out] | out | the resultant point cloud message |
Definition at line 370 of file common/src/io.cpp.
| void pcl::getEulerAngles | ( | const Eigen::Affine3f & | t, |
| float & | roll, | ||
| float & | pitch, | ||
| float & | yaw | ||
| ) | [inline] |
| int pcl::getFieldIndex | ( | const pcl::PCLPointCloud2 & | cloud, |
| const std::string & | field_name | ||
| ) | [inline] |
Get the index of a specified field (i.e., dimension/channel)
| [in] | cloud | the the point cloud message |
| [in] | field_name | the string defining the field name |
Definition at line 58 of file common/include/pcl/common/io.h.
| int pcl::getFieldIndex | ( | const pcl::PointCloud< PointT > & | cloud, |
| const std::string & | field_name, | ||
| std::vector< pcl::PCLPointField > & | fields | ||
| ) | [inline] |
Get the index of a specified field (i.e., dimension/channel)
| [in] | cloud | the the point cloud message |
| [in] | field_name | the string defining the field name |
| [out] | fields | a vector to the original PCLPointField vector that the raw PointCloud message contains |
Definition at line 49 of file common/include/pcl/common/impl/io.hpp.
| int pcl::getFieldIndex | ( | const std::string & | field_name, |
| std::vector< pcl::PCLPointField > & | fields | ||
| ) | [inline] |
Get the index of a specified field (i.e., dimension/channel)
| [in] | field_name | the string defining the field name |
| [out] | fields | a vector to the original PCLPointField vector that the raw PointCloud message contains |
Definition at line 64 of file common/include/pcl/common/impl/io.hpp.
| void pcl::getFields | ( | const pcl::PointCloud< PointT > & | cloud, |
| std::vector< pcl::PCLPointField > & | fields | ||
| ) | [inline] |
Get the list of available fields (i.e., dimension/channel)
| [in] | cloud | the point cloud message |
| [out] | fields | a vector to the original PCLPointField vector that the raw PointCloud message contains |
Definition at line 78 of file common/include/pcl/common/impl/io.hpp.
| void pcl::getFields | ( | std::vector< pcl::PCLPointField > & | fields | ) | [inline] |
Get the list of available fields (i.e., dimension/channel)
| [out] | fields | a vector to the original PCLPointField vector that the raw PointCloud message contains |
Definition at line 87 of file common/include/pcl/common/impl/io.hpp.
| int pcl::getFieldSize | ( | const int | datatype | ) | [inline] |
Obtains the size of a specific field data type in bytes.
| [in] | datatype | the field data type (see PCLPointField.h) |
Definition at line 127 of file common/include/pcl/common/io.h.
| std::string pcl::getFieldsList | ( | const pcl::PointCloud< PointT > & | cloud | ) | [inline] |
Get the list of all fields available in a given cloud.
| [in] | cloud | the the point cloud message |
Definition at line 96 of file common/include/pcl/common/impl/io.hpp.
| std::string pcl::getFieldsList | ( | const pcl::PCLPointCloud2 & | cloud | ) | [inline] |
Get the available point cloud fields as a space separated string.
| [in] | cloud | a pointer to the PointCloud message |
Definition at line 113 of file common/include/pcl/common/io.h.
| int pcl::getFieldType | ( | const int | size, |
| char | type | ||
| ) | [inline] |
Obtains the type of the PCLPointField from a specific size and type.
| [in] | size | the size in bytes of the data field |
| [in] | type | a char describing the type of the field ('F' = float, 'I' = signed, 'U' = unsigned) |
Definition at line 166 of file common/include/pcl/common/io.h.
| char pcl::getFieldType | ( | const int | type | ) | [inline] |
Obtains the type of the PCLPointField from a specific PCLPointField as a char.
| [in] | type | the PCLPointField field type |
Definition at line 204 of file common/include/pcl/common/io.h.
| void pcl::getMaxDistance | ( | const pcl::PointCloud< PointT > & | cloud, |
| const Eigen::Vector4f & | pivot_pt, | ||
| Eigen::Vector4f & | max_pt | ||
| ) | [inline] |
Get the point at maximum distance from a given point and a given pointcloud.
| cloud | the point cloud data message |
| pivot_pt | the point from where to compute the distance |
| max_pt | the point in cloud that is the farthest point away from pivot_pt |
Definition at line 116 of file common/include/pcl/common/impl/common.hpp.
| void pcl::getMaxDistance | ( | const pcl::PointCloud< PointT > & | cloud, |
| const std::vector< int > & | indices, | ||
| const Eigen::Vector4f & | pivot_pt, | ||
| Eigen::Vector4f & | max_pt | ||
| ) | [inline] |
Get the point at maximum distance from a given point and a given pointcloud.
| cloud | the point cloud data message |
| pivot_pt | the point from where to compute the distance |
| indices | the vector of point indices to use from cloud |
| max_pt | the point in cloud that is the farthest point away from pivot_pt |
Definition at line 162 of file common/include/pcl/common/impl/common.hpp.
| double pcl::getMaxSegment | ( | const pcl::PointCloud< PointT > & | cloud, |
| PointT & | pmin, | ||
| PointT & | pmax | ||
| ) | [inline] |
Obtain the maximum segment in a given set of points, and return the minimum and maximum points.
| [in] | cloud | the point cloud dataset |
| [out] | pmin | the coordinates of the "minimum" point in cloud (one end of the segment) |
| [out] | pmax | the coordinates of the "maximum" point in cloud (the other end of the segment) |
Definition at line 100 of file common/include/pcl/common/distances.h.
| double pcl::getMaxSegment | ( | const pcl::PointCloud< PointT > & | cloud, |
| const std::vector< int > & | indices, | ||
| PointT & | pmin, | ||
| PointT & | pmax | ||
| ) | [inline] |
Obtain the maximum segment in a given set of points, and return the minimum and maximum points.
| [in] | cloud | the point cloud dataset |
| [in] | indices | a set of point indices to use from cloud |
| [out] | pmin | the coordinates of the "minimum" point in cloud (one end of the segment) |
| [out] | pmax | the coordinates of the "maximum" point in cloud (the other end of the segment) |
Definition at line 139 of file common/include/pcl/common/distances.h.
| void pcl::getMeanStd | ( | const std::vector< float > & | values, |
| double & | mean, | ||
| double & | stddev | ||
| ) | [inline] |
Compute both the mean and the standard deviation of an array of values.
| values | the array of values |
| mean | the resultant mean of the distribution |
| stddev | the resultant standard deviation of the distribution |
Definition at line 57 of file common/include/pcl/common/impl/common.hpp.
| void pcl::getMeanStdDev | ( | const std::vector< float > & | values, |
| double & | mean, | ||
| double & | stddev | ||
| ) |
Compute both the mean and the standard deviation of an array of values.
| values | the array of values |
| mean | the resultant mean of the distribution |
| stddev | the resultant standard deviation of the distribution |
Definition at line 73 of file common/src/common.cpp.
| void pcl::getMinMax | ( | const PointT & | histogram, |
| int | len, | ||
| float & | min_p, | ||
| float & | max_p | ||
| ) | [inline] |
Get the minimum and maximum values on a point histogram.
| histogram | the point representing a multi-dimensional histogram |
| len | the length of the histogram |
| min_p | the resultant minimum |
| max_p | the resultant maximum |
Definition at line 377 of file common/include/pcl/common/impl/common.hpp.
| void pcl::getMinMax | ( | const pcl::PCLPointCloud2 & | cloud, |
| int | idx, | ||
| const std::string & | field_name, | ||
| float & | min_p, | ||
| float & | max_p | ||
| ) |
Get the minimum and maximum values on a point histogram.
| cloud | the cloud containing multi-dimensional histograms |
| idx | point index representing the histogram that we need to compute min/max for |
| field_name | the field name containing the multi-dimensional histogram |
| min_p | the resultant minimum |
| max_p | the resultant maximum |
Definition at line 44 of file common/src/common.cpp.
| void pcl::getMinMax3D | ( | const pcl::PointCloud< PointT > & | cloud, |
| PointT & | min_pt, | ||
| PointT & | max_pt | ||
| ) | [inline] |
Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.
| cloud | the point cloud data message |
| min_pt | the resultant minimum bounds |
| max_pt | the resultant maximum bounds |
Definition at line 212 of file common/include/pcl/common/impl/common.hpp.
| void pcl::getMinMax3D | ( | const pcl::PointCloud< PointT > & | cloud, |
| Eigen::Vector4f & | min_pt, | ||
| Eigen::Vector4f & | max_pt | ||
| ) | [inline] |
Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.
| cloud | the point cloud data message |
| min_pt | the resultant minimum bounds |
| max_pt | the resultant maximum bounds |
Definition at line 249 of file common/include/pcl/common/impl/common.hpp.
| void pcl::getMinMax3D | ( | const pcl::PointCloud< PointT > & | cloud, |
| const std::vector< int > & | indices, | ||
| Eigen::Vector4f & | min_pt, | ||
| Eigen::Vector4f & | max_pt | ||
| ) | [inline] |
Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.
| cloud | the point cloud data message |
| indices | the vector of point indices to use from cloud |
| min_pt | the resultant minimum bounds |
| max_pt | the resultant maximum bounds |
Definition at line 325 of file common/include/pcl/common/impl/common.hpp.
| void pcl::getMinMax3D | ( | const pcl::PointCloud< PointT > & | cloud, |
| const pcl::PointIndices & | indices, | ||
| Eigen::Vector4f & | min_pt, | ||
| Eigen::Vector4f & | max_pt | ||
| ) | [inline] |
Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.
| cloud | the point cloud data message |
| indices | the vector of point indices to use from cloud |
| min_pt | the resultant minimum bounds |
| max_pt | the resultant maximum bounds |
Definition at line 287 of file common/include/pcl/common/impl/common.hpp.
| bool pcl::getPointCloudAsEigen | ( | const pcl::PCLPointCloud2 & | in, |
| Eigen::MatrixXf & | out | ||
| ) |
Copy the XYZ dimensions of a pcl::PCLPointCloud2 into Eigen format.
| [in] | in | the point cloud message |
| [out] | out | the resultant Eigen MatrixXf format containing XYZ0 / point |
Definition at line 328 of file common/src/io.cpp.
| void pcl::getPointsInBox | ( | const pcl::PointCloud< PointT > & | cloud, |
| Eigen::Vector4f & | min_pt, | ||
| Eigen::Vector4f & | max_pt, | ||
| std::vector< int > & | indices | ||
| ) | [inline] |
Get a set of points residing in a box given its bounds.
| cloud | the point cloud data message |
| min_pt | the minimum bounds |
| max_pt | the maximum bounds |
| indices | the resultant set of point indices residing in the box |
Definition at line 73 of file common/include/pcl/common/impl/common.hpp.
| void pcl::getTransformation | ( | Scalar | x, |
| Scalar | y, | ||
| Scalar | z, | ||
| Scalar | roll, | ||
| Scalar | pitch, | ||
| Scalar | yaw, | ||
| Eigen::Transform< Scalar, 3, Eigen::Affine > & | t | ||
| ) | [inline] |
Create a transformation from the given translation and Euler angles (XYZ-convention)
| [in] | x | the input x translation |
| [in] | y | the input y translation |
| [in] | z | the input z translation |
| [in] | roll | the input roll angle |
| [in] | pitch | the input pitch angle |
| [in] | yaw | the input yaw angle |
| [out] | t | the resulting transformation matrix |
| Eigen::Affine3f pcl::getTransformation | ( | float | x, |
| float | y, | ||
| float | z, | ||
| float | roll, | ||
| float | pitch, | ||
| float | yaw | ||
| ) | [inline] |
Create a transformation from the given translation and Euler angles (XYZ-convention)
| [in] | x | the input x translation |
| [in] | y | the input y translation |
| [in] | z | the input z translation |
| [in] | roll | the input roll angle |
| [in] | pitch | the input pitch angle |
| [in] | yaw | the input yaw angle |
| void pcl::getTransformationFromTwoUnitVectors | ( | const Eigen::Vector3f & | y_direction, |
| const Eigen::Vector3f & | z_axis, | ||
| Eigen::Affine3f & | transformation | ||
| ) | [inline] |
Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)
| [in] | y_direction | the y direction |
| [in] | z_axis | the z-axis |
| [out] | transformation | the resultant 3D rotation |
| Eigen::Affine3f pcl::getTransformationFromTwoUnitVectors | ( | const Eigen::Vector3f & | y_direction, |
| const Eigen::Vector3f & | z_axis | ||
| ) | [inline] |
Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)
| [in] | y_direction | the y direction |
| [in] | z_axis | the z-axis |
| void pcl::getTransformationFromTwoUnitVectorsAndOrigin | ( | const Eigen::Vector3f & | y_direction, |
| const Eigen::Vector3f & | z_axis, | ||
| const Eigen::Vector3f & | origin, | ||
| Eigen::Affine3f & | transformation | ||
| ) | [inline] |
Get the transformation that will translate orign to (0,0,0) and rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)
| [in] | y_direction | the y direction |
| [in] | z_axis | the z-axis |
| [in] | origin | the origin |
| [in] | transformation | the resultant transformation matrix |
| void pcl::getTransFromUnitVectorsXY | ( | const Eigen::Vector3f & | x_axis, |
| const Eigen::Vector3f & | y_direction, | ||
| Eigen::Affine3f & | transformation | ||
| ) | [inline] |
Get the unique 3D rotation that will rotate x_axis into (1,0,0) and y_direction into a vector with z=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)
| [in] | x_axis | the x-axis |
| [in] | y_direction | the y direction |
| [out] | transformation | the resultant 3D rotation |
| Eigen::Affine3f pcl::getTransFromUnitVectorsXY | ( | const Eigen::Vector3f & | x_axis, |
| const Eigen::Vector3f & | y_direction | ||
| ) | [inline] |
Get the unique 3D rotation that will rotate x_axis into (1,0,0) and y_direction into a vector with z=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)
| [in] | x_axis | the x-axis |
| [in] | y_direction | the y direction |
| void pcl::getTransFromUnitVectorsZY | ( | const Eigen::Vector3f & | z_axis, |
| const Eigen::Vector3f & | y_direction, | ||
| Eigen::Affine3f & | transformation | ||
| ) | [inline] |
Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)
| [in] | z_axis | the z-axis |
| [in] | y_direction | the y direction |
| [out] | transformation | the resultant 3D rotation |
| Eigen::Affine3f pcl::getTransFromUnitVectorsZY | ( | const Eigen::Vector3f & | z_axis, |
| const Eigen::Vector3f & | y_direction | ||
| ) | [inline] |
Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)
| [in] | z_axis | the z-axis |
| [in] | y_direction | the y direction |
| void pcl::getTranslationAndEulerAngles | ( | const Eigen::Affine3f & | t, |
| float & | x, | ||
| float & | y, | ||
| float & | z, | ||
| float & | roll, | ||
| float & | pitch, | ||
| float & | yaw | ||
| ) | [inline] |
Extract x,y,z and the Euler angles (XYZ-convention) from the given transformation
| [in] | t | the input transformation matrix |
| [out] | x | the resulting x translation |
| [out] | y | the resulting y translation |
| [out] | z | the resulting z translation |
| [out] | roll | the resulting roll angle |
| [out] | pitch | the resulting pitch angle |
| [out] | yaw | the resulting yaw angle |
| float pcl::HIK_Norm | ( | FloatVectorT | A, |
| FloatVectorT | B, | ||
| int | dim | ||
| ) | [inline] |
| Matrix::Scalar pcl::invert2x2 | ( | const Matrix & | matrix, |
| Matrix & | inverse | ||
| ) | [inline] |
Calculate the inverse of a 2x2 matrix.
| [in] | matrix | matrix to be inverted |
| [out] | inverse | the resultant inverted matrix |
Definition at line 553 of file common/include/pcl/common/eigen.h.
| Matrix::Scalar pcl::invert3x3Matrix | ( | const Matrix & | matrix, |
| Matrix & | inverse | ||
| ) | [inline] |
Calculate the inverse of a general 3x3 matrix.
| [in] | matrix | matrix to be inverted |
| [out] | inverse | the resultant inverted matrix |
Definition at line 618 of file common/include/pcl/common/eigen.h.
| Matrix::Scalar pcl::invert3x3SymMatrix | ( | const Matrix & | matrix, |
| Matrix & | inverse | ||
| ) | [inline] |
Calculate the inverse of a 3x3 symmetric matrix.
| [in] | matrix | matrix to be inverted |
| [out] | inverse | the resultant inverted matrix |
Definition at line 578 of file common/include/pcl/common/eigen.h.
| bool pcl::isBetterCorrespondence | ( | const Correspondence & | pc1, |
| const Correspondence & | pc2 | ||
| ) | [inline] |
Comparator to enable us to sort a vector of PointCorrespondences according to their scores using std::sort (begin(), end(), isBetterCorrespondence);.
Definition at line 157 of file correspondence.h.
| float pcl::JM_Norm | ( | FloatVectorT | A, |
| FloatVectorT | B, | ||
| int | dim | ||
| ) | [inline] |
| float pcl::K_Norm | ( | FloatVectorT | A, |
| FloatVectorT | B, | ||
| int | dim, | ||
| float | P1, | ||
| float | P2 | ||
| ) | [inline] |
Compute the K norm of the vector between two points.
| A | the first point |
| B | the second point |
| dim | the number of dimensions in A and B (dimensions must match) |
| P1 | the first parameter |
| P2 | the second parameter |
| float pcl::KL_Norm | ( | FloatVectorT | A, |
| FloatVectorT | B, | ||
| int | dim | ||
| ) | [inline] |
Compute the KL between two discrete probability density functions.
| A | the first discrete PDF |
| B | the second discrete PDF |
| dim | the number of dimensions in A and B (dimensions must match) |
| float pcl::L1_Norm | ( | FloatVectorT | A, |
| FloatVectorT | B, | ||
| int | dim | ||
| ) | [inline] |
| float pcl::L2_Norm | ( | FloatVectorT | A, |
| FloatVectorT | B, | ||
| int | dim | ||
| ) | [inline] |
| float pcl::L2_Norm_SQR | ( | FloatVectorT | A, |
| FloatVectorT | B, | ||
| int | dim | ||
| ) | [inline] |
| void pcl::lineToLineSegment | ( | const Eigen::VectorXf & | line_a, |
| const Eigen::VectorXf & | line_b, | ||
| Eigen::Vector4f & | pt1_seg, | ||
| Eigen::Vector4f & | pt2_seg | ||
| ) |
Get the shortest 3D segment between two 3D lines.
| line_a | the coefficients of the first line (point, direction) |
| line_b | the coefficients of the second line (point, direction) |
| pt1_seg | the first point on the line segment |
| pt2_seg | the second point on the line segment |
Definition at line 40 of file distances.cpp.
| bool pcl::lineWithLineIntersection | ( | const Eigen::VectorXf & | line_a, |
| const Eigen::VectorXf & | line_b, | ||
| Eigen::Vector4f & | point, | ||
| double | sqr_eps = 1e-4 |
||
| ) |
Get the intersection of a two 3D lines in space as a 3D point.
| [in] | line_a | the coefficients of the first line (point, direction) |
| [in] | line_b | the coefficients of the second line (point, direction) |
| [out] | point | holder for the computed 3D point |
| [in] | sqr_eps | maximum allowable squared distance to the true solution |
Definition at line 42 of file intersections.cpp.
| bool pcl::lineWithLineIntersection | ( | const pcl::ModelCoefficients & | line_a, |
| const pcl::ModelCoefficients & | line_b, | ||
| Eigen::Vector4f & | point, | ||
| double | sqr_eps = 1e-4 |
||
| ) |
Get the intersection of a two 3D lines in space as a 3D point.
| [in] | line_a | the coefficients of the first line (point, direction) |
| [in] | line_b | the coefficients of the second line (point, direction) |
| [out] | point | holder for the computed 3D point |
| [in] | sqr_eps | maximum allowable squared distance to the true solution |
Definition at line 61 of file intersections.cpp.
| float pcl::Linf_Norm | ( | FloatVectorT | A, |
| FloatVectorT | B, | ||
| int | dim | ||
| ) | [inline] |
| void pcl::loadBinary | ( | Eigen::MatrixBase< Derived > const & | matrix, |
| std::istream & | file | ||
| ) |
| float pcl::normAngle | ( | float | alpha | ) | [inline] |
Normalize an angle to (-PI, PI].
| alpha | the input angle (in radians) |
Definition at line 48 of file angles.hpp.
| float pcl::PF_Norm | ( | FloatVectorT | A, |
| FloatVectorT | B, | ||
| int | dim, | ||
| float | P1, | ||
| float | P2 | ||
| ) | [inline] |
Compute the PF norm of the vector between two points.
| A | the first point |
| B | the second point |
| dim | the number of dimensions in A and B (dimensions must match) |
| P1 | the first parameter |
| P2 | the second parameter |
| float pcl::rad2deg | ( | float | alpha | ) | [inline] |
Convert an angle from radians to degrees.
| alpha | the input angle (in radians) |
Definition at line 61 of file angles.hpp.
| double pcl::rad2deg | ( | double | alpha | ) | [inline] |
Convert an angle from radians to degrees.
| alpha | the input angle (in radians) |
Definition at line 73 of file angles.hpp.
| void pcl::saveBinary | ( | const Eigen::MatrixBase< Derived > & | matrix, |
| std::ostream & | file | ||
| ) |
| float pcl::selectNorm | ( | FloatVectorT | A, |
| FloatVectorT | B, | ||
| int | dim, | ||
| NormType | norm_type | ||
| ) | [inline] |
| double pcl::sqrPointToLineDistance | ( | const Eigen::Vector4f & | pt, |
| const Eigen::Vector4f & | line_pt, | ||
| const Eigen::Vector4f & | line_dir | ||
| ) | [inline] |
Get the square distance from a point to a line (represented by a point and a direction)
| pt | a point |
| line_pt | a point on the line (make sure that line_pt[3] = 0 as there are no internal checks!) |
| line_dir | the line direction |
Definition at line 69 of file common/include/pcl/common/distances.h.
| double pcl::sqrPointToLineDistance | ( | const Eigen::Vector4f & | pt, |
| const Eigen::Vector4f & | line_pt, | ||
| const Eigen::Vector4f & | line_dir, | ||
| const double | sqr_length | ||
| ) | [inline] |
Get the square distance from a point to a line (represented by a point and a direction)
| pt | a point |
| line_pt | a point on the line (make sure that line_pt[3] = 0 as there are no internal checks!) |
| line_dir | the line direction |
| sqr_length | the squared norm of the line direction |
Definition at line 85 of file common/include/pcl/common/distances.h.
| float pcl::Sublinear_Norm | ( | FloatVectorT | A, |
| FloatVectorT | B, | ||
| int | dim | ||
| ) | [inline] |
| void pcl::io::swapByte | ( | char * | bytes | ) |
swap bytes order of a char array of length N
| bytes | char array to swap |
| PointT pcl::transformPoint | ( | const PointT & | point, |
| const Eigen::Transform< Scalar, 3, Eigen::Affine > & | transform | ||
| ) | [inline] |
Transform a point with members x,y,z.
| [in] | point | the point to transform |
| [out] | transform | the transformation to apply |
Definition at line 295 of file transforms.hpp.
| void pcl::transformPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| pcl::PointCloud< PointT > & | cloud_out, | ||
| const Eigen::Transform< Scalar, 3, Eigen::Affine > & | transform | ||
| ) |
Apply an affine transform defined by an Eigen Transform.
| [in] | cloud_in | the input point cloud |
| [out] | cloud_out | the resultant output point cloud |
| [in] | transform | an affine transformation (typically a rigid transformation) |
Definition at line 42 of file transforms.hpp.
| void pcl::transformPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const std::vector< int > & | indices, | ||
| pcl::PointCloud< PointT > & | cloud_out, | ||
| const Eigen::Transform< Scalar, 3, Eigen::Affine > & | transform | ||
| ) |
Apply an affine transform defined by an Eigen Transform.
| [in] | cloud_in | the input point cloud |
| [in] | indices | the set of point indices to use from the input point cloud |
| [out] | cloud_out | the resultant output point cloud |
| [in] | transform | an affine transformation (typically a rigid transformation) |
Definition at line 92 of file transforms.hpp.
| void pcl::transformPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const pcl::PointIndices & | indices, | ||
| pcl::PointCloud< PointT > & | cloud_out, | ||
| const Eigen::Transform< Scalar, 3, Eigen::Affine > & | transform | ||
| ) |
Apply an affine transform defined by an Eigen Transform.
| [in] | cloud_in | the input point cloud |
| [in] | indices | the set of point indices to use from the input point cloud |
| [out] | cloud_out | the resultant output point cloud |
| [in] | transform | an affine transformation (typically a rigid transformation) |
Definition at line 100 of file common/include/pcl/common/transforms.h.
| void pcl::transformPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| pcl::PointCloud< PointT > & | cloud_out, | ||
| const Eigen::Matrix< Scalar, 4, 4 > & | transform | ||
| ) |
Apply a rigid transform defined by a 4x4 matrix.
| [in] | cloud_in | the input point cloud |
| [out] | cloud_out | the resultant output point cloud |
| [in] | transform | a rigid transformation |
Definition at line 190 of file common/include/pcl/common/transforms.h.
| void pcl::transformPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const std::vector< int > & | indices, | ||
| pcl::PointCloud< PointT > & | cloud_out, | ||
| const Eigen::Matrix< Scalar, 4, 4 > & | transform | ||
| ) |
Apply a rigid transform defined by a 4x4 matrix.
| [in] | cloud_in | the input point cloud |
| [in] | indices | the set of point indices to use from the input point cloud |
| [out] | cloud_out | the resultant output point cloud |
| [in] | transform | a rigid transformation |
Definition at line 214 of file common/include/pcl/common/transforms.h.
| void pcl::transformPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const pcl::PointIndices & | indices, | ||
| pcl::PointCloud< PointT > & | cloud_out, | ||
| const Eigen::Matrix< Scalar, 4, 4 > & | transform | ||
| ) |
Apply a rigid transform defined by a 4x4 matrix.
| [in] | cloud_in | the input point cloud |
| [in] | indices | the set of point indices to use from the input point cloud |
| [out] | cloud_out | the resultant output point cloud |
| [in] | transform | a rigid transformation |
Definition at line 240 of file common/include/pcl/common/transforms.h.
| void pcl::transformPointCloud | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| pcl::PointCloud< PointT > & | cloud_out, | ||
| const Eigen::Matrix< Scalar, 3, 1 > & | offset, | ||
| const Eigen::Quaternion< Scalar > & | rotation | ||
| ) | [inline] |
Apply a rigid transform defined by a 3D offset and a quaternion.
| [in] | cloud_in | the input point cloud |
| [out] | cloud_out | the resultant output point cloud |
| [in] | offset | the translation component of the rigid transformation |
| [in] | rotation | the rotation component of the rigid transformation |
Definition at line 269 of file transforms.hpp.
| void pcl::transformPointCloudWithNormals | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| pcl::PointCloud< PointT > & | cloud_out, | ||
| const Eigen::Matrix< Scalar, 4, 4 > & | transform | ||
| ) |
Transform a point cloud and rotate its normals using an Eigen transform.
| [in] | cloud_in | the input point cloud |
| [out] | cloud_out | the resultant output point cloud |
| [in] | transform | an affine transformation (typically a rigid transformation) |
Definition at line 265 of file common/include/pcl/common/transforms.h.
| void pcl::transformPointCloudWithNormals | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const std::vector< int > & | indices, | ||
| pcl::PointCloud< PointT > & | cloud_out, | ||
| const Eigen::Matrix< Scalar, 4, 4 > & | transform | ||
| ) |
Transform a point cloud and rotate its normals using an Eigen transform.
| [in] | cloud_in | the input point cloud |
| [in] | indices | the set of point indices to use from the input point cloud |
| [out] | cloud_out | the resultant output point cloud |
| [in] | transform | an affine transformation (typically a rigid transformation) |
Definition at line 291 of file common/include/pcl/common/transforms.h.
| void pcl::transformPointCloudWithNormals | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| const pcl::PointIndices & | indices, | ||
| pcl::PointCloud< PointT > & | cloud_out, | ||
| const Eigen::Matrix< Scalar, 4, 4 > & | transform | ||
| ) |
Transform a point cloud and rotate its normals using an Eigen transform.
| [in] | cloud_in | the input point cloud |
| [in] | indices | the set of point indices to use from the input point cloud |
| [out] | cloud_out | the resultant output point cloud |
| [in] | transform | an affine transformation (typically a rigid transformation) |
Definition at line 319 of file common/include/pcl/common/transforms.h.
| void pcl::transformPointCloudWithNormals | ( | const pcl::PointCloud< PointT > & | cloud_in, |
| pcl::PointCloud< PointT > & | cloud_out, | ||
| const Eigen::Matrix< Scalar, 3, 1 > & | offset, | ||
| const Eigen::Quaternion< Scalar > & | rotation | ||
| ) | [inline] |
Transform a point cloud and rotate its normals using an Eigen transform.
| [in] | cloud_in | the input point cloud |
| [out] | cloud_out | the resultant output point cloud |
| [in] | offset | the translation component of the rigid transformation |
| [in] | rotation | the rotation component of the rigid transformation |
Definition at line 282 of file transforms.hpp.