nearest.cpp File Reference

#include <door_handle_detector/geometry/angles.h>
#include <door_handle_detector/geometry/nearest.h>
#include <door_handle_detector/geometry/point.h>
#include <door_handle_detector/geometry/statistics.h>
#include <door_handle_detector/kdtree/kdtree.h>
#include <door_handle_detector/kdtree/kdtree_ann.h>
#include <Eigen/Eigenvalues>

Include dependency graph for nearest.cpp:

Go to the source code of this file.

Namespaces
namespace	cloud_geometry
namespace	cloud_geometry::nearest
Functions
void	cloud_geometry::nearest::computeCentroid (const sensor_msgs::PointCloud &points, const std::vector< int > &indices, sensor_msgs::PointCloud &centroid)
	Compute the centroid of a set of points using their indices and return it as a PointCloud message with 1 value.
void	cloud_geometry::nearest::computeCentroid (const sensor_msgs::PointCloud &points, sensor_msgs::PointCloud &centroid)
	Compute the centroid of a set of points and return it as a PointCloud message with 1 value.
void	cloud_geometry::nearest::computeMomentInvariants (const sensor_msgs::PointCloud &points, double &j1, double &j2, double &j3)
	Compute the 3 moment invariants (j1, j2, j3) for a given set of points.
void	cloud_geometry::nearest::computeMomentInvariants (const sensor_msgs::PointCloud &points, const std::vector< int > &indices, double &j1, double &j2, double &j3)
	Compute the 3 moment invariants (j1, j2, j3) for a given set of points, using their indices.
void	cloud_geometry::nearest::computeOrganizedPointCloudNormals (sensor_msgs::PointCloud &points, const sensor_msgs::PointCloud &surface, int k, int downsample_factor, int width, int height, double max_z, const geometry_msgs::Point32 &viewpoint)
	Estimate the point normals and surface curvatures for a given organized point cloud dataset (points) using the data from a different point cloud (surface) for least-squares planar estimation.
void	cloud_geometry::nearest::computeOrganizedPointCloudNormals (sensor_msgs::PointCloud &points, const sensor_msgs::PointCloudConstPtr &surface, int k, int downsample_factor, int width, int height, double max_z, const geometry_msgs::Point32 &viewpoint)
	Estimate the point normals and surface curvatures for a given organized point cloud dataset (points) using the data from a different point cloud (surface) for least-squares planar estimation.
void	cloud_geometry::nearest::computeOrganizedPointCloudNormalsWithFiltering (sensor_msgs::PointCloud &points, const sensor_msgs::PointCloud &surface, int k, int downsample_factor, int width, int height, double max_z, double min_angle, double max_angle, const geometry_msgs::Point32 &viewpoint)
	Estimate the point normals and surface curvatures for a given organized point cloud dataset (points) using the data from a different point cloud (surface) for least-squares planar estimation.
void	cloud_geometry::nearest::computePatchEigen (const sensor_msgs::PointCloud &points, Eigen::Matrix3d &eigen_vectors, Eigen::Vector3d &eigen_values)
	Compute the eigenvalues and eigenvectors of a given surface patch.
void	cloud_geometry::nearest::computePatchEigen (const sensor_msgs::PointCloud &points, const std::vector< int > &indices, Eigen::Matrix3d &eigen_vectors, Eigen::Vector3d &eigen_values)
	Compute the eigenvalues and eigenvectors of a given surface patch.
void	cloud_geometry::nearest::computePatchEigenNormalized (const sensor_msgs::PointCloud &points, Eigen::Matrix3d &eigen_vectors, Eigen::Vector3d &eigen_values, geometry_msgs::Point32 &centroid)
	Compute the eigenvalues and eigenvectors of a given surface patch from its normalized covariance matrix.
void	cloud_geometry::nearest::computePatchEigenNormalized (const sensor_msgs::PointCloud &points, const std::vector< int > &indices, Eigen::Matrix3d &eigen_vectors, Eigen::Vector3d &eigen_values, geometry_msgs::Point32 &centroid)
	Compute the eigenvalues and eigenvectors of a given surface patch from its normalized covariance matrix.
void	cloud_geometry::nearest::computePointCloudNormals (sensor_msgs::PointCloud &points, const sensor_msgs::PointCloud &surface, int k, const geometry_msgs::PointStamped &viewpoint)
	Estimate (in place) the point normals and surface curvatures for a given point cloud dataset (points) using the data from a different point cloud (surface) for least-squares planar estimation.
void	cloud_geometry::nearest::computePointCloudNormals (sensor_msgs::PointCloud &points, const sensor_msgs::PointCloud &surface, double radius, const geometry_msgs::PointStamped &viewpoint)
	Estimate (in place) the point normals and surface curvatures for a given point cloud dataset (points) using the data from a different point cloud (surface) for least-squares planar estimation.
void	cloud_geometry::nearest::computePointCloudNormals (sensor_msgs::PointCloud &points, int k, const geometry_msgs::PointStamped &viewpoint)
	Estimate (in place) the point normals and surface curvatures for a given point cloud dataset (points)
void	cloud_geometry::nearest::computePointCloudNormals (sensor_msgs::PointCloud &points, double radius, const geometry_msgs::PointStamped &viewpoint)
	Estimate (in place) the point normals and surface curvatures for a given point cloud dataset (points)
void	cloud_geometry::nearest::computePointNormal (const sensor_msgs::PointCloud &points, Eigen::Vector4d &plane_parameters, double &curvature)
	Compute the Least-Squares plane fit for a given set of points, and return the estimated plane parameters together with the surface curvature.
void	cloud_geometry::nearest::computePointNormal (const sensor_msgs::PointCloud &points, const std::vector< int > &indices, Eigen::Vector4d &plane_parameters, double &curvature)
	Compute the Least-Squares plane fit for a given set of points, using their indices, and return the estimated plane parameters together with the surface curvature.
void	cloud_geometry::nearest::computePointNormal (const sensor_msgs::PointCloudConstPtr &points, const std::vector< int > &indices, Eigen::Vector4d &plane_parameters, double &curvature)
	Compute the Least-Squares plane fit for a given set of points, using their indices, and return the estimated plane parameters together with the surface curvature.
void	cloud_geometry::nearest::extractEuclideanClusters (const sensor_msgs::PointCloud &points, const std::vector< int > &indices, double tolerance, std::vector< std::vector< int > > &clusters, int nx_idx, int ny_idx, int nz_idx, double eps_angle, unsigned int min_pts_per_cluster)
	Decompose a region of space into clusters based on the euclidean distance between points, and the normal angular deviation : assumes normalized point normals !
void	cloud_geometry::nearest::extractEuclideanClusters (const sensor_msgs::PointCloud &points, double tolerance, std::vector< std::vector< int > > &clusters, int nx_idx, int ny_idx, int nz_idx, double eps_angle, unsigned int min_pts_per_cluster)
	Decompose a region of space into clusters based on the euclidean distance between points, and the normal angular deviation : assumes normalized point normals !
void	cloud_geometry::nearest::filterJumpEdges (const sensor_msgs::PointCloud &points, sensor_msgs::PointCloud &points_filtered, int k, int width, int height, double min_angle, double max_angle, const geometry_msgs::Point32 &viewpoint)
	Filter jump edges in an organized point cloud dataset (e.g., acquired using TOF or dense stereo, etc)
bool	cloud_geometry::nearest::isBoundaryPoint (const sensor_msgs::PointCloud &points, int q_idx, const std::vector< int > &neighbors, const Eigen::Vector3d &u, const Eigen::Vector3d &v, double angle_threshold)
	Check whether a point is a boundary point in a planar patch of projected points given by indices.