cloud_geometry::nearest Namespace Reference

Functions

void 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 computeCentroid (const sensor_msgs::PointCloud &points, std::vector< int > &indices, sensor_msgs::PointCloud &centroid)
void 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 computeCentroid (const sensor_msgs::PointCloud &points, const std::vector< int > &indices, std::vector< double > &centroid)
 Compute the centroid of a set of points using their indices and return it as a Point32 message.
void computeCentroid (const sensor_msgs::PointCloudConstPtr &points, const std::vector< int > &indices, geometry_msgs::Point32 &centroid)
 Compute the centroid of a set of points using their indices and return it as a Point32 message.
void computeCentroid (const sensor_msgs::PointCloud &points, const std::vector< int > &indices, geometry_msgs::Point32 &centroid)
 Compute the centroid of a set of points using their indices and return it as a Point32 message.
void computeCentroid (const geometry_msgs::Polygon &poly, geometry_msgs::Point32 &centroid)
 Compute the centroid of a 3D polygon and return it as a Point32 message.
void computeCentroid (const sensor_msgs::PointCloud &points, geometry_msgs::Point32 &centroid)
 Compute the centroid of a set of points and return it as a Point32 message.
void computeCovarianceMatrix (const sensor_msgs::PointCloud &points, const std::vector< int > &indices, Eigen::Matrix3d &covariance_matrix)
 Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3d.
void computeCovarianceMatrix (const sensor_msgs::PointCloudConstPtr &points, const std::vector< int > &indices, Eigen::Matrix3d &covariance_matrix, geometry_msgs::Point32 &centroid)
 Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3d.
void computeCovarianceMatrix (const sensor_msgs::PointCloud &points, const std::vector< int > &indices, Eigen::Matrix3d &covariance_matrix, geometry_msgs::Point32 &centroid)
 Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3d.
void computeCovarianceMatrix (const sensor_msgs::PointCloud &points, Eigen::Matrix3d &covariance_matrix)
 Compute the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix3d.
void computeCovarianceMatrix (const sensor_msgs::PointCloud &points, Eigen::Matrix3d &covariance_matrix, geometry_msgs::Point32 &centroid)
 Compute the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix3d.
void 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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).
double getAngleWithViewpoint (float px, float py, float pz, float qx, float qy, float qz, float vp_x=0, float vp_y=0, float vp_z=0)
 Computes the angle between the lines created from 2 points and the viewpoint: [vp->p1], [p1->p2] Returns the angle (in degrees).
bool 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.

Function Documentation

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.

Parameters:
points the input point cloud
indices the point cloud indices that need to be used
centroid the output centroid

Definition at line 86 of file nearest.cpp.

void cloud_geometry::nearest::computeCentroid ( const sensor_msgs::PointCloud &  points,
std::vector< int > &  indices,
sensor_msgs::PointCloud &  centroid 
)
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.

Parameters:
points the input point cloud
centroid the output centroid

Definition at line 49 of file nearest.cpp.

void cloud_geometry::nearest::computeCentroid ( const sensor_msgs::PointCloud &  points,
const std::vector< int > &  indices,
std::vector< double > &  centroid 
) [inline]

Compute the centroid of a set of points using their indices and return it as a Point32 message.

Parameters:
points the input point cloud
indices the point cloud indices that need to be used
centroid the output centroid

Definition at line 144 of file nearest.h.

void cloud_geometry::nearest::computeCentroid ( const sensor_msgs::PointCloudConstPtr &  points,
const std::vector< int > &  indices,
geometry_msgs::Point32 &  centroid 
) [inline]

Compute the centroid of a set of points using their indices and return it as a Point32 message.

Parameters:
points the input point cloud
indices the point cloud indices that need to be used
centroid the output centroid

Definition at line 121 of file nearest.h.

void cloud_geometry::nearest::computeCentroid ( const sensor_msgs::PointCloud &  points,
const std::vector< int > &  indices,
geometry_msgs::Point32 &  centroid 
) [inline]

Compute the centroid of a set of points using their indices and return it as a Point32 message.

Parameters:
points the input point cloud
indices the point cloud indices that need to be used
centroid the output centroid

Definition at line 98 of file nearest.h.

void cloud_geometry::nearest::computeCentroid ( const geometry_msgs::Polygon &  poly,
geometry_msgs::Point32 &  centroid 
) [inline]

Compute the centroid of a 3D polygon and return it as a Point32 message.

Parameters:
poly the input polygon
centroid the output centroid

Definition at line 75 of file nearest.h.

void cloud_geometry::nearest::computeCentroid ( const sensor_msgs::PointCloud &  points,
geometry_msgs::Point32 &  centroid 
) [inline]

Compute the centroid of a set of points and return it as a Point32 message.

Parameters:
points the input point cloud
centroid the output centroid

Definition at line 52 of file nearest.h.

void cloud_geometry::nearest::computeCovarianceMatrix ( const sensor_msgs::PointCloud &  points,
const std::vector< int > &  indices,
Eigen::Matrix3d &  covariance_matrix 
) [inline]

Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3d.

Parameters:
points the input point cloud
indices the point cloud indices that need to be used
covariance_matrix the 3x3 covariance matrix

Definition at line 280 of file nearest.h.

void cloud_geometry::nearest::computeCovarianceMatrix ( const sensor_msgs::PointCloudConstPtr &  points,
const std::vector< int > &  indices,
Eigen::Matrix3d &  covariance_matrix,
geometry_msgs::Point32 &  centroid 
) [inline]

Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3d.

Note:
The (x-y-z) centroid is also returned as a Point32 message.
Parameters:
points the input point cloud
indices the point cloud indices that need to be used
covariance_matrix the 3x3 covariance matrix
centroid the computed centroid

Definition at line 249 of file nearest.h.

void cloud_geometry::nearest::computeCovarianceMatrix ( const sensor_msgs::PointCloud &  points,
const std::vector< int > &  indices,
Eigen::Matrix3d &  covariance_matrix,
geometry_msgs::Point32 &  centroid 
) [inline]

Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3d.

Note:
The (x-y-z) centroid is also returned as a Point32 message.
Parameters:
points the input point cloud
indices the point cloud indices that need to be used
covariance_matrix the 3x3 covariance matrix
centroid the computed centroid

Definition at line 216 of file nearest.h.

void cloud_geometry::nearest::computeCovarianceMatrix ( const sensor_msgs::PointCloud &  points,
Eigen::Matrix3d &  covariance_matrix 
) [inline]

Compute the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix3d.

Parameters:
points the input point cloud
covariance_matrix the 3x3 covariance matrix

Definition at line 200 of file nearest.h.

void cloud_geometry::nearest::computeCovarianceMatrix ( const sensor_msgs::PointCloud &  points,
Eigen::Matrix3d &  covariance_matrix,
geometry_msgs::Point32 &  centroid 
) [inline]

Compute the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix3d.

Note:
The (x-y-z) centroid is also returned as a Point32 message.
Parameters:
points the input point cloud
covariance_matrix the 3x3 covariance matrix
centroid the computed centroid

Definition at line 169 of file nearest.h.

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.

Parameters:
points the input point cloud
indices the point cloud indices that need to be used
j1 the first moment invariant
j2 the second moment invariant
j3 the third moment invariant

Definition at line 384 of file nearest.cpp.

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.

Parameters:
points the input point cloud
j1 the first moment invariant
j2 the second moment invariant
j3 the third moment invariant

Definition at line 347 of file nearest.cpp.

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.

Note:
The main difference between this method and its computePointCloudNormals () sibblings is that assumptions are being made regarding the organization of points in the cloud. For example, point clouds acquired using camera sensors are assumed to be row-major, and the width and height of the depth image are assumed to be known, etc.
Parameters:
points the input point cloud (on output, 4 extra channels will be added: nx, ny, nz, and curvatures)
surface the point cloud data to use for least-squares planar estimation
k the windowing factor (i.e., how many pixels in the depth image in all directions should the neighborhood of a point contain)
downsample_factor factor for downsampling the input data, i.e., take every Nth row and column in the depth image
width the width in pixels of the depth image
height the height in pixels of the depth image
max_z maximum distance threshold (on Z) between the query point and its neighbors (set to -1 to ignore the check)
viewpoint the viewpoint where the cloud was acquired from (used for normal flip)

Definition at line 768 of file nearest.cpp.

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.

Note:
The main difference between this method and its computePointCloudNormals () sibblings is that assumptions are being made regarding the organization of points in the cloud. For example, point clouds acquired using camera sensors are assumed to be row-major, and the width and height of the depth image are assumed to be known, etc.
Parameters:
points the input point cloud (on output, 4 extra channels will be added: nx, ny, nz, and curvatures)
surface the point cloud data to use for least-squares planar estimation
k the windowing factor (i.e., how many pixels in the depth image in all directions should the neighborhood of a point contain)
downsample_factor factor for downsampling the input data, i.e., take every Nth row and column in the depth image
width the width in pixels of the depth image
height the height in pixels of the depth image
max_z maximum distance threshold (on Z) between the query point and its neighbors (set to -1 to ignore the check)
viewpoint the viewpoint where the cloud was acquired from (used for normal flip)

Definition at line 671 of file nearest.cpp.

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.

Note:
The main difference between this method and its computePointCloudNormals () sibblings is that assumptions are being made regarding the organization of points in the cloud. For example, point clouds acquired using camera sensors are assumed to be row-major, and the width and height of the depth image are assumed to be known, etc.
Parameters:
points the input point cloud (on output, 4 extra channels will be added: nx, ny, nz, and curvatures)
surface the point cloud data to use for least-squares planar estimation
k the windowing factor (i.e., how many pixels in the depth image in all directions should the neighborhood of a point contain)
downsample_factor factor for downsampling the input data, i.e., take every Nth row and column in the depth image
width the width in pixels of the depth image
height the height in pixels of the depth image
max_z maximum distance threshold (on Z) between the query point and its neighbors (set to -1 to ignore the check)
min_angle the minimum angle allowed between the viewpoint ray and the line formed by two subsequent points(used for filtering jump edge data)
max_angle the maximum angle allowed between the viewpoint ray and the line formed by two subsequent points(used for filtering jump edge data)
viewpoint the viewpoint where the cloud was acquired from (used for normal flip)

Definition at line 867 of file nearest.cpp.

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.

Parameters:
points the input point cloud
indices the point cloud indices that need to be used
eigen_vectors the resultant eigenvectors
eigen_values the resultant eigenvalues

Definition at line 145 of file nearest.cpp.

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.

Parameters:
points the input point cloud
eigen_vectors the resultant eigenvectors
eigen_values the resultant eigenvalues

Definition at line 123 of file nearest.cpp.

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.

Parameters:
points the input point cloud
indices the point cloud indices that need to be used
eigen_vectors the resultant eigenvectors
eigen_values the resultant eigenvalues
centroid the centroid of the points

Definition at line 197 of file nearest.cpp.

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.

Parameters:
points the input point cloud
eigen_vectors the resultant eigenvectors
eigen_values the resultant eigenvalues
centroid the centroid of the points

Definition at line 166 of file nearest.cpp.

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).

Parameters:
points the input point cloud (on output, 4 extra channels will be added: nx, ny, nz, and curvatures)
radius use a neighbor fixed radius search for the least-squares fit
viewpoint the viewpoint where the cloud was acquired from (used for normal flip)

Definition at line 618 of file nearest.cpp.

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).

Parameters:
points the input point cloud (on output, 4 extra channels will be added: nx, ny, nz, and curvatures)
k use a fixed number of k-nearest neighbors for the least-squares fit
viewpoint the viewpoint where the cloud was acquired from (used for normal flip)

Definition at line 563 of file nearest.cpp.

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.

Parameters:
points the input point cloud (on output, 4 extra channels will be added: nx, ny, nz, and curvatures)
surface the point cloud data to use for least-squares planar estimation
radius use a neighbor fixed radius search for the least-squares fit
viewpoint the viewpoint where the cloud was acquired from (used for normal flip)

Definition at line 520 of file nearest.cpp.

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.

Parameters:
points the input point cloud (on output, 4 extra channels will be added: nx, ny, nz, and curvatures)
surface the point cloud data to use for least-squares planar estimation
k use a fixed number of k-nearest neighbors for the least-squares fit
viewpoint the viewpoint where the cloud was acquired from (used for normal flip)

Definition at line 475 of file nearest.cpp.

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.

Parameters:
points the input point cloud
indices the point cloud indices that need to be used
plane_parameters the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
curvature the estimated surface curvature as a measure of

\[ \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2) \]

Definition at line 310 of file nearest.cpp.

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.

Parameters:
points the input point cloud
indices the point cloud indices that need to be used
plane_parameters the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
curvature the estimated surface curvature as a measure of

\[ \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2) \]

Definition at line 270 of file nearest.cpp.

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.

Parameters:
points the input point cloud
plane_parameters the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
curvature the estimated surface curvature as a measure of

\[ \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2) \]

Definition at line 231 of file nearest.cpp.

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 !

Parameters:
points the point cloud message
tolerance the spatial tolerance as a measure in the L2 Euclidean space
clusters the resultant clusters containing point indices
nx_idx the index of the channel containing the X component of the normal (use -1 to disable the check)
ny_idx the index of the channel containing the Y component of the normal (use -1 to disable the check)
nz_idx the index of the channel containing the Z component of the normal (use -1 to disable the check)
eps_angle the maximum allowed difference between normals in degrees for cluster/region growing (only valid if nx_idx && ny_idx && nz_idx != -1)
min_pts_per_cluster minimum number of points that a cluster may contain (default = 1)

Definition at line 1144 of file nearest.cpp.

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 !

Parameters:
points the point cloud message
indices a list of point indices to use
tolerance the spatial tolerance as a measure in the L2 Euclidean space
clusters the resultant clusters containing point indices
nx_idx the index of the channel containing the X component of the normal (use -1 to disable the check)
ny_idx the index of the channel containing the Y component of the normal (use -1 to disable the check)
nz_idx the index of the channel containing the Z component of the normal (use -1 to disable the check)
eps_angle the maximum allowed difference between normals in degrees for cluster/region growing (only valid if nx_idx && ny_idx && nz_idx != -1)
min_pts_per_cluster minimum number of points that a cluster may contain (default = 1)

Definition at line 1050 of file nearest.cpp.

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).

the input point cloud size is assumed to have (width*height) number of points, in order!

Parameters:
points the input organized point cloud data
points_filtered the output filtered point cloud data
k the windowing factor (i.e., how many pixels in the depth image in all directions should the neighborhood of a point contain)
width the width in pixels of the depth image
height the height in pixels of the depth image
min_angle the minimum angle allowed between the viewpoint ray and the line formed by two subsequent points(used for filtering jump edge data)
max_angle the maximum angle allowed between the viewpoint ray and the line formed by two subsequent points(used for filtering jump edge data)
viewpoint the viewpoint where the cloud was acquired from (used for normal flip)

Definition at line 970 of file nearest.cpp.

double cloud_geometry::nearest::getAngleWithViewpoint ( float  px,
float  py,
float  pz,
float  qx,
float  qy,
float  qz,
float  vp_x = 0,
float  vp_y = 0,
float  vp_z = 0 
) [inline]

Computes the angle between the lines created from 2 points and the viewpoint: [vp->p1], [p1->p2] Returns the angle (in degrees).

Parameters:
px X coordinate for the first point
py Y coordinate for the first point
pz Z coordinate for the first point
qx X coordinate for the second point
qy Y coordinate for the second point
qz Z coordinate for the second point
vp_x X coordinate of the viewpoint (0 by default)
vp_y Y coordinate of the viewpoint (0 by default)
vp_z Z coordinate of the viewpoint (0 by default)

Definition at line 300 of file nearest.h.

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.

Note:
A coordinate system u-v-n must be computed a-priori using getCoordinateSystemOnPlane
Parameters:
points a pointer to the input point cloud
q_idx the index of the query point in points
neighbors the estimated point neighbors of the query point
u the u direction
v the v direction
angle_threshold the threshold angle (default $ / 2.0$)

Definition at line 423 of file nearest.cpp.

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stereo_wall_detection
Author(s): Radu Bogdan Rusu
autogenerated on Fri Jan 11 09:37:23 2013