Functions
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	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, 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, 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::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, 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::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, std::vector< int > &indices, sensor_msgs::PointCloud &centroid)
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	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, 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::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)
	Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3d.
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	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	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	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	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, 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, const std::vector< int > &indices, 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, 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, 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	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	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, 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, double radius, const geometry_msgs::PointStamped &viewpoint)
	Estimate (in place) the point normals and surface curvatures for a given point cloud dataset (points)
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	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::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	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	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	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,
		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 57 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 80 of file nearest.h.

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 92 of file nearest.cpp.

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 103 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 126 of file nearest.h.

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 149 of file nearest.h.

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 55 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::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 174 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 205 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 221 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 254 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 285 of file nearest.h.

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 352 of file nearest.cpp.

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 389 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 676 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 773 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 872 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 129 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 150 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 171 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 202 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 480 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 525 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 568 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 623 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 236 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 275 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 315 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 1055 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 1149 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 975 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 305 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 428 of file nearest.cpp.

Functions

Function Documentation