Point container with a flat vector. More...

#include <flat_container.hpp>

Classes
struct	Empty

struct	Setting
	FlatContainer setting. More...

Public Member Functions
	FlatContainer ()
	Constructor. More...

size_t	size () const
	Number of points. More...

template<typename PointCloud >
void	add (const Setting &setting, const Eigen::Vector4d &transformed_pt, const PointCloud &points, size_t i, const Eigen::Isometry3d &T)
	Add a point to the container. If there is a point that is too close to the input point, or there are too many points in the cell, the input point will not be ignored. More...

void	finalize ()
	Finalize the container (Nothing to do for FlatContainer). More...

size_t	nearest_neighbor_search (const Eigen::Vector4d &pt, size_t k_index, double k_sq_dist) const
	Find the nearest neighbor. More...

size_t	knn_search (const Eigen::Vector4d &pt, int k, size_t k_indices, double k_sq_dists) const
	Find k nearest neighbors. More...

template<typename Result >
void	knn_search (const Eigen::Vector4d &pt, Result &result) const
	Find k nearest neighbors. More...

Public Attributes
std::vector< Eigen::Vector4d >	points
	Points. More...

std::conditional_t< HasNormals, std::vector< Eigen::Vector4d >, Empty >	normals
	Normals (Empty if HasNormals is false) More...

std::conditional_t< HasCovs, std::vector< Eigen::Matrix4d >, Empty >	covs
	Covariances (Empty if HasCovs is false) More...

Detailed Description

template<bool HasNormals = false, bool HasCovs = false>
struct small_gicp::FlatContainer< HasNormals, HasCovs >

Point container with a flat vector.

Note: IncrementalVoxelMap combined with FlastContainer is mostly the same as linear iVox. Bai et al., "Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels", IEEE RA-L, 2022; This container stores only up to max_num_points_in_cell points and avoids insertings points that are too close to existing points (min_sq_dist_in_cell).

Template Parameters

HasNormals	If true, store normals.
HasCovs	If true, store covariances.

Constructor & Destructor Documentation

◆ FlatContainer()

template<bool HasNormals = false, bool HasCovs = false>

small_gicp::FlatContainer< HasNormals, HasCovs >::FlatContainer ( )

inline

Constructor.

Member Function Documentation

◆ add()

template<bool HasNormals = false, bool HasCovs = false>

template<typename PointCloud >

void small_gicp::FlatContainer< HasNormals, HasCovs >::add	(	const Setting &	setting,
		const Eigen::Vector4d &	transformed_pt,
		const PointCloud &	points,
		size_t	i,
		const Eigen::Isometry3d &	T
	)

inline

Add a point to the container. If there is a point that is too close to the input point, or there are too many points in the cell, the input point will not be ignored.

Parameters

setting	Setting
transformed_pt	Transformed point (== T * points[i])
points	Point cloud
i	Index of the point
T	Transformation matrix

◆ finalize()

template<bool HasNormals = false, bool HasCovs = false>

void small_gicp::FlatContainer< HasNormals, HasCovs >::finalize ( )

inline

Finalize the container (Nothing to do for FlatContainer).

◆ knn_search() [1/2]

template<bool HasNormals = false, bool HasCovs = false>

size_t small_gicp::FlatContainer< HasNormals, HasCovs >::knn_search	(	const Eigen::Vector4d &	pt,
		int	k,
		size_t *	k_indices,
		double *	k_sq_dists
	)		const

inline

Find k nearest neighbors.

Parameters

pt	Query point
k	Number of neighbors
k_index	Indices of nearest neighbors
k_sq_dist	Squared distances to nearest neighbors (sorted in ascending order)

Returns: Number of found points

◆ knn_search() [2/2]

template<bool HasNormals = false, bool HasCovs = false>

template<typename Result >

void small_gicp::FlatContainer< HasNormals, HasCovs >::knn_search	(	const Eigen::Vector4d &	pt,
		Result &	result
	)		const

inline

Find k nearest neighbors.

Parameters

pt	Query point
k	Number of neighbors
k_index	Indices of nearest neighbors
k_sq_dist	Squared distances to nearest neighbors (sorted in ascending order)

Returns: Number of found points

◆ nearest_neighbor_search()

template<bool HasNormals = false, bool HasCovs = false>

size_t small_gicp::FlatContainer< HasNormals, HasCovs >::nearest_neighbor_search	(	const Eigen::Vector4d &	pt,
		size_t *	k_index,
		double *	k_sq_dist
	)		const

inline

Find the nearest neighbor.

Parameters

pt	Query point
k_index	Index of the nearest neighbor
k_sq_dist	Squared distance to the nearest neighbor

Returns: Number of found points (0 or 1)

◆ size()

template<bool HasNormals = false, bool HasCovs = false>

size_t small_gicp::FlatContainer< HasNormals, HasCovs >::size ( ) const

inline

Number of points.

Member Data Documentation

◆ covs

template<bool HasNormals = false, bool HasCovs = false>

std::conditional_t<HasCovs, std::vector<Eigen::Matrix4d>, Empty> small_gicp::FlatContainer< HasNormals, HasCovs >::covs

Covariances (Empty if HasCovs is false)

◆ normals

template<bool HasNormals = false, bool HasCovs = false>

std::conditional_t<HasNormals, std::vector<Eigen::Vector4d>, Empty> small_gicp::FlatContainer< HasNormals, HasCovs >::normals

Normals (Empty if HasNormals is false)

◆ points

template<bool HasNormals = false, bool HasCovs = false>

std::vector<Eigen::Vector4d> small_gicp::FlatContainer< HasNormals, HasCovs >::points

Points.

The documentation for this struct was generated from the following file:

/home/runner/work/small_gicp/small_gicp/include/small_gicp/ann/flat_container.hpp

Classes

Public Member Functions

Public Attributes

Detailed Description

template<bool HasNormals = false, bool HasCovs = false> struct small_gicp::FlatContainer< HasNormals, HasCovs >

Constructor & Destructor Documentation

◆ FlatContainer()

Member Function Documentation

◆ add()

◆ finalize()

◆ knn_search() [1/2]

◆ knn_search() [2/2]

◆ nearest_neighbor_search()

◆ size()

Member Data Documentation

◆ covs

◆ normals

◆ points

template<bool HasNormals = false, bool HasCovs = false>
struct small_gicp::FlatContainer< HasNormals, HasCovs >