Detailed Description

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>
class mrpt::math::KDTreeCapable< Derived, num_t, metric_t >

A generic adaptor class for providing Approximate Nearest Neighbors (ANN) (via the nanoflann library) to MRPT classses.

This makes use of the CRTP design pattern.

Derived classes must be aware of the need to call "kdtree_mark_as_outdated()" when the data points change to mark the cached KD-tree (an "index") as invalid, and also implement the following interface (note that these are not virtual functions due to the usage of CRTP):

// Must return the number of data points
inline size_t kdtree_get_point_count() const { ... }
// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
inline float kdtree_distance(const float *p1, const size_t idx_p2,size_t size) const { ... }
// Returns the dim'th component of the idx'th point in the class:
inline num_t kdtree_get_pt(const size_t idx, int dim) const { ... }
// Optional bounding-box computation: return false to default to a standard bbox computation loop.
//   Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
//   Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
template <class BBOX>
bool kdtree_get_bbox(BBOX &bb) const
{
   bb[0].low = ...; bb[0].high = ...;  // "x" limits
   return true;
}

The KD-tree index will be built on demand only upon call of any of the query methods provided by this class.

Notice that there is only ONE internal cached KD-tree, so if a method to query a 2D point is called, then another method for 3D points, then again the 2D method, three KD-trees will be built. So, try to group all the calls for a given dimensionality together or build different class instances for queries of each dimensionality, etc.

See also: See some of the derived classes for example implementations. See also the documentation of nanoflann

Definition at line 67 of file KDTreeCapable.h.

#include <mrpt/math/KDTreeCapable.h>

Inheritance diagram for mrpt::math::KDTreeCapable< Derived, num_t, metric_t >:

[legend]

Classes
struct	TKDTreeDataHolder
	Internal structure with the KD-tree representation (mainly used to avoid copying pointers with the = operator) More...

struct	TKDTreeSearchParams

Public Types
typedef KDTreeCapable< Derived, num_t, metric_t >	self_t

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

	~KDTreeCapable ()
	Destructor (nothing needed to do here) More...

const Derived &	derived () const
	CRTP helper method. More...

Derived &	derived ()
	CRTP helper method. More...

Public utility methods to query the KD-tree
size_t	kdTreeClosestPoint2D (float x0, float y0, float &out_x, float &out_y, float &out_dist_sqr) const
	KD Tree-based search for the closest point (only ONE) to some given 2D coordinates. More...

size_t	kdTreeClosestPoint2D (float x0, float y0, float &out_dist_sqr) const

size_t	kdTreeClosestPoint2D (const TPoint2D &p0, TPoint2D &pOut, float &outDistSqr) const

float	kdTreeClosestPoint2DsqrError (float x0, float y0) const
	Like kdTreeClosestPoint2D, but just return the square error from some point to its closest neighbor. More...

float	kdTreeClosestPoint2DsqrError (const TPoint2D &p0) const

void	kdTreeTwoClosestPoint2D (float x0, float y0, float &out_x1, float &out_y1, float &out_x2, float &out_y2, float &out_dist_sqr1, float &out_dist_sqr2) const
	KD Tree-based search for the TWO closest point to some given 2D coordinates. More...

void	kdTreeTwoClosestPoint2D (const TPoint2D &p0, TPoint2D &pOut1, TPoint2D &pOut2, float &outDistSqr1, float &outDistSqr2) const

std::vector< size_t >	kdTreeNClosestPoint2D (float x0, float y0, size_t knn, std::vector< float > &out_x, std::vector< float > &out_y, std::vector< float > &out_dist_sqr) const
	KD Tree-based search for the N closest point to some given 2D coordinates. More...

std::vector< size_t >	kdTreeNClosestPoint2D (const TPoint2D &p0, size_t N, std::vector< TPoint2D > &pOut, std::vector< float > &outDistSqr) const

void	kdTreeNClosestPoint2DIdx (float x0, float y0, size_t knn, std::vector< size_t > &out_idx, std::vector< float > &out_dist_sqr) const
	KD Tree-based search for the N closest point to some given 2D coordinates and returns their indexes. More...

void	kdTreeNClosestPoint2DIdx (const TPoint2D &p0, size_t N, std::vector< size_t > &outIdx, std::vector< float > &outDistSqr) const

size_t	kdTreeClosestPoint3D (float x0, float y0, float z0, float &out_x, float &out_y, float &out_z, float &out_dist_sqr) const
	KD Tree-based search for the closest point (only ONE) to some given 3D coordinates. More...

size_t	kdTreeClosestPoint3D (float x0, float y0, float z0, float &out_dist_sqr) const

size_t	kdTreeClosestPoint3D (const TPoint3D &p0, TPoint3D &pOut, float &outDistSqr) const

void	kdTreeNClosestPoint3D (float x0, float y0, float z0, size_t knn, std::vector< float > &out_x, std::vector< float > &out_y, std::vector< float > &out_z, std::vector< float > &out_dist_sqr) const
	KD Tree-based search for the N closest points to some given 3D coordinates. More...

void	kdTreeNClosestPoint3DWithIdx (float x0, float y0, float z0, size_t knn, std::vector< float > &out_x, std::vector< float > &out_y, std::vector< float > &out_z, std::vector< size_t > &out_idx, std::vector< float > &out_dist_sqr) const
	KD Tree-based search for the N closest points to some given 3D coordinates. More...

void	kdTreeNClosestPoint3D (const TPoint3D &p0, size_t N, std::vector< TPoint3D > &pOut, std::vector< float > &outDistSqr) const

size_t	kdTreeRadiusSearch3D (const num_t x0, const num_t y0, const num_t z0, const num_t maxRadius, std::vector< std::pair< size_t, num_t > > &out_indices_dist) const
	KD Tree-based search for all the points within a given radius of some 3D point. More...

size_t	kdTreeRadiusSearch2D (const num_t x0, const num_t y0, const num_t maxRadius, std::vector< std::pair< size_t, num_t > > &out_indices_dist) const
	KD Tree-based search for all the points within a given radius of some 2D point. More...

void	kdTreeNClosestPoint3DIdx (float x0, float y0, float z0, size_t knn, std::vector< size_t > &out_idx, std::vector< float > &out_dist_sqr) const
	KD Tree-based search for the N closest point to some given 3D coordinates and returns their indexes. More...

void	kdTreeNClosestPoint3DIdx (const TPoint3D &p0, size_t N, std::vector< size_t > &outIdx, std::vector< float > &outDistSqr) const

Public Attributes
TKDTreeSearchParams	kdtree_search_params
	Parameters to tune the ANN searches. More...

Protected Member Functions
void	kdtree_mark_as_outdated () const
	To be called by child classes when KD tree data changes. More...

Private Member Functions
void	rebuild_kdTree_2D () const
	Rebuild, if needed the KD-tree for 2D (nDims=2), 3D (nDims=3), ... asking the child class for the data points. More...

void	rebuild_kdTree_3D () const
	Rebuild, if needed the KD-tree for 2D (nDims=2), 3D (nDims=3), ... asking the child class for the data points. More...

Private Attributes
TKDTreeDataHolder< 2 >	m_kdtree2d_data

TKDTreeDataHolder< 3 >	m_kdtree3d_data

TKDTreeDataHolder	m_kdtreeNd_data

bool	m_kdtree_is_uptodate
	whether the KD tree needs to be rebuilt or not. More...

Member Typedef Documentation

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

typedef KDTreeCapable<Derived,num_t,metric_t> mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::self_t

Definition at line 71 of file KDTreeCapable.h.

Constructor & Destructor Documentation

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::KDTreeCapable ( )

inline

Constructor.

Definition at line 75 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::~KDTreeCapable ( )

inline

Destructor (nothing needed to do here)

Definition at line 78 of file KDTreeCapable.h.

Member Function Documentation

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

const Derived& mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::derived ( ) const

inline

CRTP helper method.

Definition at line 81 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

Derived& mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::derived ( )

inline

CRTP helper method.

Definition at line 83 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

void mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdtree_mark_as_outdated ( ) const

inlineprotected

To be called by child classes when KD tree data changes.

Definition at line 675 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

size_t mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeClosestPoint2D	(	float	x0,
		float	y0,
		float &	out_x,
		float &	out_y,
		float &	out_dist_sqr
	)		const

inline

KD Tree-based search for the closest point (only ONE) to some given 2D coordinates.

This method automatically build the "m_kdtree_data" structure when:

It is called for the first time
The map has changed
The KD-tree was build for 3D.

Parameters

x0	The X coordinate of the query.
y0	The Y coordinate of the query.
out_x	The X coordinate of the found closest correspondence.
out_y	The Y coordinate of the found closest correspondence.
out_dist_sqr	The square distance between the query and the returned point.

Returns: The index of the closest point in the map array.

See also: kdTreeClosestPoint3D, kdTreeTwoClosestPoint2D

Definition at line 117 of file KDTreeCapable.h.

Referenced by mrpt::math::KDTreeCapable< CFeatureList >::kdTreeClosestPoint2D(), and mrpt::math::KDTreeCapable< CFeatureList >::kdTreeClosestPoint2DsqrError().

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

size_t mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeClosestPoint2D	(	float	x0,
		float	y0,
		float &	out_dist_sqr
	)		const

inline

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Definition at line 147 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

size_t mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeClosestPoint2D	(	const TPoint2D &	p0,
		TPoint2D &	pOut,
		float &	outDistSqr
	)		const

inline

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Definition at line 170 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

float mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeClosestPoint2DsqrError	(	float	x0,
		float	y0
	)		const

inline

Like kdTreeClosestPoint2D, but just return the square error from some point to its closest neighbor.

Definition at line 180 of file KDTreeCapable.h.

Referenced by mrpt::math::KDTreeCapable< CFeatureList >::kdTreeClosestPoint2DsqrError().

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

float mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeClosestPoint2DsqrError ( const TPoint2D & p0 ) const

inline

Definition at line 189 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

size_t mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeClosestPoint3D	(	float	x0,
		float	y0,
		float	z0,
		float &	out_x,
		float &	out_y,
		float &	out_z,
		float &	out_dist_sqr
	)		const

inline

KD Tree-based search for the closest point (only ONE) to some given 3D coordinates.

This method automatically build the "m_kdtree_data" structure when:

It is called for the first time
The map has changed
The KD-tree was build for 2D.

Parameters

x0	The X coordinate of the query.
y0	The Y coordinate of the query.
z0	The Z coordinate of the query.
out_x	The X coordinate of the found closest correspondence.
out_y	The Y coordinate of the found closest correspondence.
out_z	The Z coordinate of the found closest correspondence.
out_dist_sqr	The square distance between the query and the returned point.

Returns: The index of the closest point in the map array.

See also: kdTreeClosestPoint2D

Definition at line 375 of file KDTreeCapable.h.

Referenced by mrpt::math::KDTreeCapable< CFeatureList >::kdTreeClosestPoint3D().

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

size_t mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeClosestPoint3D	(	float	x0,
		float	y0,
		float	z0,
		float &	out_dist_sqr
	)		const

inline

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Definition at line 409 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

size_t mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeClosestPoint3D	(	const TPoint3D &	p0,
		TPoint3D &	pOut,
		float &	outDistSqr
	)		const

inline

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Definition at line 435 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

std::vector<size_t> mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeNClosestPoint2D	(	float	x0,
		float	y0,
		size_t	knn,
		std::vector< float > &	out_x,
		std::vector< float > &	out_y,
		std::vector< float > &	out_dist_sqr
	)		const

inline

KD Tree-based search for the N closest point to some given 2D coordinates.

This method automatically build the "m_kdtree_data" structure when:

It is called for the first time
The map has changed
The KD-tree was build for 3D.

Parameters

x0	The X coordinate of the query.
y0	The Y coordinate of the query.
N	The number of closest points to search.
out_x	The vector containing the X coordinates of the correspondences.
out_y	The vector containing the Y coordinates of the correspondences.
out_dist_sqr	The vector containing the square distance between the query and the returned points.

Returns: The list of indices

See also: kdTreeClosestPoint2D; kdTreeTwoClosestPoint2D

Definition at line 274 of file KDTreeCapable.h.

Referenced by mrpt::math::KDTreeCapable< CFeatureList >::kdTreeNClosestPoint2D().

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

std::vector<size_t> mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeNClosestPoint2D	(	const TPoint2D &	p0,
		size_t	N,
		std::vector< TPoint2D > &	pOut,
		std::vector< float > &	outDistSqr
	)		const

inline

Definition at line 307 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

void mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeNClosestPoint2DIdx	(	float	x0,
		float	y0,
		size_t	knn,
		std::vector< size_t > &	out_idx,
		std::vector< float > &	out_dist_sqr
	)		const

inline

KD Tree-based search for the N closest point to some given 2D coordinates and returns their indexes.

This method automatically build the "m_kdtree_data" structure when:

It is called for the first time
The map has changed
The KD-tree was build for 3D.

Parameters

x0	The X coordinate of the query.
y0	The Y coordinate of the query.
N	The number of closest points to search.
out_idx	The indexes of the found closest correspondence.
out_dist_sqr	The square distance between the query and the returned point.

See also: kdTreeClosestPoint2D

Definition at line 332 of file KDTreeCapable.h.

Referenced by mrpt::math::KDTreeCapable< CFeatureList >::kdTreeNClosestPoint2DIdx().

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

void mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeNClosestPoint2DIdx	(	const TPoint2D &	p0,
		size_t	N,
		std::vector< size_t > &	outIdx,
		std::vector< float > &	outDistSqr
	)		const

inline

Definition at line 354 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

void mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeNClosestPoint3D	(	float	x0,
		float	y0,
		float	z0,
		size_t	knn,
		std::vector< float > &	out_x,
		std::vector< float > &	out_y,
		std::vector< float > &	out_z,
		std::vector< float > &	out_dist_sqr
	)		const

inline

KD Tree-based search for the N closest points to some given 3D coordinates.

This method automatically build the "m_kdtree_data" structure when:

It is called for the first time
The map has changed
The KD-tree was build for 2D.

Parameters

x0	The X coordinate of the query.
y0	The Y coordinate of the query.
z0	The Z coordinate of the query.
N	The number of closest points to search.
out_x	The vector containing the X coordinates of the correspondences.
out_y	The vector containing the Y coordinates of the correspondences.
out_z	The vector containing the Z coordinates of the correspondences.
out_dist_sqr	The vector containing the square distance between the query and the returned points.

See also: kdTreeNClosestPoint2D

Definition at line 461 of file KDTreeCapable.h.

Referenced by mrpt::math::KDTreeCapable< CFeatureList >::kdTreeNClosestPoint3D().

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

void mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeNClosestPoint3D	(	const TPoint3D &	p0,
		size_t	N,
		std::vector< TPoint3D > &	pOut,
		std::vector< float > &	outDistSqr
	)		const

inline

Definition at line 554 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

void mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeNClosestPoint3DIdx	(	float	x0,
		float	y0,
		float	z0,
		size_t	knn,
		std::vector< size_t > &	out_idx,
		std::vector< float > &	out_dist_sqr
	)		const

inline

KD Tree-based search for the N closest point to some given 3D coordinates and returns their indexes.

This method automatically build the "m_kdtree_data" structure when:

It is called for the first time
The map has changed
The KD-tree was build for 2D.

Parameters

x0	The X coordinate of the query.
y0	The Y coordinate of the query.
z0	The Z coordinate of the query.
N	The number of closest points to search.
out_idx	The indexes of the found closest correspondence.
out_dist_sqr	The square distance between the query and the returned point.

See also: kdTreeClosestPoint2D, kdTreeRadiusSearch3D

Definition at line 643 of file KDTreeCapable.h.

Referenced by mrpt::math::KDTreeCapable< CFeatureList >::kdTreeNClosestPoint3DIdx().

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

void mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeNClosestPoint3DIdx	(	const TPoint3D &	p0,
		size_t	N,
		std::vector< size_t > &	outIdx,
		std::vector< float > &	outDistSqr
	)		const

inline

Definition at line 667 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

void mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeNClosestPoint3DWithIdx	(	float	x0,
		float	y0,
		float	z0,
		size_t	knn,
		std::vector< float > &	out_x,
		std::vector< float > &	out_y,
		std::vector< float > &	out_z,
		std::vector< size_t > &	out_idx,
		std::vector< float > &	out_dist_sqr
	)		const

inline

KD Tree-based search for the N closest points to some given 3D coordinates.

This method automatically build the "m_kdtree_data" structure when:

It is called for the first time
The map has changed
The KD-tree was build for 2D.

Parameters

x0	The X coordinate of the query.
y0	The Y coordinate of the query.
z0	The Z coordinate of the query.
N	The number of closest points to search.
out_x	The vector containing the X coordinates of the correspondences.
out_y	The vector containing the Y coordinates of the correspondences.
out_z	The vector containing the Z coordinates of the correspondences.
out_idx	The vector containing the indexes of the correspondences.
out_dist_sqr	The vector containing the square distance between the query and the returned points.

See also: kdTreeNClosestPoint2D

Definition at line 516 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

size_t mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeRadiusSearch2D	(	const num_t	x0,
		const num_t	y0,
		const num_t	maxRadius,
		std::vector< std::pair< size_t, num_t > > &	out_indices_dist
	)		const

inline

KD Tree-based search for all the points within a given radius of some 2D point.

This method automatically build the "m_kdtree_data" structure when:

It is called for the first time
The map has changed
The KD-tree was build for 3D.

Parameters

x0	The X coordinate of the query.
y0	The Y coordinate of the query.
maxRadius	The search radius.
out_indices_dist	The output list, with pairs of indeces/squared distances for the found correspondences.

Returns: Number of found points.

See also: kdTreeRadiusSearch3D, kdTreeNClosestPoint2DIdx

Definition at line 611 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

size_t mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeRadiusSearch3D	(	const num_t	x0,
		const num_t	y0,
		const num_t	z0,
		const num_t	maxRadius,
		std::vector< std::pair< size_t, num_t > > &	out_indices_dist
	)		const

inline

KD Tree-based search for all the points within a given radius of some 3D point.

This method automatically build the "m_kdtree_data" structure when:

It is called for the first time
The map has changed
The KD-tree was build for 2D.

Parameters

x0	The X coordinate of the query.
y0	The Y coordinate of the query.
z0	The Z coordinate of the query.
maxRadius	The search radius.
out_indices_dist	The output list, with pairs of indeces/squared distances for the found correspondences.

Returns: Number of found points.

See also: kdTreeRadiusSearch2D, kdTreeNClosestPoint3DIdx

Definition at line 580 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

void mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeTwoClosestPoint2D	(	float	x0,
		float	y0,
		float &	out_x1,
		float &	out_y1,
		float &	out_x2,
		float &	out_y2,
		float &	out_dist_sqr1,
		float &	out_dist_sqr2
	)		const

inline

KD Tree-based search for the TWO closest point to some given 2D coordinates.

This method automatically build the "m_kdtree_data" structure when:

It is called for the first time
The map has changed
The KD-tree was build for 3D.

Parameters

x0	The X coordinate of the query.
y0	The Y coordinate of the query.
out_x1	The X coordinate of the first correspondence.
out_y1	The Y coordinate of the first correspondence.
out_x2	The X coordinate of the second correspondence.
out_y2	The Y coordinate of the second correspondence.
out_dist_sqr1	The square distance between the query and the first returned point.
out_dist_sqr2	The square distance between the query and the second returned point.

See also: kdTreeClosestPoint2D

Definition at line 210 of file KDTreeCapable.h.

Referenced by mrpt::math::KDTreeCapable< CFeatureList >::kdTreeTwoClosestPoint2D().

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

void mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdTreeTwoClosestPoint2D	(	const TPoint2D &	p0,
		TPoint2D &	pOut1,
		TPoint2D &	pOut2,
		float &	outDistSqr1,
		float &	outDistSqr2
	)		const

inline

Definition at line 247 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

void mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::rebuild_kdTree_2D ( ) const

inlineprivate

Rebuild, if needed the KD-tree for 2D (nDims=2), 3D (nDims=3), ... asking the child class for the data points.

Definition at line 715 of file KDTreeCapable.h.

Referenced by mrpt::math::KDTreeCapable< CFeatureList >::kdTreeClosestPoint2D(), mrpt::math::KDTreeCapable< CFeatureList >::kdTreeNClosestPoint2D(), mrpt::math::KDTreeCapable< CFeatureList >::kdTreeNClosestPoint2DIdx(), mrpt::math::KDTreeCapable< CFeatureList >::kdTreeRadiusSearch2D(), and mrpt::math::KDTreeCapable< CFeatureList >::kdTreeTwoClosestPoint2D().

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

void mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::rebuild_kdTree_3D ( ) const

inlineprivate

Rebuild, if needed the KD-tree for 2D (nDims=2), 3D (nDims=3), ... asking the child class for the data points.

Definition at line 740 of file KDTreeCapable.h.

Referenced by mrpt::math::KDTreeCapable< CFeatureList >::kdTreeClosestPoint3D(), mrpt::math::KDTreeCapable< CFeatureList >::kdTreeNClosestPoint3D(), mrpt::math::KDTreeCapable< CFeatureList >::kdTreeNClosestPoint3DIdx(), mrpt::math::KDTreeCapable< CFeatureList >::kdTreeNClosestPoint3DWithIdx(), and mrpt::math::KDTreeCapable< CFeatureList >::kdTreeRadiusSearch3D().

Member Data Documentation

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

TKDTreeSearchParams mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::kdtree_search_params

Parameters to tune the ANN searches.

Definition at line 97 of file KDTreeCapable.h.

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

TKDTreeDataHolder<2> mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::m_kdtree2d_data

mutableprivate

Definition at line 709 of file KDTreeCapable.h.

Referenced by mrpt::math::KDTreeCapable< CFeatureList >::kdTreeClosestPoint2D(), mrpt::math::KDTreeCapable< CFeatureList >::kdTreeNClosestPoint2D(), mrpt::math::KDTreeCapable< CFeatureList >::kdTreeNClosestPoint2DIdx(), mrpt::math::KDTreeCapable< CFeatureList >::kdTreeRadiusSearch2D(), and mrpt::math::KDTreeCapable< CFeatureList >::kdTreeTwoClosestPoint2D().

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

TKDTreeDataHolder<3> mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::m_kdtree3d_data

mutableprivate

Definition at line 710 of file KDTreeCapable.h.

Referenced by mrpt::math::KDTreeCapable< CFeatureList >::kdTreeClosestPoint3D(), mrpt::math::KDTreeCapable< CFeatureList >::kdTreeNClosestPoint3D(), mrpt::math::KDTreeCapable< CFeatureList >::kdTreeNClosestPoint3DIdx(), mrpt::math::KDTreeCapable< CFeatureList >::kdTreeNClosestPoint3DWithIdx(), and mrpt::math::KDTreeCapable< CFeatureList >::kdTreeRadiusSearch3D().

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

bool mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::m_kdtree_is_uptodate

mutableprivate

whether the KD tree needs to be rebuilt or not.

Definition at line 712 of file KDTreeCapable.h.

Referenced by mrpt::math::KDTreeCapable< CFeatureList >::kdtree_mark_as_outdated().

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>

TKDTreeDataHolder mrpt::math::KDTreeCapable< Derived, num_t, metric_t >::m_kdtreeNd_data

mutableprivate

Definition at line 711 of file KDTreeCapable.h.

Detailed Description

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>> class mrpt::math::KDTreeCapable< Derived, num_t, metric_t >

Classes

Public Types

Public Member Functions

Public Attributes

Protected Member Functions

Private Member Functions

Private Attributes

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

template<class Derived, typename num_t = float, typename metric_t = nanoflann::L2_Simple_Adaptor<num_t,Derived>>
class mrpt::math::KDTreeCapable< Derived, num_t, metric_t >