autoware::perception::segmentation::euclidean_cluster::EuclideanCluster Class Reference

implementation of euclidean clustering for point cloud segmentation This clas implicitly projects points onto a 2D (x-y) plane, and segments according to euclidean distance. This can be thought of as a graph-based approach where points are vertices and edges are defined by euclidean distance The input to this should be nonground points pased through a voxel grid. More...

#include <euclidean_cluster.hpp>

Public Types
enum	Error : uint8_t { Error::NONE = 0U, Error::TOO_MANY_CLUSTERS }

Public Member Functions
	EuclideanCluster (const Config &cfg, const HashConfig &hash_cfg, const FilterConfig &filter_cfg)
	Constructor. More...
void	insert (const PointXYZIR &pt)
	Insert an individual point. More...
template<typename IT >
void	insert (const IT begin, const IT end)
	Multi-insert. More...
void	cluster (Clusters &clusters)
	Compute the clusters from the inserted points, where the final clusters object lives in another scope. More...
Error	get_error () const
	Gets last error, intended to be used with clustering with internal cluster result This is a separate function rather than using an exception because the main error mode is exceeding preallocated cluster capacity. However, throwing an exception would throw away perfectly valid information that is still usable in an error state. More...
const Config &	get_config () const
	Gets the internal configuration class, for use when it was inline generated. More...
const FilterConfig &	get_filter_config () const
	Gets internal configuration class for filters. More...
void	throw_stored_error () const
	Throw the stored error during clustering process. More...

Detailed Description

implementation of euclidean clustering for point cloud segmentation This clas implicitly projects points onto a 2D (x-y) plane, and segments according to euclidean distance. This can be thought of as a graph-based approach where points are vertices and edges are defined by euclidean distance The input to this should be nonground points pased through a voxel grid.

Member Enumeration Documentation

Error

enum autoware::perception::segmentation::euclidean_cluster::EuclideanCluster::Error : uint8_t

strong

Enumerator
NONE
TOO_MANY_CLUSTERS

Constructor & Destructor Documentation

EuclideanCluster()

autoware::perception::segmentation::euclidean_cluster::EuclideanCluster::EuclideanCluster	(	const Config &	cfg,
		const HashConfig &	hash_cfg,
		const FilterConfig &	filter_cfg
	)

Constructor.

Parameters

[in]	cfg	The configuration of the clustering algorithm, contains threshold function
[in]	hash_cfg	The configuration of the underlying spatial hash, controls the maximum number of points in a scene
[in]	filter_cfg	The configuration of the min/max size limit of the bounding boxes

Member Function Documentation

cluster()

void autoware::perception::segmentation::euclidean_cluster::EuclideanCluster::cluster

(

Clusters &

clusters

)

Compute the clusters from the inserted points, where the final clusters object lives in another scope.

Parameters

[in,out]

clusters

The clusters object

get_config()

const Config & autoware::perception::segmentation::euclidean_cluster::EuclideanCluster::get_config

(

)

const

Gets the internal configuration class, for use when it was inline generated.

Returns

Internal configuration class

get_error()

EuclideanCluster::Error autoware::perception::segmentation::euclidean_cluster::EuclideanCluster::get_error

(

)

const

Gets last error, intended to be used with clustering with internal cluster result This is a separate function rather than using an exception because the main error mode is exceeding preallocated cluster capacity. However, throwing an exception would throw away perfectly valid information that is still usable in an error state.

get_filter_config()

const FilterConfig & autoware::perception::segmentation::euclidean_cluster::EuclideanCluster::get_filter_config

(

)

const

Gets internal configuration class for filters.

Returns

Internal FilterConfiguration class

insert() [1/2]

template<typename IT >

void autoware::perception::segmentation::euclidean_cluster::EuclideanCluster::insert ( const IT  begin, const IT  end  )

inline

Multi-insert.

Parameters

[in]	begin	Iterator pointing to to the first point to insert
[in]	end	Iterator pointing to one past the last point to insert

Exceptions

std::length_error

If the underlying spatial hash is full

Template Parameters

IT	The type of the iterator

insert() [2/2]

void autoware::perception::segmentation::euclidean_cluster::EuclideanCluster::insert

(

const PointXYZIR &

pt

)

Insert an individual point.

Parameters

[in]

pt

The point to insert

Exceptions

std::length_error

If the underlying spatial hash is full

throw_stored_error()

void autoware::perception::segmentation::euclidean_cluster::EuclideanCluster::throw_stored_error

(

)

const

Throw the stored error during clustering process.

Exceptions

std::runtime_error

If the maximum number of clusters may have been exceeded

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The documentation for this class was generated from the following files:

• euclidean_cluster.hpp
• euclidean_cluster.cpp