DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm implementation. More...

#include <dbscan.hpp>

Inheritance diagram for DBSCAN:

Collaboration diagram for DBSCAN:

Public Member Functions
	DBSCAN (int min_cluster_size, double neighbours_dist_threshold)
	Constructor for the DBSCAN clustering algorithm.

void	clustering (const sensor_msgs::msg::PointCloud2::SharedPtr &point_cloud, std::vector< Cluster > *clusters) const override
	Clusters the input point cloud into groups using the DBSCAN algorithm.

Private Attributes
int	min_cluster_size
	Minimum number of points required to form a cluster.

double	neighbours_dist_threshold
	Maximum distance between points to be considered as neighbors.

Detailed Description

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm implementation.

The DBSCAN class is a concrete implementation of the Clustering interface, using the DBSCAN algorithm to cluster point clouds based on density and distance thresholds.

Definition at line 14 of file dbscan.hpp.

Constructor & Destructor Documentation

◆ DBSCAN()

DBSCAN::DBSCAN	(	int	min_cluster_size,
		double	neighbours_dist_threshold
	)

Constructor for the DBSCAN clustering algorithm.

Parameters

min_cluster_size	Minimum number of points required to form a cluster.
neighbours_dist_threshold	Maximum distance between points to be considered as neighbors.

Definition at line 3 of file dbscan.cpp.

Member Function Documentation

◆ clustering()

void DBSCAN::clustering	(	const sensor_msgs::msg::PointCloud2::SharedPtr &	point_cloud,
		std::vector< Cluster > *	clusters
	)		const

overridevirtual

Clusters the input point cloud into groups using the DBSCAN algorithm.

This function implements the clustering method from the Clustering interface.

Parameters

point_cloud	A shared pointer to a point cloud of type pcl::PointCloud<pcl::PointXYZI>.
clusters	A pointer to a vector of shared pointers to point clouds to store the resulting clusters.

Implements Clustering.

Definition at line 6 of file dbscan.cpp.

Member Data Documentation

◆ min_cluster_size

int DBSCAN::min_cluster_size

private

Minimum number of points required to form a cluster.

Definition at line 37 of file dbscan.hpp.

◆ neighbours_dist_threshold

double DBSCAN::neighbours_dist_threshold

private

Maximum distance between points to be considered as neighbors.

Definition at line 38 of file dbscan.hpp.

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

src/perception/include/clustering/dbscan.hpp
src/perception/src/clustering/dbscan.cpp

Public Member Functions

Private Attributes