pcsegdist
Segment point cloud into clusters based on Euclidean distance
Syntax
Description
segments a point cloud into clusters, with a minimum Euclidean distance of
labels
= pcsegdist(ptCloud
,minDistance
)minDistance
between points from different clusters.
pcsegdist
assigns an integer cluster label to each point in
the point cloud, and returns the labels
of all points.
[
also returns the number of clusters.labels
,numClusters
] = pcsegdist(ptCloud
,minDistance
)
[___] = pcsegdist(___,
sets properties using name-value arguments. For example, Name=Value
)labels =
pcsegdist(
sets the minimum and maximum number of points in each cluster to
ptCloud
,minDistance
,NumClusterPoints=[1,Inf])[1,Inf]
.
Examples
Cluster Point Cloud Based on Euclidean Distance
Create two concentric spheres and combine them.
[X,Y,Z] = sphere(100);
loc1 = [X(:),Y(:),Z(:)];
loc2 = 2*loc1;
ptCloud = pointCloud([loc1;loc2]);
pcshow(ptCloud)
title('Point Cloud')
Set the minimum Euclidean distance between clusters.
minDistance = 0.5;
Segment the point cloud.
[labels,numClusters] = pcsegdist(ptCloud,minDistance);
Plot the labeled results. The points are grouped into two clusters.
pcshow(ptCloud.Location,labels)
colormap(hsv(numClusters))
title('Point Cloud Clusters')
Cluster Lidar Point Cloud Based on Euclidean Distance
Load an organized lidar point cloud to the workspace.
ld = load('drivingLidarPoints.mat');
Detect the ground plane. Distance is measured in meters.
maxDistance = 0.9; referenceVector = [0 0 1]; [~,inliers,outliers] = pcfitplane(ld.ptCloud,maxDistance,referenceVector);
Remove the ground plane points.
ptCloudWithoutGround = select(ld.ptCloud,outliers);
Cluster the point cloud with a minimum of 10 points per cluster using the exhaustive method.
minDistance = 2; minPoints = 10; [labels,numClusters] = pcsegdist(ptCloudWithoutGround,minDistance,Method="exhaustive",... NumClusterPoints=minPoints);
Remove the points with a label value of 0
.
idxValidPoints = find(labels); labelColorIndex = labels(idxValidPoints); segmentedPtCloud = select(ptCloudWithoutGround,idxValidPoints);
Plot the labeled results.
figure
colormap(hsv(numClusters))
pcshow(segmentedPtCloud.Location,labelColorIndex)
title('Point Cloud Clusters')
Input Arguments
ptCloud
— Point cloud
pointCloud
object
Point cloud, specified as a pointCloud
object.
minDistance
— Minimum Euclidean distance
positive scalar
Minimum Euclidean distance between points from two different clusters, specified as a positive scalar.
Data Types: single
| double
Name-Value Arguments
Example: ParallelNeighborSearch
=false
sets
the ParallelNeighborSearch
to
false
.
Specify optional pairs
of arguments as Name1=Value1,...,NameN=ValueN
, where
Name
is the argument name and Value
is the
corresponding value. Name-value arguments must appear after other arguments, but the
order of the pairs does not matter.
NumClusterPoints
— Minimum and maximum number of points in each cluster
[1,Inf]
(default) | 2-element vector | scalar
Minimum and maximum number of points in each cluster, specified as a
scalar or a 2-element vector of the form
[minPoints,maxPoints]. When
you specify NumClusterPoints
as a scalar, the
maximum number of points in the cluster is unrestricted. The function
sets labels
to 0
when clusters
are outside of the specified range.
Method
— Method to segment the point cloud
"approximate"
(default) | "exhaustive"
Method to segment the point cloud, specified as
"approximate"
or "exhaustive"
.
Set Method
to "exhaustive"
to
ensure that no points outside of each cluster are less than
minDistance
away. This approach uses the
density-based spatial clustering of applications with noise (DBSCAN)
algorithm. Set Method
to
"approximate"
for faster segmentation, but at the
expense of accuracy.
ParallelNeighborSearch
— Parallel neighbor search to segment point cloud data
false
(default) | true
Parallel neighbor search to segment point cloud data, specified as
true
or false
. Set this
property to true
when you expect there to be
approximately 50 clusters or more with fewer than 100 points per
cluster.
A parallel neighbor search can improve segmentation speed for some
datasets. Improved speed depends on the dataset and the value of the
minDistance
input. This argument is not
supported when you set the Method
name-value
argument to "exhaustive"
.
Output Arguments
labels
— Cluster labels
M-by-1 vector | M-by-N matrix
Cluster labels, returned as one of the following.
If the point cloud,
ptCloud
, stores point locations as an unorganized M-by-3 matrix, thenlabels
is an M-by-1 vector.If the point cloud,
ptCloud
, stores point locations as an organized M-by-N-by-3 matrix, thenlabels
is an M-by-N matrix.
Each point in the point cloud has a cluster label, specified
by the corresponding element in labels
. The value of
each label is an integer from 0
to the number of clusters
of valid points, numClusters
. The value
0
is reserved for invalid points, such as points with
Inf
or NaN
coordinates.
Data Types: uint32
numClusters
— Number of clusters
positive integer
Number of clusters, returned as a positive integer. The number of clusters
excludes the label value 0
, which is reserved for invalid
points. The function returns numClusters
as a
single
data type when the value of the
Location
property of the ptCloud
object is single
. Otherwise, the function returns the
value as a double
data type.
Data Types: single
| double
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
The "exhaustive"
segmentation method, which can be set by using
the Method
name-value argument, does not support C/C++ code
generation.
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
Usage notes and limitations:
The generated CUDA® code segments the point cloud into clusters by using a combination of algorithms described in [1] and [2]. The output from the generated code can differ slightly with results from MATLAB® simulation.
The
NumClusterPoints
name-value argument is not supported for GPU code generation.The
"exhaustive"
segmentation method, which can be set by using theMethod
name-value argument, does not support GPU code generation.
References
[1] Andrade, Guilherme, Gabriel Ramos, Daniel Madeira, Rafael Sachetto, Renato Ferreira, and Leonardo Rocha. “G-DBSCAN: A GPU Accelerated Algorithm for Density-Based Clustering.” Procedia Computer Science 18 (2013): 369–78. https://doi.org/10.1016/j.procs.2013.05.200.
[2] Kalentev, Oleksandr, Abha Rai, Stefan Kemnitz, and Ralf Schneider. “Connected Component Labeling on a 2D Grid Using CUDA.” Journal of Parallel and Distributed Computing 71, no. 4 (April 2011): 615–20. https://doi.org/10.1016/j.jpdc.2010.10.012.
Version History
Introduced in R2018aR2024b: New Method
name-value argument for segmenting point cloud
Configure the Method
name-value argument to choose between
"exhaustive"
or "approximate"
segmentation methods. The ParallelNeighborhood
name-value
argument does not support the "exhaustive"
method.
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