Segment organized 3-D range data into clusters
segments organized 3-D range data
labels = segmentLidarData(
ptCloud into clusters. The
function assigns an integer cluster label to each point in the point cloud and
returns the cluster label of all points in
The function groups two neighboring points into the same cluster if their
Euclidean distance is less than
distThreshold or if the angle
between the sensor and two neighboring points is at least 5 degrees.
Create organized synthetic lidar data containing two objects.
ldr = zeros(5,100); ldr(:,1:50) = 10; ldr(:,51:end) = 20; pitch = linspace(-18,18,5); pitch = repmat(pitch',1,100); yaw = linspace(-90,90,100); yaw = repmat(yaw,5,1);
Convert to Cartesian coordinates.
X = ldr .* cosd(pitch) .* sind(yaw); Y = ldr .* cosd(pitch) .* cosd(yaw); Z = ldr .* sind(pitch); pc = pointCloud(cat(3,X,Y,Z)); figure pcshow(pc.Location,'r') title('Unclustered Point Cloud')
Set the threshold.
distThreshold = 5;
Segment the lidar data.
labels = segmentLidarData(pc,distThreshold);
Plot the results.
figure hold on title('Segmented Clusters') pc1 = select(pc,find(labels == 1)); pcshow(pc1.Location,'g') pc2 = select(pc,find(labels == 2)); pcshow(pc2.Location,'y')
Set up the PCAP file reader.
veloReader = velodyneFileReader('lidarData_ConstructionRoad.pcap','HDL32E');
Wait for 0.3 seconds from the beginning of the file, then read the point cloud from the next frame.
veloReader.CurrentTime = veloReader.StartTime + seconds(0.3); ptCloud = readFrame(veloReader);
Segment and remove the ground plane.
groundPtsIdx = segmentGroundFromLidarData(ptCloud); ptCloudWithoutGround = select(ptCloud,~groundPtsIdx,'OutputSize','full');
Cluster the remaining points. Distance is in meters.
distThreshold = 0.5; [labels,numClusters] = segmentLidarData(ptCloudWithoutGround,distThreshold);
Add an additional label for the ground plane.
numClusters = numClusters+1; labels(groundPtsIdx) = numClusters;
Plot the labeled results. Display the ground plane in black.
labelColorIndex = labels+1; pcshow(ptCloud.Location,labelColorIndex) colormap([hsv(numClusters);[0 0 0]]) title('Point Cloud Clusters')
ptCloud— Point cloud
distThreshold— Distance threshold
Distance threshold in world units, specified as a nonnegative scalar.
Adjacent points are grouped into the same cluster if the distance between
them is less than the distance threshold. To reduce the number of output
clusters, increase the value of
angleThreshold— Angle threshold
Angle threshold, in degrees, specified as a scalar in the range [0, 180].
To reduce the number of output clusters, decrease the value of
angleThreshold. The function groups adjacent points
into the same cluster if the angle formed by the sensor and the points is
greater than the angle threshold. For example, in the figure, the function
groups points A and B into the same cluster if the angle formed by the
sensor, point A, and point B, is greater than
angleThreshold. For more details, see Algorithms.
labels— Cluster labels
Cluster labels of all points in the point cloud
ptCloud, returned as an
M-by-N matrix of integers. Each
valid point in
ptCloud belongs to a cluster. All points
in a cluster are assigned the same integer cluster label, ranging from
points, such as points with
coordinates, are assigned the label
numClusters— Number of clusters
Number of clusters of valid points, returned as a nonnegative integer. The number of clusters does not include the cluster corresponding to invalid points.
segmentLidarData function uses distance and angle thresholds to
cluster neighboring points. The function groups two neighboring points into the same
cluster if their Euclidean distance is less than the input
distThreshold or if the angle between the sensor and
neighboring points is greater than or equal to the input
angleThreshold. If you do not specify
angleThreshold, the function sets this angle to
For example, suppose
angleThreshold is set to
90. Because angles α and β in the figure are both greater than
the specified threshold of 90 degrees, the function groups points A, B, and C into the
same cluster. Because angle σ is less than the 90-degree threshold, the function groups
point D into a separate cluster. Each angle the function uses for clustering is formed
by the line from a point to the sensor and the line from that same point to the
 Bogoslavskyi, I. “Efficient Online Segmentation for Sparse 3D Laser Scans.” Journal of Photogrammetry, Remote Sensing and Geoinformation Science. Vol. 85, Issue 1, 2017, pp. 41–52.