Extract eigenvalue-based features from point cloud segments
Eigenvalue-based features characterize geometrical features of point cloud segments. These features can be used in simultaneous localization and mapping (SLAM) applications for loop closure detection and localization in a target map.
[___] = extractEigenFeatures(___,NormalizeEigenvalues=tf)
normalizes the eigenvalues prior to computing features, specified as
true when the next step is to use a classifier to assign a semantic
label to a 3-D point. Set
false when the next
step is to find matching features. The default value is
Compute Eigenvalue-Based Features from Normalized Eigenvalues
Load an organized lidar point cloud.
ld = load('drivingLidarPoints.mat'); ptCloud = ld.ptCloud;
Segment and remove the ground plane.
groundPtsIdx = segmentGroundFromLidarData(ptCloud,'ElevationAngleDelta',15); ptCloud = select(ptCloud,~groundPtsIdx,'OutputSize','full');
Cluster the remaining points with a minimum of 50 points per cluster.
distThreshold = 0.5; % in meters minPoints = 50; [labels,numClusters] = segmentLidarData(ptCloud,distThreshold,'NumClusterPoints',minPoints);
Compute eigenvalue-based features.
features = extractEigenFeatures(ptCloud,labels,'NormalizeEigenvalues',true)
features=17×1 object 16x1 eigenFeature array with properties: Feature Centroid ⋮
Match Eigenvalue-Based Features Between Point Clouds
Create a Velodyne PCAP file reader.
veloReader = velodyneFileReader('lidarData_ConstructionRoad.pcap','HDL32E');
Read the first and fourth scans from the file.
ptCloud1 = readFrame(veloReader,1); ptCloud2 = readFrame(veloReader,4);
Remove the ground plane from the scans.
maxDistance = 1; % in meters referenceVector = [0 0 1]; [~,~,selectIdx] = pcfitplane(ptCloud1,maxDistance,referenceVector); ptCloud1 = select(ptCloud1,selectIdx,'OutputSize','full'); [~,~,selectIdx] = pcfitplane(ptCloud2,maxDistance,referenceVector); ptCloud2 = select(ptCloud2,selectIdx,'OutputSize','full');
Cluster the point clouds with a minimum of 10 points per cluster.
minDistance = 2; % in meters minPoints = 10; labels1 = pcsegdist(ptCloud1,minDistance,'NumClusterPoints',minPoints); labels2 = pcsegdist(ptCloud2,minDistance,'NumClusterPoints',minPoints);
Extract eigen-value features and the corresponding segments from each point cloud.
[eigFeatures1,segments1] = extractEigenFeatures(ptCloud1,labels1); [eigFeatures2,segments2] = extractEigenFeatures(ptCloud2,labels2);
Create matrices of the features and centroids extracted from each point cloud, for matching.
features1 = vertcat(eigFeatures1.Feature); features2 = vertcat(eigFeatures2.Feature); centroids1 = vertcat(eigFeatures1.Centroid); centroids2 = vertcat(eigFeatures2.Centroid);
Find putative feature matches.
indexPairs = pcmatchfeatures(features1,features2, ... pointCloud(centroids1),pointCloud(centroids2));
Get the matched segments and features for visualization.
matchedSegments1 = segments1(indexPairs(:,1)); matchedSegments2 = segments2(indexPairs(:,2)); matchedFeatures1 = eigFeatures1(indexPairs(:,1)); matchedFeatures2 = eigFeatures2(indexPairs(:,2));
Visualize the matches.
figure pcshowMatchedFeatures(matchedSegments1,matchedSegments2,matchedFeatures1,matchedFeatures2) title('Matched Segments')
labels — Cluster labels
M-element vector of numeric values | M-by-N matrix of numeric values
Cluster labels, specified as an M-element vector of numeric
values for unorganized point clouds or an M-by-N
matrix of numeric values for organized point clouds. The labels correspond to the
results of segmenting the input point cloud. Each point in the point cloud has a cluster
label, specified by the corresponding element in
segmentsIn — Point cloud segments
Point cloud segments, specified as a vector of
pointCloud objects. Each point cloud segment in the input must have a
minimum of two points for feature extraction. No features or segments are returned for
input segments with only one point.
features — Eigenvalue-based features
Eigenvalue-based features, returned as a vector of
objects. When you extract features from a labeled point cloud input, each element in
this vector contains the features extracted from the corresponding cluster of labeled
points. When you extract features from a segments input, each element in this vector
contains the features extracted from the corresponding element in the segments
segmentsOut — Segments extracted from point cloud
Segments extracted from the point cloud, specified as a vector of
pointCloud objects. The length of the segments vector corresponds to the
number of nonzero, unique labels.
 Weinmann, M., B. Jutzi, and C. Mallet. “Semantic 3D Scene Interpretation: A Framework Combining Optimal Neighborhood Size Selection with Relevant Features.” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II–3 (August 7, 2014): 181–88. https://doi.org/10.5194/isprsannals-II-3-181-2014.
Introduced in R2021a