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extractEigenFeatures

Extract eigenvalue-based features from point cloud segments

Description

example

features = extractEigenFeatures(ptCloud,labels) extracts eigenvalue-based features from a point cloud using labels, labels, that correspond to the segmented point cloud.

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.

features = extractEigenFeatures(segmentsIn) returns eigenvalue-based features from the point cloud segments segmentsIn. Use this syntax to facilitate the selection of specific segments in a point cloud scan for local feature extraction.

[features,segmentsOut] = extractEigenFeatures(___) additionally returns the segments extracted from the input point cloud using any combination of arguments from previous syntaxes. Use this syntax to facilitate visualization of the segments.

Examples

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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);

Extract the eigenvalue-based features and the corresponding segments from the point cloud.

[features,segments] = extractEigenFeatures(ptCloud,labels)
features=17×1 object
  16x1 eigenFeature array with properties:

    Feature
    Centroid
      ⋮

segments=17×1 object
  16x1 pointCloud array with properties:

    Location
    Count
    XLimits
    YLimits
    ZLimits
    Color
    Normal
    Intensity
      ⋮

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')

Figure contains an axes. The axes with title Matched Segments contains 5 objects of type scatter, line.

Input Arguments

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Point cloud, specified as a pointCloud object.

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 labels.

You can use the pcsegdist or the segmentLidarData function to return labels.

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.

Output Arguments

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Eigenvalue-based features, returned as a vector of eigenFeature 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 vector.

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.

References

[1] 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