how to extract features from 350 clusters | extractEigenFeatures

3 次查看（过去 30 天）
Pavan Kumar B N
Pavan Kumar B N 2021-10-10
编辑： Pavan Kumar B N 2021-10-11
Hello All,
I have around 350 clusters and I am trying to retain only matching clusters and remove unmatched ones. How to use this example in this case https://www.mathworks.com/help/lidar/ref/pcshowmatchedfeatures.html
  2 个评论
Venkata Ram Prasad Vanguri
Venkata Ram Prasad Vanguri 2021-10-11
Hi,
We can extract matched cluster segments and corresponding features from the matched index pairs as below:
matchedSegments1 = segments1(indexPairs(:,1));
matchedSegments2 = segments2(indexPairs(:,2));
matchedFeatures1 = eigFeatures1(indexPairs(:,1));
matchedFeatures2 = eigFeatures2(indexPairs(:,2));
Pavan Kumar B N
Pavan Kumar B N 2021-10-11
编辑：Pavan Kumar B N 2021-10-11
@Venkata Ram Prasad Vanguri Thank you so much for your response.
I tried as you suggested on pcap data. When I run the following code it is generating 50 figures of matched segments. I tired visualize these results using pcplayer. But it is giving a error like "Too many input arguments". Could you please help on how to visulize these matching features using pcplayer.?
Eagerly looking forward for your kind help.
veloReader = velodyneFileReader('test.pcap', 'VLP16');
for i = 1:50
ptCloud1 = veloReader.readFrame(i);
ptCloud2 = veloReader.readFrame(i+1);
%remove the ground plane from the scans
maxDistance = 1;
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 cloud with 10 points per cluster
minDistance = 2;
minPoints = 10;
lables1 = pcsegdist(ptCloud1, minDistance, 'NumClusterPoints', minPoints);
lables2 = pcsegdist(ptCloud2, minDistance, 'NumClusterPoints', minPoints);
[eigFeatures1, segments1] = extractEigenFeatures(ptCloud1,lables1);
[eigFeatures2, segments2] = extractEigenFeatures(ptCloud2,lables2);
%create matrices of the features and centroids extracted from each point
%cloud
features1 = vertcat(eigFeatures1.Feature);
features2 = vertcat(eigFeatures2.Feature);
centroids1 = vertcat(eigFeatures1.Centroid);
centroids2 = vertcat(eigFeatures2.Centroid);
%find future matches
indexPairs = pcmatchfeatures(features1,features2, pointCloud(centroids1),pointCloud(centroids2),'MatchThreshold',0.1,'RejectRatio',0.9);
matchedSegments1 = segments1(indexPairs(:,1));
matchedSegments2 = segments2(indexPairs(:,2));
matchedFeatures1 = eigFeatures1(indexPairs(:,1));
matchedFeatures2 = eigFeatures2(indexPairs(:,2));
figure
pcshowMatchedFeatures(matchedSegments1,matchedSegments2,matchedFeatures1,matchedFeatures2)
title('Matched Segments')
xlimits = [-35 118];
ylimits = [-55 75];
zlimits = [-10 10];
player = pcplayer(xlimits,ylimits,zlimits);
%while(hasFrame(ptCloudPlayer))
% ptCloudObj = readFrame(ptCloudPlayer);
view(player,matchedSegments2.Location,matchedSegments2.Intensity);
%end
end

请先登录，再进行评论。

请先登录，再回答此问题。

回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Preprocessing 的更多信息

标签

产品


版本

R2021a

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by