基于matlab点云工具箱对点云进行处理三：对点云进行欧式聚类，使用三角剖分处理后获取点云簇的外接凸多边形

本文主要是介绍基于matlab点云工具箱对点云进行处理三：对点云进行欧式聚类，使用三角剖分处理后获取点云簇的外接凸多边形，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

基于matlab点云工具箱对点云进行处理三：对点云进行欧式聚类，使用三角剖分处理后获取点云簇的外接凸多边形

步骤：

1. 读取velodyne数据包pcap文件内的点云数据
2. 使用pcdownsample函数对点云数据进行体素化采样，减少点云数量
3. 使用find函数对点云进行筛选
4. 使用pcdnoise去除点云内的噪声
5. 使用pcsegdist进行欧式聚类
6. 使用delaunayTriangulation进行三角剖分
7. 使用convexHull获得外接凸包的顶点ID

相关程序在这里https://download.csdn.net/download/rmrgjxeivt/59557139

存在的问题：
弯曲道路的护栏（例如匝道）被识别为半圆形状，误识别区域巨大

基于matlab点云工具箱对点云进行处理一：去除地面，保留剩下的点https://blog.csdn.net/rmrgjxeivt/article/details/121830344
基于matlab点云工具箱对点云进行处理二：对点云进行欧式聚类，获得聚类后点云簇的外接矩形https://blog.csdn.net/rmrgjxeivt/article/details/121830919
基于matlab点云工具箱对点云进行处理三：对点云进行欧式聚类，使用三角剖分处理后获取点云簇的外接凸多边形https://blog.csdn.net/rmrgjxeivt/article/details/121831507
基于matlab点云工具箱对点云进行处理四：对点云进行欧式聚类，并获得包围点云簇的外接凹多边形https://blog.csdn.net/rmrgjxeivt/article/details/121831934

% 读取激光的PCAP文件
% 筛选感兴趣区域
% 播放筛选后的点云veloReader = velodyneFileReader('2021-11-23-12-49-43_Velodyne-HDL-32-Data.pcap','VLP32c');%% 设置感兴趣区域vehPara.length = 5.5;
vehPara.width = 2.2;
vehPara.d = 2.3; % 轴距
vehPara.rearOverhang = 1; % 前悬
vehPara.rearOverhang = 1; % 后悬
vehPara.CG2Rear = 1.45; % 质心到后轴insRegion = [-20 50 -10 10 0 2]; % 感兴趣区域[minX maxX minY maxY]
groundRegion = [-1, 0.2]; % 地面区域，z轴方向xLimits = [insRegion(1), insRegion(2)];
yLimits = [insRegion(3), insRegion(4)];
zLimits = [insRegion(5), insRegion(6)]; % 原点在后轴中心，因此此处相对于轮芯高度player = pcplayer(xLimits,yLimits,zLimits);xlabel(player.Axes,'X (m)');
ylabel(player.Axes,'Y (m)');
zlabel(player.Axes,'Z (m)');veloReader.CurrentTime = veloReader.StartTime + seconds(0.3);disp(['frame数量',num2str(veloReader.NumberOfFrames)])pause(2)frameID = 1000;while(hasFrame(veloReader) && player.isOpen() && (veloReader.CurrentTime < veloReader.EndTime))ptCloudObj = readFrame(veloReader,frameID);frameIDticlidarLo = [3.5 0 1.1 0 0 0];% 取出XYZxTemp = ptCloudObj.Location(:,:,2)+lidarLo(1);yTemp = -ptCloudObj.Location(:,:,1)+lidarLo(2);zTemp = ptCloudObj.Location(:,:,3)+lidarLo(3);pc = [xTemp(:) yTemp(:) zTemp(:) single(ptCloudObj.Intensity(:))];% max(pc(:,1))% min(pc(:,1))% max(pc(:,2))% 对地面的点进行范围筛选zMin = groundRegion(1);zMax = groundRegion(2);pcObj = pointCloud(pc(:,1:3));pcObj.Intensity = pc(:,4);pcOutNum = 30000; % 输出的点云数量objPointVeh = zeros(pcOutNum,4,'single');objPointVeh(:,1) = single(insRegion(2));objPointVeh(:,2) = single(insRegion(4));objPointVeh(:,3) = single(insRegion(6));objPointVeh(:,4) = single(0);% tic%% 降低点云密度 coder会报错gridStep = 0.05;pcObj_downSample = pcdownsample(pcObj,'gridAverage',gridStep); % 降低点云密度% maxNumPoints = 6;% pcObj_downSample = pcdownsample(pcObj,'nonuniformGridSample',maxNumPoints);%     percentage = 0.3;%     pcObj_downSample = pcdownsample(pcObj,'random',percentage);%% 筛选感兴趣区域(单位米),并排除车身内部的点云xLimits = [insRegion(1), insRegion(2)];yLimits = [insRegion(3), insRegion(4)];zLimits = [insRegion(5), insRegion(6)]; % 原点在后轴中心，因此此处相对于轮芯高度indices = find((pcObj_downSample.Location(:, 2) >= yLimits(1) ...& pcObj_downSample.Location(:,2) <=  yLimits(2) ...& pcObj_downSample.Location(:,1) >=  xLimits(1) ...& pcObj_downSample.Location(:,1) <=  xLimits(2) ...& pcObj_downSample.Location(:,3) <=  zLimits(2) ...& pcObj_downSample.Location(:,3) >=  zLimits(1) ...& ~(pcObj_downSample.Location(:,1)<(vehPara.length-vehPara.rearOverhang) ...& pcObj_downSample.Location(:,1)>(-vehPara.rearOverhang) ...& pcObj_downSample.Location(:,2)<vehPara.width/2 ...& pcObj_downSample.Location(:,2)>-vehPara.width/2)));% 设置感兴趣的点云区域if ~isempty(indices)pcObj_downSample = select(pcObj_downSample,indices);%% 去除噪声[pcObj_downSample,inlierIndices,~] = pcdenoise(pcObj_downSample);pcID_noNoise = 1:1:pcObj_downSample.Count;if ~isempty(inlierIndices)outlierIndices = [];if ~isempty(outlierIndices) % 非空才输出pcRemainObj = select(pcObj_downSample,pcID_out);elsepcRemainObj = pcObj_downSample;endelsepcRemainObj = pcObj_downSample;endcowPCRemain = size(pcRemainObj.Location)*[1;0];if cowPCRemain>pcOutNumcowPCRemain = pcOutNum;endobjPointVeh(1:cowPCRemain,:) = [pcRemainObj.Location pcRemainObj.Intensity];end% end% figure(2)% % pcshow(plane1)% pcshow(pcPlanel)% title('First Plane')% cowPCRemain = length(pcObj.Location(:,1));% pcRemain(1:cowPCRemain,:) = pcObj.Location;% figure(3)% % pcshow(plane1)% pcshow(pcRemain)% title('remainPtCloud')%% 欧式聚类% 最小聚类欧式距离minDist = 0.5;% 执行欧式聚类分割[labels,numClusters] = pcsegdist(pcRemainObj,minDist);% 显示分割结果hsvColorMap = hsv(numClusters);hsvColorMap_H = hsvColorMap(:,1);hsvColorMap_S = hsvColorMap(:,2);hsvColorMap_V = hsvColorMap(:,3);%     view(player,pcRemainObj.Location,[hsvColorMap_H(labels) hsvColorMap_S(labels) hsvColorMap_V(labels)]);%     pcshow(pcRemainObj.Location,labels);%     colormap(hsv(numClusters));% 遍历所有聚类结果figure(5);clfaxis([insRegion(1) insRegion(2) insRegion(3) insRegion(4)])title('欧式聚类分割');xlabel('X(m)');ylabel('Y(m)');zlabel('Z(m)');hold on;for i = 1:1:numClusters%% 进行多边形框计算pcClusterObjTemp = select(pcRemainObj,find(labels == i));% 求解获得凸多边形进行多边形框计算if length(pcClusterObjTemp.Location(:,1))>=3 % triPart = delaunayTriangulation(double(pcClusterObjTemp.Location(:,1)), ...double(pcClusterObjTemp.Location(:,2)));hull = convexHull(triPart);plot(pcClusterObjTemp.Location(hull,1), pcClusterObjTemp.Location(hull,2), 'r');endendhold offobjVehPoint = objPointVeh;%%pcObjOut =   pointCloud(objVehPoint(:,1:3));pcObjOut.Intensity = objVehPoint(:,4);frameID = frameID+1;tocview(player,pcObjOut);%     figure(4)%     pcshow(pcObjOut.Location)%     xlabel('X(m)');%     ylabel('Y(m)');%     zlabel('Z(m)');%     axis([insRegion(1) insRegion(2) insRegion(3) insRegion(4)])pause(0.02);end

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