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：
- 读取velodyne数据包pcap文件内的点云数据
- 使用pcdownsample函数对点云数据进行体素化采样，减少点云数量
- 使用find函数对点云进行筛选
- 使用pcdnoise去除点云内的噪声
- 使用pcsegdist进行欧式聚类
- 使用delaunayTriangulation进行三角剖分
- 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
% 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);
frameID
tic
lidarLo = [3.5 0 1.1 0 0 0];
% 取出XYZ
xTemp = 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);
else
pcRemainObj = pcObj_downSample;
end
else
pcRemainObj = pcObj_downSample;
end
cowPCRemain = size(pcRemainObj.Location)*[1;0];
if cowPCRemain>pcOutNum
cowPCRemain = pcOutNum;
end
objPointVeh(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);
clf
axis([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');
end
end
hold off
objVehPoint = objPointVeh;
%%
pcObjOut = pointCloud(objVehPoint(:,1:3));
pcObjOut.Intensity = objVehPoint(:,4);
frameID = frameID+1;
toc
view(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