Divided interest of space (ROI) into continuous cubes

Hello

Dear Friends

I have a Lider result consist of X, Y, Z in matlab file and for each cell/frame I want to divide the region of interest (ROI) space (X: -150m to 150m; Y:-100m to 100m; Z: -15m to 15m) into continuous cubes (0.1m cube length) and calculate the point density in each cube.

clc

close all

fileDir = 'D:\Examps'; % the target data directory

%outfile = 'D:\XYZ_14000.csv';

fid=fopen(outfile,'w');

d=dir(fullfile(fileDir,'*.csv')); % dir() struct of *.csv files in directory

N=numel(d); % total number files found

for i = 1:N % iterate over them...

XYZ=readmatrix(fullfile(fileDir,d(i).name),'NumHeaderLines',1);

data=XYZ(:,1:3); % select the three columns only

fmt=[repmat('%f,',1,numel(data)-1) '%f\n'];

fprintf(fid,fmt,data);

X=XYZ(1:4,1);

Y=XYZ(1:4,2);

Z=XYZ(1:4,3);

% if -100<=X<=100

% X=Xlimit

%else X'='0

end

fid=fclose(fid);

Thanks

Answer 1

[Image Analyst]

Description

example

labels = segmentLidarData(ptCloud,distThreshold) segments organized 3-D range data ptCloud into clusters. The function assigns an integer cluster label to each point in the point cloud and returns the cluster label of all points in labels.

The function groups two neighboring points into the same cluster if their Euclidean distance is less than distThreshold or if the angle between the sensor and two neighboring points is at least 5 degrees.

[Ehsan Shooshtari]

Hello Dear

Thanks for response

First: I have a threshold for X, Y, Z and labels = segmentLidarData(ptCloud,distThreshold) just accept a simple threshold 5 without mention X,Y,Z.

Second: I added below script and still doesn't show any things.(like my script is not mach by the rest)

% Specify limits of point cloud display

xlimits = [-150 150]; % meters

ylimits = [-100 100];

zlimits = [-15 15];

pc = pointCloud(cat(3,X,Y,Z));

figure

pcshow(pc.Location,'r')

title('Unclustered Point Cloud')

Regards

[Ehsan Shooshtari]

Hello

Dear Friends

I need some clue to handle my rest, consist of

1- calculate the point density in each cube.

2- background filtering.

clc

close all

fileDir = 'D:\Examps2'; % the target data directory

outfile = 'D:\100Frames.las';

fid=fopen(outfile,'w');

d=dir(fullfile(fileDir,'*.csv')); % dir() struct of *.csv files in directory

N=numel(d); % total number files found

for i = 1:N % iterate over them...

XYZ=readmatrix(fullfile(fileDir,d(i).name),'NumHeaderLines',1);

data=XYZ(:,1:3); % select the three columns only

fmt=[repmat('%f,',1,numel(data)-1) '%f\n'];

fprintf(fid,fmt,data);

[m,n]=size(data);

X=XYZ(1:m,1);

Y=XYZ(1:m,2);

Z=XYZ(1:m,3);

end

fid=fclose(fid);

figure

pc = pointCloud(cat(3,X,Y,Z));

pcshow(pc.Location,'r')

title('Clustered Point Cloud')

xlim([-100 100]); % meters

ylim([-100 100]);

zlim([-5 5]);

hold on

figure

minDistance = 0.1;

[labels,numClusters] = pcsegdist(ptCloud,minDistance);

%Plot the labeled results. The points are grouped into two clusters.

pcshow(ptCloud.Location,labels)

colormap(hsv(numClusters))

%title('Point Cloud Clusters')

hold on

figure

pcshow(ptCloud)

xlim([-100 100]); % meters

ylim([-100 100]);

zlim([-5 5]);

title('Unclustered Point Cloud')

maxDistance = 0.3;

referenceVector = [0,0,1];

Thank you "Image Analyst" for answer half of my first question, I could not open his example due to privacy and Crome security warning, however, he/she was too busy and left my questions idle, so after almost 2 days waiting for the rest and inquiry, unfortunatly, I unaccepted his answer.

[Image Analyst]

Sorry - I've never used lidar data before. I suggest you call tech support.

[Ehsan Shooshtari]

Thanks for let me know, by the way this script is too simple for you, if you run it. Just Lider data exchange to the cordinator X,Y,Z and I want to focus on some special object with remove the background.