Main Content

lidarDetect

Report point cloud detections from all lidar sensor on platform

Description

example

pointCloulds = lidarDetect(plat,time) reports the point cloud detections from all monostaticLidarSensor object mounted on the ego platform, plat.

pointCloulds = lidarDetect(plat,time,includeSelf) allows you to choose whether the lidar sensor reports detections of the ego platform, plat, on which the sensors are mounted. Specify includeSelf as true or false.

[pointClouds,configs] = lidarDetect(___) also returns the configurations of the sensors, configs, at the current simulation time. You can use these output arguments with any of the previous input syntaxes.

[pointClouds,configs, clusters] = lidarDetect(___) also returns clusters, the cluster labels for each point in the point cloud.

Examples

collapse all

Create a tracking scenario.

sc = trackingScenario;
rng(2020)% for repeatable results

Add an ego platform to the tracking scenario.

ego = platform(sc);

Add a target platform to the tracking scenario.

target = platform(sc);

Define a simple waypoint trajectory for the target.

traj = waypointTrajectory("Waypoints",[1 1 1; 2 2 2],"TimeOfArrival",[0,1]);
target.Trajectory = traj;

Define a sphere mesh for the target.

target.Mesh = extendedObjectMesh("Sphere");
target.Dimensions = struct("Length",4,"Width",3,"Height",2,"OriginOffset",[0 0 0]);

Show the mesh of the target.

figure()
show(target.Mesh);
legend("Target Mesh")
xlabel('x (m)'); ylabel('y (m)'); zlabel('z (m)');

Create two lidar sensors with different range accuracy. Mount them on the ego platform.

sensor1 = monostaticLidarSensor(1,"RangeAccuracy",0.01);
sensor2 = monostaticLidarSensor(2,"RangeAccuracy",0.1);
ego.Sensors = {sensor1;sensor2};

Generate detections from the two lidar sensor using lidarDetect.

[pointClouds,configs,clusters] = lidarDetect(ego,0);

Visualize the results.

cloud1 = pointClouds{1};
cloud2 = pointClouds{2};
figure()
plot3(cloud1(:,1),cloud1(:,2),cloud1(:,3),'bo')
hold on
plot3(cloud2(:,1),cloud2(:,2),cloud2(:,3),'go')
legend('Sensor1','Sensor2')
xlabel('x (m)'); ylabel('y (m)'); zlabel('z (m)')

Input Arguments

collapse all

Ego platform, specified as a Platform object.

Current simulation time, specified as a positive scalar in seconds.

Data Types: double

Enable reporting ego platform's mesh detection in the output, specified as true or false.

Output Arguments

collapse all

Point cloud detections generated by the sensors, returned as a K-element cell array. K is the number of monostaticLidarSensor objects mounted on the platform, plat. Each cell element is an array representing the point cloud generated by the corresponding sensor. The dimension of the array is determined by the HasOrganizedOuput property of the sensor.

  • When this property is set as true, the cell element is returned an N-by-M-by-3 array of scalars, where N is the number of elevation channels, and M is the number of azimuth channels.

  • When this property is set as false, the cell element is returned as an P-by-3 matrix of scalars, where P is the product of the numbers of elevation and azimuth channels.

The coordinate frame in which the point cloud locations are reported is determined by the DetectionCoordinates property of the sensor.

Current sensor configurations, returned as a K-element array of structures. K is the number of monostaticLidarSensor objects mounted on the platform, plat. Each structure has these fields:

FieldDescription
SensorIndex

Unique sensor index, returned as a positive integer.

IsValidTime

Valid detection time, returned as true or false. IsValidTime is false when detection updates are requested between update intervals specified by the update rate.

IsScanDone

IsScanDone is true when the sensor has completed a scan.

FieldOfView

Field of view of the sensor, returned as a 2-by-2 matrix of positive real values. The first row elements are the lower and upper azimuth limits; the second row elements are the lower and upper elevation limits.

MeasurementParameters

Sensor measurement parameters, returned as an array of structures containing the coordinate frame transforms needed to transform positions and velocities in the top-level frame to the current sensor frame.

Data Types: struct

Cluster labels of points in the pointClouds output, returned as a K-element cell array. K is the number of monostaticLidarSensor objects mounted on the platform, plat. Each cell element is an array representing cluster labels of points in the point cloud generated by the corresponding sensor. The dimension of the array is determined by the HasOrganizedOuput of the sensor.

  • When this property is set as true, the cell element is returned as an N-by-M-by-2 array of scalars, where N is the number of elevation channels, and M is the number of azimuth channels. On the third dimension, the first element represents the PlatformID of the target generating the point whereas the second element represents the ClassID of the target.

  • When this property is set as false, the cell element is returned as a P-by-2 matrix of scalars, where P is the product of the numbers of elevation and azimuth channels. For each column of the matrix, the first element represents the PlatformID of the target generating the point whereas the second element represents the ClassID of the target.

Introduced in R2020b