lidarSensor

Simulate lidar sensor readings

Since R2022a

Description

The lidarSensor System object™ simulates a lidar sensor mounted on an ego vehicle and outputs point cloud data for a given scene. The generated data is with respect to the ego vehicle coordinate system based on the sensor pose and the actors present in the scene. You can use the drivingScenario (Automated Driving Toolbox) object to create a scenario containing actors and trajectories, then generate the point cloud data for the scenario by using the lidarSensor object.

You can also use the lidarSensor object with vehicle actors in RoadRunner Scenario simulation. First you must create a SensorSimulation (Automated Driving Toolbox) object to interface sensors with RoadRunner Scenario and then register the lidar as a sensor model using the addSensors (Automated Driving Toolbox) object function before simulation. For more information, see Add Lidar Sensor Model with Simulated Weather Effects to RoadRunner Scenario. (since R2024a)

To simulate lidar sensor using this object:

Create the lidarSensor object and set its properties.
Call the object with arguments, as if it were a function.

To learn more about how System objects work, see What Are System Objects?

Creation

Syntax

lidar = lidarSensor

lidar = lidarSensor(Name=Value)

Description

lidar = lidarSensor creates a lidarSensor object with default property values. You can use this object to generate lidar point cloud data for a given 3-D environment.

lidar = lidarSensor(Name=Value) sets the properties of the object using one or more name-value arguments. For example, lidarSensor(UpdateRate=0.2) creates a lidarSensor object that generates point cloud detections at every 0.2 seconds.

example

Properties

expand all

Unless otherwise indicated, properties are nontunable, which means you cannot change their values after calling the object. Objects lock when you call them, and the release function unlocks them.

If a property is tunable, you can change its value at any time.

For more information on changing property values, see System Design in MATLAB Using System Objects.

`SensorIndex` — Unique identifier for sensor
`1` (default) | positive integer

Unique identifier for the sensor, specified as a positive integer. In a multisensor system, this index distinguishes different sensors from one another.

`HostID` — `ActorID` of ego vehicle
`1` (default) | positive integer

ActorID of the ego vehicle, specified as a positive integer. The ego vehicle is the actor on which the sensor is mounted, and ActorID is the unique identifier for an actor.

`UpdateRate` — Time interval between consecutive sensor updates
`0.1` (default) | positive scalar

Time interval between two consecutive sensor updates, specified as a positive scalar. The lidarSensor object generates new detections at the interval specified by this property. The value must be an integer multiple of the simulation time. Updates requested from the sensor in between the update intervals contain no detections. Units are in seconds.

Data Types: single | double

`Position` — Sensor center position
`[1.5 0 1.6]` (default) | [x y height]

Sensor center position, specified as a three-element vector of the form [x y height]. The values of x and y represent the location of the sensor with respect to the x- and y-axes of the ego vehicle coordinate system. height is the height of the sensor above the ground. The default value defines a lidar sensor mounted on the front edge of the roof of a sedan. Units are in meters.

Data Types: single | double

`Orientation` — Sensor orientation
`[0 0 0]` (default) | three-element vector of form [roll pitch yaw]

Sensor orientation, specified as a three-element vector of the form, [roll pitch yaw]. These values are with respect to the ego vehicle coordinate system. Units are in degrees.

roll — The roll angle is the angle of rotation around the front-to-back axis, which is the x-axis of the ego vehicle coordinate system. A positive roll angle corresponds to a clockwise rotation when looking in the positive direction of the x-axis.
pitch — The pitch angle is the angle of rotation around the side-to-side axis, which is the y-axis of the ego vehicle coordinate system. A positive roll angle corresponds to a clockwise rotation when looking in the positive direction of the y-axis.
yaw — The yaw angle is the angle of rotation around the vertical axis,which is the z-axis of the ego vehicle coordinate system. A positive roll angle corresponds to a clockwise rotation when looking in the positive direction of the z-axis. This rotation appears counter-clockwise when viewing the vehicle from above.

Data Types: single | double

`MaxRange` — Maximum detection range of sensor
`120` (default) | positive scalar

Maximum detection range of the lidar sensor, specified as a positive scalar in meters. The sensor cannot detect roads and actors beyond this range.

`RangeAccuracy` — Accuracy of sensor range measurement
`0.002` (default) | positive scalar

Accuracy of the sensor range measurement, specified as a positive scalar. Units are in meters.

Data Types: single | double

`HasNoise` — Point cloud data has added noise
`true` (default) | `false`

Point cloud data has added noise, specified as true or false. When set to true, the function adds random Gaussian noise to each point in the point cloud using the RangeAccuracy property as one standard deviation. Otherwise, the data has no noise.

Note

When you specify the FogVisibility and Rainrate properties while HasNoise value is set to true, the function adds noise points with high intensity values. The ActorID and the ClassID of these noise points in 0 in the clusters output.

Data Types: logical

`HasOrganizedOutput` — Output point cloud is organized
`true` (default) | `false`

Output point cloud is organized, specified as true or false.

true — The function returns an organized point cloud of the form M-by-N-by-3, where M is the number of elevation channels and N is the number of azimuth channels in the point cloud.
false — The function returns an unorganized point cloud of the form P-by-3, where P is the number of points in the point cloud.

Data Types: logical

`AzimuthResolution` — Azimuth resolution of lidar sensor
`0.16` (default) | positive scalar

Azimuth resolution of the lidar sensor, specified as a positive scalar in degrees.

Data Types: single | double

`ElevationResolution` — Elevation resolution of lidar sensor
`1.25` (default) | positive scalar

Elevation resolution of the lidar sensor, specified as a positive scalar in degrees.

Note

The System object ignores this property when you specify the ElevationAngles property.

Data Types: single | double

`AzimuthLimits` — Azimuth limits of lidar sensor
`[-180 180]` (default) | two-element vector

Azimuth limits of the lidar sensor, specified as a two-element vector of the form [min max]. The values must be in the range [-180, 180], max must be greater than min. Units are in degrees.

Data Types: single | double

`ElevationLimits` — Elevation limits of lidar sensor
`[-20 20]` (default) | two-element vector

Elevation limits of the lidar sensor, specified as a two-element vector of the form [min max]. The values must be in the range [-180, 180], max must be greater than min. Units are in degrees.

Note

The System object ignores this property when you specify the ElevationAngles property.

Data Types: single | double

`ElevationAngles` — Elevation angles of lidar sensor
`[]` (default) | N-element vector

Elevation angles of the lidar sensor, specified as an N-element vector. N is the number of elevation channels. The elements of the vector must be in the increasing order and their values must be in the range [-180, 180]. Units are in degrees.

Note

When you specify this property, the System object ignores the ElevationResolution and ElevationLimits properties.

Data Types: single | double

`ActorProfiles` — Physical characteristics of actors
`[]` (default) | structure | L-element array of structures

Physical characteristics of the actors in the scene, specified as a structure or as an L-element array of structures. L is the number of actors in the scene.

To generate an array of actor profile structures for your driving scenario, use the actorProfiles (Automated Driving Toolbox) function. You can also create these structures manually. This table shows the valid structure fields.

Field Description Value

ActorID Unique identifier for the actor. In a scene with multiple actors, this value distinguishes different actors from one another. Positive integer

ClassID

User-defined classification ID for the actor.

`ClassID`	Class Name
`1`	Car
`2`	Truck
`3`	Bicycle
`4`	Pedestrian
`5`	Jersey Barrier
`6`	Guardrail

Positive scalar

Length Length of the actor in meters. Positive scalar

Width Width of the actor in meters. Positive scalar

Height Height of the actor in meters. Positive scalar

OriginOffset

Offset of the rotational center of the actor from its geometric center. The rotational center, or origin, is located at the bottom center of the actor. For vehicles, the rotational center is the point on the ground beneath the center of the rear axle.

A three-element vector of the form [x y z]. Units are in meters.

MeshVertices Vertices of the actor in mesh representation. N-by-3 numeric matrix, where each row defines a vertex in 3-D space.

MeshFaces Face of the actor in mesh representation. M-by-3 integer matrix, where each row represents a triangle defined by vertex IDs, which are the row numbers of MeshVertices.

MeshTargetReflectances Material reflectance for each triangular face of the actor. M-by-1 numeric vector, where M is the number of triangle faces of the actor. Each value must be in the range [0, 1].

For more information about these structure fields, see the actor (Automated Driving Toolbox) and vehicle (Automated Driving Toolbox) functions.

`FogVisibility` — Visible distance in fog
`1000` (default) | positive scalar

Visible distance in fog, specified as a positive scalar, in meters. This value must not be greater than 1000. A higher value indicates a better visibility and a lower fog impact. The default value of 1000 indicates clear visibility, or no fog.

Note

When you specify both the FogVisibility and Rainrate properties, the function simulates only the foggy weather.

Data Types: single | double

`Rainrate` — Rate of rainfall
`0` (default) | positive scalar

Rate of rainfall, specified as a positive scalar in millimeters per hour. This value must be less than or equal to 200. Increasing this value increases the impact of the rain on the generated point cloud. The default value is 0, indicating no rainfall.

Note

When you specify both the FogVisibility and Rainrate properties, the function simulates only the foggy weather.

Data Types: single | double

`Model` — Name of sensor model
`'Custom'` (default) | character vector | string scalar

Since R2024a

Name of the sensor model, specified as a character vector or string scalar. For information on the valid sensor model names, see Supported Sensors.

This property sets default elevation angle values based on the specified sensor. To apply custom elevation angles, use the ElevationAngles property.

Data Types: char | string

`HasMotionDistortion` — Motion distortion flag
`false` or `0` (default) | `true` or `1`

Since R2024a

Motion distortion flag, specified as a logical 1 (true) or 0 (false).

true or logical 1 — Simulate motion distortion.
false or logical 0 — Do not simulate motion distortion.

Data Types: logical

`FiringTimes` — Firing times of lasers in lidar sensor
`[]` (default) | `duration` object | array of `duration` objects

Since R2024a

Firing times of the lasers in the lidar sensor, specified as a duration object or N-element array of duration objects. N is the number of elevation angles. Use a single duration object to specify the same firing time for all lasers. Units are in seconds.

You can specify elevation angles by using the ElevationAngles property. If you do not specify the ElevationAngles property, the System object determines the elevation angles by using the ElevationLimits and ElevationResolution properties.

Data Types: duration

`MaterialAttributes` — Information of target materials
table

Since R2024b

Information of target materials in RoadRunner Scenario, stored as an M-by-4 table. M is the number of materials, such as brick, cement, and steel. Four variables of the table specifies these attributes of a material:

Reflectance — Reflectance of a target material in the range (0, 1], where 0 specifies no reflectance and 1 specifies the highest reflectance.
Diffuseness — Diffuseness of a target material in the range [0,1], where 0 specifies specular material and 1 specifies a diffuse material.
Roughness — Roughness of a target material in the range [0, 1], where 0 specifies the smooth surface and 1 specifies a rough surface.
Description — Description of a target material, stored as a string scalar.

You can add, remove, or modify material attributes using this property after creating the System object.

Note

The System object uses material information to generate point cloud data only when a lidar sensor object is connected to RoadRunner Scenario.

By default, the System object stores information of these materials:

Material	Reflectance	Diffuseness	Roughness	Description
`Aluminum`	`0.63`	`1`	`0.3`	`"Weathered aluminum metal from roof"`
`Asphalt`	`0.10`	`1`	`0.3`	`"Road or construction material"`
`Bark`	`0.56`	`1`	`0.3`	`"Pinus ponderosa tree bark"`
`Brick`	`0.29`	`1`	`0.3`	`"Smooth-faced red building construction brick"`
`Cement`	`0.35`	`1`	`0.3`	`"Cement construction concrete cinderblock"`
`Concrete`	`0.33`	`1`	`0.3`	`"Road or construction material"`
`Copper`	`0.57`	`1`	`0.3`	`"Weathered copper metal from roof"`
`Glass`	`0.08`	`1`	`0.3`	`"Transparent construction plate window glass"`
`Grass`	`0.50`	`1`	`0.3`	`"Green Rye grass"`
`Ice`	`0.017`	`1`	`0.3`	`"Ice"`
`Leaves`	`0.56`	`1`	`0.3`	`"Quercus species (Oak tree) leaves"`
`Marble`	`0.84`	`1`	`0.3`	`"White weathered construction marble"`
`Rubber`	`0.60`	`1`	`0.3`	`"White rubberized coating from building roof"`
`Snow`	`0.71`	`1`	`0.3`	`"Coarse granular snow"`
`Steel`	`0.064`	`1`	`0.3`	`"Weathered, galvanized steel metal from roof"`
`Tar`	`0.07`	`1`	`0.3`	`"Black, glossy construction tar"`
`Terracotta`	`0.17`	`1`	`0.3`	`"Weathered terra cotta roofing tile of ceramic composite construction"`
`Water`	`0.025`	`1`	`0.3`	`"Tap water"`
`Wood`	`0.85`	`1`	`0.3`	`"Bare pine wood construction lumber"`

Data Types: table

`CoordinateSystem` — Coordinate system for output point cloud
`"World"` (default) | `"Sensor"` | `"Host"`

Since R2024b

Coordinate system for the output point cloud, specified as one of these options:

"World" — The function outputs point cloud in world coordinate system.
"Sensor" — The function outputs point cloud in sensor coordinate system.
"Host" — The function outputs point cloud in coordinate system of a host actor.

Data Types: char | string

Usage

Syntax

ptCloud = lidar(time)

ptCloud = lidar(tgtPoses,time)

[ptCloud,isValidTime,clusters] = lidar(tgtPoses,time)

Description

ptCloud = lidar(time) generates a lidar point cloud, ptCloud, at the specified simulation time time. The function generates data at time intervals specified by the UpdateRate property of the lidarSensor object lidar.

Note

Use this syntax to generate point cloud data for a drivingScenario (Automated Driving Toolbox) object after adding the lidarSensor object to the scenario using the addSensors (Automated Driving Toolbox) function. This also updates the ActorProfiles property of the lidarSensor object based on the values of the connected scenario.

ptCloud = lidar(tgtPoses,time) generates a lidar point cloud, ptCloud, using the actor poses tgtPoses at the specified simulation time time.

[ptCloud,isValidTime,clusters] = lidar(tgtPoses,time) additionally returns isValidTime, which indicates whether the simulation time is valid, and clusters, which contain the classification data of the output point cloud.

Input Arguments

expand all

`tgtPoses` — Actor poses in scene
L- element array of structures

Actor poses in the scene, specified as an L- element array of structures. Each structure corresponds to an actor. L is the number of actors used.

You can generate this structure using the actorPoses (Automated Driving Toolbox) function. You can also create these structures manually. Each structure has these fields:

Field	Description	Value
`ActorID`	Unique identifier for the actor.	Positive scalar
`Position`	Position of the actor with respect to the ego vehicle coordinate system, in meters.	Vector of the form [`x y z`]
`Velocity`	Velocity (V) of the actor, in meters per second, along the x-, y-, and z- directions.	A vector of the form [`V_x V_y V_z`] Default: `[0 0 0]`
`Roll`	Roll angle of the actor in degrees.	Numeric scalar Default: `0`
`Pitch`	Pitch angle of the actor in degrees.	Numeric scalar Default: `0`
`Yaw`	Yaw angle of the actor in degrees.	Numeric scalar Default: `0`
`AngularVelocity`	Angular velocity (ω) of the actor, in degrees per second, along the x-, y-, and z- directions.	Vector of the form [`ω_x ω_y ω_z`] Default: `[0 0 0]`

`time` — Simulation time
positive scalar

Simulation time, specified as a positive scalar. The lidarSensor object generates new detections at the interval specified by the UpdateRate property. The value of the UpdateRate property must be an integer multiple of the simulation time interval. Updates requested from the sensor between update intervals do not generate a point cloud.

Data Types: single | double

Output Arguments

expand all

`ptCloud` — Point cloud data
`pointCloud` object

Point cloud data generated from the scene, returned as a pointCloud object.

`isValidTime` — Valid simulation time
`0` | `1`

Valid simulation time, returned as a logical 0(false) or 1(true). The value is 0 for updates requested at times between the update interval specified by the UpdateRate property.

`clusters` — Classification data of actors
M-by-N-by-3 array | P-by-3 matrix

Classification data of actors in the scene, returned as an M-by-N-by-3 array for an organized point cloud or a P-by-3 matrix for an unorganized point cloud. M, N are the number of rows and columns in the organized point cloud, and P is the number of points in the unorganized point cloud. The first column contains the ActorIDs, the second column contains the ClassIDs, and the third column contains the material ID representing row number of the table that contains MaterialAttributes. You can use the material ID to get information of target material for the corresponding point.

Note

If the lidar sensor object is not connected to RoadRunner Scenario or if the material type returned by RoadRunner Scenario is not specified in the MaterialAttributes property, the System object outputs NaN values for material attributes.

Object Functions

To use an object function, specify the System object as the first input argument. For example, to release system resources of a System object named obj, use this syntax:

release(obj)

expand all

Common to All System Objects

`step`	Run System object algorithm
`release`	Release resources and allow changes to System object property values and input characteristics
`reset`	Reset internal states of System object

Examples

collapse all

Generate Lidar Point Cloud Data

Open Live Script

Load synthetic scene data containing actor profiles and target poses generated using the drivingScenario (Automated Driving Toolbox) object into the workspace.

sceneData = load("scene_data.mat");
sceneActorProfiles = sceneData.ActorProfiles;
sceneTargetPoses = sceneData.TargetPoses;

Load the target material reflectance data.

reflectanceData = load("scene_target_reflectances.mat");
targetReflectance = reflectanceData.TargetReflectances;

Define the reflectances for each actor.

for i = 1:numel(sceneActorProfiles)
    sceneActorProfiles(i).MeshTargetReflectances = targetReflectance{i};
end

Create a lidarSensor System object, and define the actor profiles for the object.

lidarS = lidarSensor(AzimuthResolution=0.5,RainRate=2.5);
lidarS.ActorProfiles = sceneActorProfiles;

Create a pcplayer object to visualize the lidar sensor point cloud detections.

player = pcplayer([-100 100],[-20 20],[0 5]);

Generate and visualize the point cloud detections at valid simulation times.

  for i = 1:5:numel(sceneTargetPoses)
     if(~player.isOpen)
         break
     end
     [ptCloud,isValid] = lidarS(sceneTargetPoses{i},i*0.1);
     if(isValid)
         view(player,ptCloud)
     end
  end

Figure Point Cloud Player contains an axes object. The axes object with xlabel X, ylabel Y contains an object of type scatter.

References

[1] Tian, Wenxin, Lingli Tang, Yuwei Chen, Ziyang Li, Jiajia Zhu, Changhui Jiang, Peilun Hu, et al. “Analysis and Radiometric Calibration for Backscatter Intensity of Hyperspectral LiDAR Caused by Incident Angle Effect.” Sensors 21, no. 9 (April 23, 2021): 2960. https://doi.org/10.3390/s21092960.

[2] Goodin, Christopher, Daniel Carruth, Matthew Doude, and Christopher Hudson. “Predicting the Influence of Rain on LIDAR in ADAS.” Electronics 8, no. 1 (January 15, 2019): 89. https://doi.org/10.3390/electronics8010089.

Version History

Introduced in R2022a

expand all

R2024b: Use target material information from RoadRunner Scenario

You can now use material information of target actors in RoadRunner Scenario to generate point cloud data by using the MaterialAttributes property. clusters output argument also contains material ID information.

R2024b: Specify coordinate system for output point cloud

Specify World, Host, or Sensor type of coordinate system for the output point cloud by using the CoordinateSystem property.

R2024a: Specify sensor model

You can now specify the type of sensor model by using the Model property. Specifying this property sets the default elevation angle values based on the specified sensor.

R2024a: Simulate motion distortion

You can now simulate motion distortion by using the HasMotionDistortion and FiringTimes properties.

R2024a: Add lidar sensor to vehicle actors in RoadRunner Scenario

You can now generate point cloud data in a RoadRunner scenario using a lidar sensor model defined in MATLAB^®. The SensorSimulation (Automated Driving Toolbox) object now supports the lidarSensor System object. Configure the lidar sensor model in MATLAB, and then use the addSensors (Automated Driving Toolbox) function to add it to vehicles in RoadRunner scenario.

R2023b: Generate point cloud data from a `drivingScenario` object

You can now generate point cloud data for a driving scenario by adding the lidarSensor object to a drivingScenario (Automated Driving Toolbox) object using the addSensors (Automated Driving Toolbox) object function.

R2023a: Simulate weather conditions such as rain and fog

You can now simulate the effects of weather conditions on the point cloud data by using the FogVisibility and Rainrate properties.

lidarSensor

Description

Creation

Syntax

Description

Properties

SensorIndex — Unique identifier for sensor 1 (default) | positive integer

HostID — ActorID of ego vehicle 1 (default) | positive integer

UpdateRate — Time interval between consecutive sensor updates 0.1 (default) | positive scalar

Position — Sensor center position [1.5 0 1.6] (default) | [x y height]

Orientation — Sensor orientation [0 0 0] (default) | three-element vector of form [roll pitch yaw]

MaxRange — Maximum detection range of sensor 120 (default) | positive scalar

RangeAccuracy — Accuracy of sensor range measurement 0.002 (default) | positive scalar

HasNoise — Point cloud data has added noise true (default) | false

HasOrganizedOutput — Output point cloud is organized true (default) | false

AzimuthResolution — Azimuth resolution of lidar sensor 0.16 (default) | positive scalar

ElevationResolution — Elevation resolution of lidar sensor 1.25 (default) | positive scalar

AzimuthLimits — Azimuth limits of lidar sensor [-180 180] (default) | two-element vector

ElevationLimits — Elevation limits of lidar sensor [-20 20] (default) | two-element vector

ElevationAngles — Elevation angles of lidar sensor [] (default) | N-element vector

ActorProfiles — Physical characteristics of actors [] (default) | structure | L-element array of structures

FogVisibility — Visible distance in fog 1000 (default) | positive scalar

Rainrate — Rate of rainfall 0 (default) | positive scalar

Model — Name of sensor model 'Custom' (default) | character vector | string scalar

HasMotionDistortion — Motion distortion flag false or 0 (default) | true or 1

FiringTimes — Firing times of lasers in lidar sensor [] (default) | duration object | array of duration objects

MaterialAttributes — Information of target materials table

CoordinateSystem — Coordinate system for output point cloud "World" (default) | "Sensor" | "Host"

Usage

Syntax

Description

Input Arguments

tgtPoses — Actor poses in scene L- element array of structures

time — Simulation time positive scalar

Output Arguments

ptCloud — Point cloud data pointCloud object

isValidTime — Valid simulation time 0 | 1

clusters — Classification data of actors M-by-N-by-3 array | P-by-3 matrix

Object Functions

Common to All System Objects

Examples

Generate Lidar Point Cloud Data

References

Version History

R2024b: Use target material information from RoadRunner Scenario

R2024b: Specify coordinate system for output point cloud

R2024a: Specify sensor model

R2024a: Simulate motion distortion

R2024a: Add lidar sensor to vehicle actors in RoadRunner Scenario

R2023b: Generate point cloud data from a drivingScenario object

R2023a: Simulate weather conditions such as rain and fog

See Also

Apps

Functions

Blocks

Topics

WeChat

`SensorIndex` — Unique identifier for sensor
`1` (default) | positive integer

`HostID` — `ActorID` of ego vehicle
`1` (default) | positive integer

`UpdateRate` — Time interval between consecutive sensor updates
`0.1` (default) | positive scalar

`Position` — Sensor center position
`[1.5 0 1.6]` (default) | [x y height]

`Orientation` — Sensor orientation
`[0 0 0]` (default) | three-element vector of form [roll pitch yaw]

`MaxRange` — Maximum detection range of sensor
`120` (default) | positive scalar

`RangeAccuracy` — Accuracy of sensor range measurement
`0.002` (default) | positive scalar

`HasNoise` — Point cloud data has added noise
`true` (default) | `false`

`HasOrganizedOutput` — Output point cloud is organized
`true` (default) | `false`

`AzimuthResolution` — Azimuth resolution of lidar sensor
`0.16` (default) | positive scalar

`ElevationResolution` — Elevation resolution of lidar sensor
`1.25` (default) | positive scalar

`AzimuthLimits` — Azimuth limits of lidar sensor
`[-180 180]` (default) | two-element vector

`ElevationLimits` — Elevation limits of lidar sensor
`[-20 20]` (default) | two-element vector

`ElevationAngles` — Elevation angles of lidar sensor
`[]` (default) | N-element vector

`ActorProfiles` — Physical characteristics of actors
`[]` (default) | structure | L-element array of structures

`FogVisibility` — Visible distance in fog
`1000` (default) | positive scalar

`Rainrate` — Rate of rainfall
`0` (default) | positive scalar

`Model` — Name of sensor model
`'Custom'` (default) | character vector | string scalar

`HasMotionDistortion` — Motion distortion flag
`false` or `0` (default) | `true` or `1`

`FiringTimes` — Firing times of lasers in lidar sensor
`[]` (default) | `duration` object | array of `duration` objects

`MaterialAttributes` — Information of target materials
table

`CoordinateSystem` — Coordinate system for output point cloud
`"World"` (default) | `"Sensor"` | `"Host"`

`tgtPoses` — Actor poses in scene
L- element array of structures

`time` — Simulation time
positive scalar

`ptCloud` — Point cloud data
`pointCloud` object

`isValidTime` — Valid simulation time
`0` | `1`

`clusters` — Classification data of actors
M-by-N-by-3 array | P-by-3 matrix

R2023b: Generate point cloud data from a `drivingScenario` object