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LidarSensor is the component that simulates the LiDAR (Light Detection and Ranging) sensor. LiDAR works by emitting laser beams that bounce off objects in the environment, and then measuring the time it takes for the reflected beams to return, allowing the sensor to create a 3D map of the surroundings. This data is used for object detection, localization, and mapping.

LiDAR in an autonomous vehicle can be used for many purposes. The ones mounted on the top of autonomous vehicles are primarily used

  • to scan the environment for localization in space
  • to detect and identify obstacles such as approaching vehicles, pedestrians or other objects in the driving path.

LiDARs placed on the left and right sides of the vehicle are mainly used to monitor the traffic lane and detect vehicles moving in adjacent lanes, enabling safe maneuvers such as lane changing or turning.

LidarSensor component is a part of RGLUnityPlugin that integrates the external RobotecGPULidar (RGL) library with Unity. RGL also allows to provide additional information about objects, more about it here.

Use RGL in your scene

If you want to use RGL in your scene, make sure the scene has an SceneManager component added and all objects meet the usage requirements.

RGL default scenes

If you would like to see how LidarSensor works using RGL or run some tests, we encourage you to familiarize yourself with the RGL test scenes section.

Supported LiDARs

The current scripts implementation allows you to configure the prefab for any mechanical LiDAR. You can read about how to do it here. MEMS-based LiDARs due to their different design are not yet fully supported.


Prefabs can be found under the following path:


The table of available prefabs can be found below:

LiDAR Path Appearance
HESAI Pandar40P HesaiPandar40P.prefab
HESAI PandarQT64 HesaiPandarQT64.prefab
HESAI PandarXT32 HesaiPandarXT32.prefab
HESAI QT128C2X HesaiQT128C2X.prefab
HESAI Pandar128E4X HesaiPandar128E4X.prefab
HESAI AT128 E2X HesaiAT128E2X.prefab
Ouster OS1-64 OusterOS1-64.prefab
Velodyne VLP-16 VelodyneVLP16.prefab
Velodyne VLC-32C VelodyneVLP32C.prefab
Velodyne VLS-128-AP VelodyneVLS128.prefab


LidarSensor is configured in default vehicle EgoVehicle prefab. It is added to URDF object as a child of sensor_kit_base_link. LidarSensor placed in this way does not have its own frame, and the data is published relative to sensor_kit_base_link. More details about the location of the sensors in the vehicle can be found here.

A detailed description of the URDF structure and sensors added to prefab Lexus RX450h 2015 is available in this section.

Additional LiDARs

For a LiDAR placed on the left side, right side or rear, an additional link should be defined.

Components and Resources


The LiDAR sensor simulation functionality is split into three components:

  • Lidar Sensor (script) - provides lidar configuration, creates RGL pipeline to simulate lidar, and performs native RGL raytrace calls,
  • Rgl Lidar Publisher (script) - extends RGL pipeline with nodes to publish ROS2 messages.
  • Point Cloud Visualization (script) - visualizes point cloud collected by sensor.

Moreover, the scripts use Resources to provide configuration for prefabs of supported lidar models:

  • LaserModels - provides a list of supported models,
  • LaserArrayLibrary - provides data related to laser array construction for supported models,
  • LaserConfigurationLibrary - provides full configuration, with ranges and noise for supported models.

These are elements of the RGLUnityPlugin, you can read more here.

Lidar Sensor (script)


This is the main component that creates the RGL node pipeline for the LiDAR simulation. The pipeline consists of:

  • setting ray pattern,
  • transforming rays to represent pose of the sensor on the scene,
  • applying Gaussian noise,
  • performing raytracing,
  • removing non-hits from the result point cloud,
  • transforming point cloud to sensor frame coordinate system.

Elements configurable from the editor level

  • Automatic Capture Hz - the rate of sensor processing (default: 10Hz)
  • Model Preset - allows selecting one of the built-in LiDAR models (default: RangeMeter)
  • Apply Distance Gaussian Noise - enable/disable distance Gaussian noise (default: true)
  • Apply Angular Gaussian Noise - enable/disable angular Gaussian noise (default: true)
  • Apply Velocity Distortion - enable/disable velocity distortion (default: false)
  • Configuration:
    • Laser Array - geometry description of lidar's array of lasers, should be prepared on the basis of the manual for a given model of LiDAR (default: loaded from LaserArrayLibrary)
    • Horizontal Resolution - the horiontal resolution of laser array firings
    • Min H Angle - minimum horizontal angle, left (default: 0)
    • Max H Angle - maximum horizontal angle, right (default: 0)
    • Laser Array Cycle Time - time between two consecutive firings of the whole laser array in milliseconds (default: 0); used for velocity distortion feature.
    • Beam Divergence - represents the deviation of photons from a single beam emitted by a LiDAR sensor (in degrees); used for simulating snow only (private feature).
    • Noise Params:
      • Angular Noise Type - angular noise type
        (default: Ray Based)
      • Angular Noise St Dev - angular noise standard deviation in degree
        (default: 0.05729578)
      • Angular Noise Mean - angular noise mean in degrees
        (default: 0)
      • Distance Noise St Dev Base - distance noise standard deviation base in meters
        (default: 0.02)
      • Distance Noise Rise Per Meter - distance noise standard deviation rise per meter
        (default: 0)
      • Distance Noise Mean - distance noise mean in meters
        (default: 0)
    • Additional options (available for some Lidar Model Preset)
      • Min Range - minimum range of the sensor (if not avaiable, the range is different for each laser in Laser Array)
      • Max Range - maximum range of the sensor (if not avaiable, the range is different for each laser in Laser Array)
      • High Resolution Mode Enabled - whether to activate high resolution mode (available for Hesai Pandar 128E4X LiDAR model)

Output Data

LidarSensor provides public methods to extend this pipeline with additional RGL nodes. In this way, other components can request point cloud processing operations and receive data in the desired format.

Example of how to get XYZ point cloud data:

  1. To obtain point cloud data from another component you have to create a new RGLNodeSequence with RGL node to yield XYZ field and connect it to LidarSensor:
    rglOutSubgraph = new RGLNodeSequence().AddNodePointsYield("OUT_XYZ", RGLField.XYZ_F32);
    lidarSensor = GetComponent<LidarSensor>();
    lidarSensor.ConnectToWorldFrame(rglOutSubgraph); // you can also connect to Lidar frame using ConnectToLidarFrame
    // You can add a callback to receive a notification when new data is ready
    lidarSensor.onNewData += HandleLidarDataMethod;
  2. To get data from RGLNodeSequence call GetResultData:
    Vector3[] xyz = new Vector3[0];
    rglOutSubgraph.GetResultData<Vector3>(ref xyz);

Rgl Lidar Publisher (script)


RglLidarPublisher extends the main RGL pipeline created in LidarSensor with RGL nodes that produce point clouds in specific format and publish them to the ROS2 topic. Thanks to the ROS2 integration with RGL, point clouds can be published directly from the native library. RGL creates ROS2 node named /RobotecGPULidar with publishers generated by RGL nodes.

Currently, RglLidarPublisher implements ROS2 publishers for two message types:

PointCloud2 message allows publishing point clouds with different points attributes (described by fields parameter). In order to easily select different frequently used field sets RglLidarPublisher has several field presets defined:

Preset Description Fields
Pcl 24 24-byte point cloud format used by Autoware XYZ_VEC3_F32, PADDING_32, INTENSITY_F32, RING_ID_U16, PADDING_16
Pcl 48 48-byte extended version point cloud format used by Autoware XYZ_VEC3_F32, PADDING_32, INTENSITY_F32, RING_ID_U16, PADDING_16, AZIMUTH_F32, DISTANCE_F32, RETURN_TYPE_U8, PADDING_8, PADDING_16, PADDING_32, TIME_STAMP_F64
ML Instance Segmentation Machine learning format for instance/semantic segmentation tasks XYZ_VEC3_F32, ENTITY_ID_I32, INTENSITY_F32
Radar Smart Micro Format used in Radar Smart Micro XYZ_VEC3_F32, RADIAL_SPEED_F32, POWER_F32, RCS_F32, NOISE_F32, SNR_F32
Custom Empty format that allows the user to define its fieldsets

rosPCL48 format

For a better understanding of the rosPCL48 format, we encourage you to familiarize yourself with the point cloud pre-processing process in Autoware, which is described here.

Elements configurable from the editor level

  • Frame ID - frame in which data are published, used in Header (default: "world")
  • Qos - Quality of service profile used in the publication
    • Reliability Policy - Reliability policy (default: Best effort)
    • Durability Policy - Durability policy (default: Volatile)
    • History Policy - History policy (default: Keep last)
    • History Depth - History depth. If history policy is Keep all, depth is ignored. (default: 5)
  • Point Cloud 2 Publishers - List of sensor_msgs/PointCloud2 message publishers
    • Topic - Topic name to publish on
    • Publish - If false, publishing will be stopped
    • Fields Preset - allows selecting one of the pre-defined fieldsets (choose Custom to define your own)
    • Fields - List of fields to be present in the message
  • Radar Scan Publishers - List of radar_msgs/RadarScan message publishers
    • Topic - Topic name to publish on
    • Publish - If false, publishing will be stopped

Elements configurable in simulation runtime

Once the simulation starts, only the Publish flag is handled. All of the publishers are initialized on the simulation startup and updates of their parameters are not supported in runtime. Any changes to the publishing configuration are ignored.

Default Publishing Topics

  • Frequency: 10Hz
  • QoS: Best effort, Volatile, Keep last/5
Category Topic Message type frame_id
PointCloud 24-byte format /lidar/pointcloud sensor_msgs/PointCloud2 world
PointCloud 48-byte format /lidar/pointcloud_ex sensor_msgs/PointCloud2 world

Point Cloud Visualization (script)


A component visualizing a point cloud obtained from RGL in the form of a Vector3 list as colored points in the Unity scene. Based on the defined color table, it colors the points depending on the height at which they are located.

The obtained points are displayed as the vertices of mesh, and their coloring is possible thanks to the use of PointCloudMaterial material which can be found in the following path:


Point Cloud Visualization preview:

Elements configurable from the editor level

  • Point Shape - the shape of the displayed points (default: Box)
  • Point Size - the size of the displayed points (default: 0.05)
  • Colors - color list used depending on height
    (default: 6 colors: red, orange, yellow, green, blue, violet)
  • Auto Compute Coloring Heights - automatic calculation of heights limits for a list of colors (default: false)
  • Min Coloring Height - minimum height value from which color matching is performed, below this value all points have the first color from the list (default: 0)
  • Max Coloring Height - maximum height value from which color matching is performed, above this value all points have the last color from the list (default: 20)

Read material information

To ensure the publication of the information described in this section, GameObjects must be adjusted accordingly. This tutorial describes how to do it.

Intensity Texture

RGL Unity Plugin allows assigning an Intensity Texture to the GameObjects to produce a point cloud containing information about the lidar ray intensity of hit. It can be used to distinguish different levels of an object's reflectivity.

Output data

Point cloud containing intensity is published on the ROS2 topic via RglLidarPublisher component. The intensity value is stored in the intensity field of the sensor_msgs/PointCloud2 message.

Instance segmentation

RGL Unity Plugin allows assigning an ID to GameObjects to produce a point cloud containing information about hit objects. It can be used for instance/semantic segmentation tasks. This tutorial describes how to do it.


If you would like to see how LidarInstanceSegmentationDemo works using RGL or run some tests, we encourage you to familiarize yourself with this section.

Output data

Point cloud containing hit objects IDs is published on the ROS2 topic via RglLidarPublisher component. The publisher for such point cloud format is not added by default. Add a new PointCloud2 publisher with ML Instance Segmentation fields preset or create your format by selecting Custom preset (remember to add ENTITY_ID_I32 field which holds objects IDs).

Dictionary mapping

The resulting simulation data contains only the id of objects without their human-readable names. To facilitate the interpretation of such data, a function has been implemented to save a file with a dictionary mapping instance ID to GameObject names. It writes pairs of values in the yaml format:

  • The name of the GameObject
  • Category ID of SemanticCategory component

To enable saving dictionary mapping set output file path to the Semantic Category Dictionary File property in the Scene Manager component:

The dictionary mapping file will be saved at the end of the simulation.