LiDAR Sensor

AutonomySim supports light detection-and-ranging (LiDAR) sensors.

The enablement of lidar and the other lidar settings can be configured via AutonomySimSettings.json. Please see general sensors for information on configruation of general/shared sensor settings.

Enabling LiDAR on a Vehicle

• By default, LiDAR is disabled. To enable it, set the SensorType and Enabled attributes in settings json:

    "Lidar1": {
         "SensorType": 6,
         "Enabled" : true,
    }

• Multiple lidars can be enabled on a vehicle.

LiDAR configuration

The following parameters can be configured right now via settings json.

Parameter	Description
NumberOfChannels	Number of channels/lasers of the lidar
Range	Range, in meters
PointsPerSecond	Number of points captured per second
RotationsPerSecond	Rotations per second
HorizontalFOVStart	Horizontal FOV start for the lidar, in degrees
HorizontalFOVEnd	Horizontal FOV end for the lidar, in degrees
VerticalFOVUpper	Vertical FOV upper limit for the lidar, in degrees
VerticalFOVLower	Vertical FOV lower limit for the lidar, in degrees
X Y Z	Position of the lidar relative to the vehicle (in NED, in meters)
Roll Pitch Yaw	Orientation of the lidar relative to the vehicle (in degrees, yaw-pitch-roll order to front vector +X)
DataFrame	Frame for the points in output ("VehicleInertialFrame" or "SensorLocalFrame")
ExternalController	Whether data is to be sent to external controller such as ArduPilot or PX4 if being used (default true) (PX4 doesn't send Lidar data currently)

For example:

{
    "SeeDocsAt": "https://nervosys.github.io/AutonomySim/settings/",
    "SettingsVersion": 1.2,

    "SimMode": "Multirotor",

     "Vehicles": {
        "Drone1": {
            "VehicleType": "simpleflight",
            "AutoCreate": true,
            "Sensors": {
                "LidarSensor1": {
                    "SensorType": 6,
                    "Enabled" : true,
                    "NumberOfChannels": 16,
                    "RotationsPerSecond": 10,
                    "PointsPerSecond": 100000,
                    "X": 0, "Y": 0, "Z": -1,
                    "Roll": 0, "Pitch": 0, "Yaw" : 0,
                    "VerticalFOVUpper": -15,
                    "VerticalFOVLower": -25,
                    "HorizontalFOVStart": -20,
                    "HorizontalFOVEnd": 20,
                    "DrawDebugPoints": true,
                    "DataFrame": "SensorLocalFrame"
                },
                "LidarSensor2": {
                   "SensorType": 6,
                    "Enabled" : true,
                    "NumberOfChannels": 4,
                    "RotationsPerSecond": 10,
                    "PointsPerSecond": 10000,
                    "X": 0, "Y": 0, "Z": -1,
                    "Roll": 0, "Pitch": 0, "Yaw" : 0,
                    "VerticalFOVUpper": -15,
                    "VerticalFOVLower": -25,
                    "DrawDebugPoints": true,
                    "DataFrame": "SensorLocalFrame"
                }
            }
        }
    }
}

Server side visualization for debugging

By default, the LiDAR points are not drawn on the viewport. To enable the drawing of hit laser points on the viewport, please enable setting DrawDebugPoints via settings json.

    "Lidar1": {
         ...
         "DrawDebugPoints": true
    },

Note

Enabling DrawDebugPoints can cause excessive memory usage and crash in releases v1.3.1, v1.3.0. This has been fixed in the main branch and should work in later releases

Client API

Use getLidarData() API to retrieve the Lidar data.

• The API returns a Point-Cloud as a flat array of floats along with the timestamp of the capture and lidar pose.
• Point-Cloud:
  • The floats represent [x,y,z] coordinate for each point hit within the range in the last scan.
  • The frame for the points in the output is configurable using "DataFrame" attribute -
    • "" or VehicleInertialFrame -- default; returned points are in vehicle inertial frame (in NED, in meters)
    • SensorLocalFrame -- returned points are in lidar local frame (in NED, in meters)
• LiDAR Pose:
  • Lidar pose in the vehicle inertial frame (in NED, in meters)
  • Can be used to transform points to other frames.
• Segmentation: The segmentation of each lidar point's collided object

Python Examples

• drone_lidar.py
• car_lidar.py
• sensorframe_lidar_pointcloud.py
• vehicleframe_lidar_pointcloud.py

Coming soon

• Visualization of LiDAR data on client side.