Cameras and sensors

Client window

Sensors are a special type of actor able to measure and stream data. All the sensors have a listen method that registers the callback function that will be called each time the sensor produces a new measurement. Sensors are typically attached to vehicles and produce data either each simulation update, or when a certain event is registered.

The following Python excerpt shows how you would typically attach a sensor to a vehicle, in this case we are adding a dashboard HD camera to a vehicle.

# Find the blueprint of the sensor.
blueprint = world.get_blueprint_library().find('sensor.camera.rgb')
# Modify the attributes of the blueprint to set image resolution and field of view.
blueprint.set_attribute('image_size_x', '1920')
blueprint.set_attribute('image_size_y', '1080')
blueprint.set_attribute('fov', '110')
# Set the time in seconds between sensor captures
blueprint.set_attribute('sensor_tick', '1.0')
# Provide the position of the sensor relative to the vehicle.
transform = carla.Transform(carla.Location(x=0.8, z=1.7))
# Tell the world to spawn the sensor, don't forget to attach it to your vehicle actor.
sensor = world.spawn_actor(blueprint, transform, attach_to=my_vehicle)
# Subscribe to the sensor stream by providing a callback function, this function is
# called each time a new image is generated by the sensor.
sensor.listen(lambda data: do_something(data))

Note that each sensor has a different set of attributes and produces different type of data. However, the data produced by a sensor comes always tagged with a frame number and a transform. The frame number is used to identify the frame at which the measurement took place, the transform gives you the transformation in world coordinates of the sensor at that same frame.

Most sensor data objects, like images and lidar measurements, have a function for saving the measurements to disk.

This is the list of sensors currently available

sensor.camera.rgb

ImageRGB

The "RGB" camera acts as a regular camera capturing images from the scene.

Blueprint attribute Type Default Description
image_size_x int 800 Image width in pixels
image_size_y int 600 Image height in pixels
fov float 90.0 Field of view in degrees
enable_postprocess_effects bool True Whether the post-process effect in the scene affect the image
sensor_tick float 0.0 Seconds between sensor captures (ticks)

sensor_tick tells how fast we want the sensor to capture the data. A value of 1.5 means that we want the sensor to capture data each second and a half. By default a value of 0.0 means as fast as possible.

If enable_postprocess_effects is enabled, a set of post-process effects is applied to the image to create a more realistic feel

  • Vignette Darkens the border of the screen.
  • Grain jitter Adds a bit of noise to the render.
  • Bloom Intense lights burn the area around them.
  • Auto exposure Modifies the image gamma to simulate the eye adaptation to darker or brighter areas.
  • Lens flares Simulates the reflection of bright objects on the lens.
  • Depth of field Blurs objects near or very far away of the camera.

This sensor produces carla.Image objects.

Sensor data attribute Type Description
frame_number int Frame count when the measurement took place
transform carla.Transform Transform in world coordinates of the sensor at the time of the measurement
width int Image width in pixels
height int Image height in pixels
fov float Field of view in degrees
raw_data bytes Array of BGRA 32-bit pixels

sensor.camera.depth

ImageDepth

The "Depth" camera provides a view over the scene codifying the distance of each pixel to the camera (also known as depth buffer or z-buffer).

Blueprint attribute Type Default Description
image_size_x int 800 Image width in pixels
image_size_y int 600 Image height in pixels
fov float 90.0 Field of view in degrees
sensor_tick float 0.0 Seconds between sensor captures (ticks)

This sensor produces carla.Image objects.

Sensor data attribute Type Description
frame_number int Frame count when the measurement took place
transform carla.Transform Transform in world coordinates of the sensor at the time of the measurement
width int Image width in pixels
height int Image height in pixels
fov float Field of view in degrees
raw_data bytes Array of BGRA 32-bit pixels

The image codifies the depth in 3 channels of the RGB color space, from less to more significant bytes: R -> G -> B. The actual distance in meters can be decoded with

normalized = (R + G * 256 + B * 256 * 256) / (256 * 256 * 256 - 1)
in_meters = 1000 * normalized

sensor.camera.semantic_segmentation

ImageSemanticSegmentation

The "Semantic Segmentation" camera classifies every object in the view by displaying it in a different color according to the object class. E.g., pedestrians appear in a different color than vehicles.

Blueprint attribute Type Default Description
image_size_x int 800 Image width in pixels
image_size_y int 600 Image height in pixels
fov float 90.0 Field of view in degrees
sensor_tick float 0.0 Seconds between sensor captures (ticks)

This sensor produces carla.Image objects.

Sensor data attribute Type Description
frame_number int Frame count when the measurement took place
transform carla.Transform Transform in world coordinates of the sensor at the time of the measurement
width int Image width in pixels
height int Image height in pixels
fov float Field of view in degrees
raw_data bytes Array of BGRA 32-bit pixels

The server provides an image with the tag information encoded in the red channel. A pixel with a red value of x displays an object with tag x. The following tags are currently available

Value Tag Converted color
0 Unlabeled ( 0, 0, 0)
1 Building ( 70, 70, 70)
2 Fence (190, 153, 153)
3 Other (250, 170, 160)
4 Pedestrian (220, 20, 60)
5 Pole (153, 153, 153)
6 Road line (157, 234, 50)
7 Road (128, 64, 128)
8 Sidewalk (244, 35, 232)
9 Vegetation (107, 142, 35)
10 Car ( 0, 0, 142)
11 Wall (102, 102, 156)
12 Traffic sign (220, 220, 0)

This is implemented by tagging every object in the scene before hand (either at begin play or on spawn). The objects are classified by their relative file path in the project. E.g., every mesh stored in the "Unreal/CarlaUE4/Content/Static/Pedestrians" folder it's tagged as pedestrian.

Note

Adding new tags: At the moment adding new tags is not very flexible and requires to modify the C++ code. Add a new label to the ECityObjectLabel enum in "Tagger.h", and its corresponding filepath check inside GetLabelByFolderName() function in "Tagger.cpp".

sensor.lidar.ray_cast

LidarPointCloud

This sensor simulates a rotating Lidar implemented using ray-casting. The points are computed by adding a laser for each channel distributed in the vertical FOV, then the rotation is simulated computing the horizontal angle that the Lidar rotated this frame, and doing a ray-cast for each point that each laser was supposed to generate this frame; points_per_second / (FPS * channels).

Blueprint attribute Type Default Description
channels int 32 Number of lasers
range float 1000 Maximum measurement distance in meters
points_per_second int 56000 Points generated by all lasers per second
rotation_frequency float 10.0 Lidar rotation frequency
upper_fov float 10.0 Angle in degrees of the upper most laser
lower_fov float -30.0 Angle in degrees of the lower most laser
sensor_tick float 0.0 Seconds between sensor captures (ticks)

This sensor produces carla.LidarMeasurement objects.

Sensor data attribute Type Description
frame_number int Frame count when the measurement took place
transform carla.Transform Transform in world coordinates of the sensor at the time of the measurement
horizontal_angle float Angle in XY plane of the lidar this frame (in degrees)
channels int Number of channels (lasers) of the lidar
get_point_count(channel) int Number of points per channel captured this frame
raw_data bytes Array of 32-bits floats (XYZ of each point)

The object also acts as a Python list of carla.Location

for location in lidar_measurement:
    print(location)

A Lidar measurement contains a packet with all the points generated during a 1/FPS interval. During this interval the physics is not updated so all the points in a measurement reflect the same "static picture" of the scene.

Tip

Running the simulator at fixed time-step it is possible to tune the horizontal angle of each measurement. By adjusting the frame rate and the rotation frequency is possible, for instance, to get a 360 view each measurement.

sensor.other.collision

This sensor, when attached to an actor, it registers an event each time the actor collisions against something in the world. This sensor does not have any configurable attribute.

This sensor produces a carla.CollisionEvent object for each collision registered

Sensor data attribute Type Description
frame_number int Frame count when the measurement took place
transform carla.Transform Transform in world coordinates of the sensor at the time of the measurement
actor carla.Actor Actor that measured the collision ("self" actor)
other_actor carla.Actor Actor against whom we collide
normal_impulse carla.Vector3D Normal impulse result of the collision

Note that several collision events might be registered during a single simulation update.

sensor.other.lane_detector

This sensor is a work in progress, currently very limited.

This sensor, when attached to an actor, it registers an event each time the actor crosses a lane marking. This sensor is somehow special as it works fully on the client-side. The lane detector uses the road data of the active map to determine whether a vehicle is invading another lane. This information is based on the OpenDrive file provided by the map, therefore it is subject to the fidelity of the OpenDrive description. In some places there might be discrepancies between the lanes visible by the cameras and the lanes registered by this sensor.

This sensor does not have any configurable attribute.

This sensor produces a carla.LaneInvasionEvent object for each lane marking crossed by the actor

Sensor data attribute Type Description
frame_number int Frame count when the measurement took place
transform carla.Transform Transform in world coordinates of the sensor at the time of the measurement
actor carla.Actor Actor that invaded another lane ("self" actor)
crossed_lane_markings carla.LaneMarking list List of lane markings that have been crossed