Several documentation fixes and rewording
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<h1>Cameras and sensors</h1>
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This document describes the details of the different cameras/sensors currently
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available as well as the resulting images produced by them.
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Sensors are one type of actor with the characteristic of having a listen
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function, you can subscribe to the sensor by providing a callback function. This
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callback is called each time a new measurement is received from the sensor.
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Sensors are a special type of actor able to measure and stream data. All the
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sensors have a `listen` method that registers the callback function that will
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be called each time the sensor produces a new measurement. Sensors are typically
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attached to vehicles and produce data either each simulation update, or when a
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certain event is registered.
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You typically add a sensor to a vehicle with the following Python code, here we
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are adding an HD camera
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The following Python excerpt shows how you would typically attach a sensor to a
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vehicle, in this case we are adding a dashboard HD camera to a vehicle.
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```py
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# Find the blueprint of the sensor.
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@ -16,14 +17,14 @@ blueprint = world.get_blueprint_library().find('sensor.camera.rgb')
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# Modify the attributes of the blueprint to set image resolution and field of view.
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blueprint.set_attribute('image_size_x', '1920')
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blueprint.set_attribute('image_size_y', '1080')
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blueprint.set_attribute('fov', '100')
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blueprint.set_attribute('fov', '110')
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# Provide the position of the sensor relative to the vehicle.
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transform = carla.Transform(carla.Location(x=0.8, z=1.7))
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# Tell the world to spawn the sensor, don't forget to attach it to your vehicle actor.
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sensor = world.spawn_actor(blueprint, transform, attach_to=my_vehicle)
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# Subscribe to the sensor stream by providing a callback function, this function is
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# called each time a new image is generated by the sensor.
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sensor.listen(lambda image: do_something(image))
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sensor.listen(lambda data: do_something(data))
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```
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Note that each sensor has a different set of attributes and produces different
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@ -32,7 +33,10 @@ type of data. However, the data produced by a sensor comes always tagged with a
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frame at which the measurement took place, the transform gives you the
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transformation in world coordinates of the sensor at that same frame.
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This is the list of sensors currently available in CARLA
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Most sensor data objects, like images and lidar measurements, have a function
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for saving the measurements to disk.
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This is the list of sensors currently available
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* [sensor.camera.rgb](#sensorcamerargb)
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* [sensor.camera.depth](#sensorcameradepth)
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@ -66,7 +70,8 @@ applied to the image to create a more realistic feel
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* **Lens flares** Simulates the reflection of bright objects on the lens.
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* **Depth of field** Blurs objects near or very far away of the camera.
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This sensor produces `carla.Image` objects.
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This sensor produces [`carla.Image`](python_api.md#carlaimagecarlasensordata)
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objects.
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| Sensor data attribute | Type | Description |
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| --------------------- | ---- | ----------- |
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@ -91,7 +96,8 @@ pixel to the camera (also known as **depth buffer** or **z-buffer**).
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| `image_size_y` | int | 600 | Image height in pixels |
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| `fov` | float | 90.0 | Field of view in degrees |
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This sensor produces `carla.Image` objects.
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This sensor produces [`carla.Image`](python_api.md#carlaimagecarlasensordata)
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objects.
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| Sensor data attribute | Type | Description |
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| --------------------- | ---- | ----------- |
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@ -127,7 +133,8 @@ pedestrians appear in a different color than vehicles.
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| `image_size_y` | int | 600 | Image height in pixels |
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| `fov` | float | 90.0 | Field of view in degrees |
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This sensor produces `carla.Image` objects.
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This sensor produces [`carla.Image`](python_api.md#carlaimagecarlasensordata)
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objects.
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| Sensor data attribute | Type | Description |
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| --------------------- | ---- | ----------- |
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@ -180,7 +187,7 @@ This sensor simulates a rotating Lidar implemented using ray-casting. The points
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are computed by adding a laser for each channel distributed in the vertical FOV,
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then the rotation is simulated computing the horizontal angle that the Lidar
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rotated this frame, and doing a ray-cast for each point that each laser was
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supposed to generate this frame; `PointsPerSecond / (FPS * Channels)`.
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supposed to generate this frame; `points_per_second / (FPS * channels)`.
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| Blueprint attribute | Type | Default | Description |
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| -------------------- | ---- | ------- | ----------- |
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@ -191,7 +198,9 @@ supposed to generate this frame; `PointsPerSecond / (FPS * Channels)`.
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| `upper_fov` | float | 10.0 | Angle in degrees of the upper most laser |
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| `lower_fov` | float | -30.0 | Angle in degrees of the lower most laser |
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This sensor produces `carla.LidarMeasurement` objects.
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This sensor produces
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[`carla.LidarMeasurement`](python_api.md#carlalidarmeasurementcarlasensordata)
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objects.
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| Sensor data attribute | Type | Description |
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| -------------------------- | ---------- | ----------- |
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@ -227,8 +236,9 @@ This sensor, when attached to an actor, it registers an event each time the
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actor collisions against something in the world. This sensor does not have any
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configurable attribute.
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This sensor produces a `carla.CollisionEvent` object for each collision
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registered
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This sensor produces a
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[`carla.CollisionEvent`](python_api.md#carlacollisioneventcarlasensordata)
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object for each collision registered
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| Sensor data attribute | Type | Description |
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| ---------------------- | ----------- | ----------- |
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@ -257,8 +267,9 @@ by this sensor.
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This sensor does not have any configurable attribute.
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This sensor produces a `carla.LaneInvasionEvent` object for each lane marking
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crossed by the actor
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This sensor produces a
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[`carla.LaneInvasionEvent`](python_api.md#carlalaneinvasioneventcarlasensordata)
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object for each lane marking crossed by the actor
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| Sensor data attribute | Type | Description |
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| ----------------------- | ----------- | ----------- |
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@ -1,12 +1,5 @@
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# Download
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### Stable [[Documentation](https://carla.readthedocs.io/en/stable/)]
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> The most tested and robust release out there!
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- [CARLA 0.8.2](https://github.com/carla-simulator/carla/releases/tag/0.8.2) -
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[[Blog post](http://carla.org/2018/04/23/release-0.8.2/)] - _Driving Benchmark_
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### Development [[Documentation](https://carla.readthedocs.io/en/latest/)]
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> These are the version of CARLA, more frequently updated and with the latest
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@ -21,3 +14,10 @@
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[[Blog post](http://carla.org/2018/06/18/release-0.8.4/)] - _Fixes And More!_
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- [CARLA 0.8.3](https://github.com/carla-simulator/carla/releases/tag/0.8.3) -
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[[Blog post](http://carla.org/2018/06/08/release-0.8.3/)] - _Now with bikes!_
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### Stable [[Documentation](https://carla.readthedocs.io/en/stable/)]
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> The most tested and robust release out there!
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- [CARLA 0.8.2](https://github.com/carla-simulator/carla/releases/tag/0.8.2) -
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[[Blog post](http://carla.org/2018/04/23/release-0.8.2/)] - _Driving Benchmark_
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@ -14,11 +14,11 @@ CARLA consists mainly of two modules, the **CARLA Simulator** and the **CARLA
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Python API** module. The simulator does most of the heavy work, controls the
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logic, physics, and rendering of all the actors and sensors in the scene; it
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requires a machine with a dedicated GPU to run. The CARLA Python API is a module
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that you can import into your Python scripts and provides and interface for
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controlling the simulator and retrieving data. Through this Python API you can
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for instance control any vehicle in the simulation, attach sensors to it, and
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read back the data these sensors generate. Most of the aspects of the simulation
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are accessible from our Python API, and more will be in future releases.
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that you can import into your Python scripts, it provides an interface for
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controlling the simulator and retrieving data. With this Python API you can, for
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instance, control any vehicle in the simulation, attach sensors to it, and read
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back the data these sensors generate. Most of the aspects of the simulation are
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accessible from our Python API, and more will be in future releases.
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<h2>How to run CARLA</h2>
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@ -61,15 +61,16 @@ waiting for a client app to connect and interact with the world.
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the simulator with the command-line argument `-carla-port=N`, the second
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port will be automatically set to `N+1`.
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Let's add now some live to the city, open a new terminal window and execute
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Let's add now some life to the city, open a new terminal window and execute
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```sh
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python spawn_npc.py -n 80
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```
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This adds 80 vehicles to the world driving in "autopilot" mode. Back to the
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simulator window we should see these vehicles driving around the city. They will
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keep driving randomly until we stop the script. Let's leave them there for now.
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With this script we are adding 80 vehicles to the world driving in "autopilot"
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mode. Back to the simulator window we should see these vehicles driving around
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the city. They will keep driving randomly until we stop the script. Let's leave
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them there for now.
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Now, it's nice and sunny in CARLA, but that's not a very interesting driving
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condition. One of the cool features of CARLA is that you can control the weather
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@ -91,3 +92,15 @@ This should open a new window with a 3rd person view of a car, you can drive
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this car with the WASD/arrow keys. Press 'h' to see all the options available.
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As you have noticed, we can connect as many scripts as we want to control the
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simulation and gather data. Even someone with a different computer can jump now
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into your simulation and drive along with you
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```sh
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python manual_control.py --host=<your-ip-address-here>
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```
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<br>
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Now that we covered the basics, in the next section we'll take a look at some of
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the details of the Python API to help you write your own scripts.
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@ -8,8 +8,9 @@ Install the build tools and dependencies
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```
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sudo add-apt-repository ppa:ubuntu-toolchain-r/test
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sudo apt-get update
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sudo apt-get install build-essential clang-5.0 lld-5.0 g++-7 ninja-build python python-pip python-dev libpng16-dev libtiff5-dev libjpeg-dev tzdata sed curl wget unzip autoconf libtool
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pip install --user setuptools nose2
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sudo apt-get install build-essential clang-5.0 lld-5.0 g++-7 cmake ninja-build python python-pip python-dev python3-dev python3-pip libpng16-dev libtiff5-dev libjpeg-dev tzdata sed curl wget unzip autoconf libtool
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pip2 install --user setuptools nose2
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pip3 install --user setuptools nose2
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```
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To avoid compatibility issues between Unreal Engine and the CARLA dependencies,
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@ -23,8 +24,6 @@ sudo update-alternatives --install /usr/bin/clang++ clang++ /usr/lib/llvm-5.0/bi
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sudo update-alternatives --install /usr/bin/clang clang /usr/lib/llvm-5.0/bin/clang 101
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```
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[cmakelink]: https://cmake.org/download/
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Build Unreal Engine
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-------------------
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@ -61,7 +60,7 @@ Note that the `master` branch contains the latest fixes and features, for the
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latest stable code may be best to switch to the `stable` branch.
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Now you need to download the assets package, to do so we provide a handy script
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that downloads and extracts the latest version (note that the package is >10GB,
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that downloads and extracts the latest version (note that this package is >3GB,
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this step might take some time depending on your connection)
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```sh
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@ -77,19 +76,21 @@ export UE4_ROOT=~/UnrealEngine_4.19
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You can also add this variable to your `~/.bashrc` or `~/.profile`.
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Now that the environment is set up, you can run make to run different commands
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Now that the environment is set up, you can use make to run different commands
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and build the different modules
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```sh
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make launch # Compiles CARLA and launches Unreal Engine's Editor.
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make package # Compiles CARLA and creates a packaged version for distribution.
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make launch # Compiles the simulator and launches Unreal Engine's Editor.
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make PythonAPI # Compiles the PythonAPI module necessary for running the Python examples.
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make package # Compiles everything and creates a packaged version able to run without UE4 editor.
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make help # Print all available commands.
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```
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Updating CARLA
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--------------
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Every new release of CARLA we release a new package with the latest changes in
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the CARLA assets. To download the latest version and recompile CARLA, run
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Every new release of CARLA, we release too a new package with the latest changes
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in the CARLA assets. To download the latest version and recompile CARLA, run
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```sh
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make clean
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@ -97,3 +98,25 @@ git pull
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./Update.sh
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make launch
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```
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- - -
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<h2>Assets repository (development only)</h2>
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Our 3D assets, models, and maps have also a
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[publicly available git repository][contentrepolink]. We regularly push latest
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updates to this repository. However, using this version of the content is only
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recommended to developers, as we often have work in progress maps and models.
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Handling this repository requires [git-lfs][gitlfslink] installed in your
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machine. Clone this repository to "Unreal/CarlaUE4/Content/Carla"
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```sh
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git lfs clone https://bitbucket.org/carla-simulator/carla-content Unreal/CarlaUE4/Content/Carla
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```
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It is recommended to clone with "git lfs clone" as this is significantly faster
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in older versions of git.
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[contentrepolink]: https://bitbucket.org/carla-simulator/carla-content
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[gitlfslink]: https://git-lfs.github.com/
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