A tool to generate synthetic autonomous driving datasets from arbitrary sensor setups from the carla simulator
numpy
carla (for instance segmentation 0.9.13 is required)
argparse
The script 'sensor_capture.py' allows to generate datasets of arbitrary size by collecting data from user defined sensor setups.
The data is synchronized, so the multiple sensor perspectives can be fused together.
To define a sensor setup proceed by creating a SimulationCfg object, and then adding new sensors to it
Note: Carla-simulator defines its coordinate frame as LEFT-HANDED:
- X is forward
- Y is right
- Z is up
^ z
|
|
| . x
|/
+-------> y
Hence, to define sensor locations with respect to the ego vehicle, you have to follow such guidelines.
Also, since data about all world objects location is gathered, simulation can slow down and to compensate for that
the simulation timestep should be set to 0.5 of the desired timestep, or less depending on your machine
cfg = SimulationCfg(sync_sim_stepsize=FIXED_DELTA_SECONDS)
camera_setup_blueprints = [SenseBP.RGBCAMERA, SenseBP.DEPTH, SenseBP.SEGMENTATION]
cfg.add_sensor('lidar',SenseBP.LIDAR,
x=-0.595, z=1.73,
sensor_options=lidar_sensor_options )
cfg.add_sensor('cam0',SenseBP.RGBCAMERA,x=-0.325, z=1.65, sensor_options=front_camera_attributes )After the definition above, simply call:
cfg.write_to_file()To generate the simulated sensor setup that will be automatically read when calling the script sensor_capture.py
-
define your sensor setup on the provided jupyter notebook or on your custom python script.
-
Make sure to give your preferred path where the simulation setup will be generated. In all cases, everything that concerns the simulation setup will fall under that directory,
in the folder 'simsetup'
Every attribute, pose or blueprint follows the naming convention provided by carla.
So to define your own custom sensor options, just referr to (https://carla.readthedocs.io/en/latest/python_api/) -
start CarlaUE4.sh
-
python3 sensor_capture.py --help to see available options
DO NOT FORGET TO GIVE THE PATH OF THE DIRECTORY WHERE THE GENERATED SIMSETUP IS LOCATED
(do not add 'simsetup' to the folder path, just give the directory path that contains it) -
data will be stored in a folder named according to the chosen simulation parameters
(customizable by the command line args of sensor_capture.py)
top level folder contains the directories *sensor_data *calibration
Sensor_data contains all data of the sensors in subfolders named with the provided sensor_label.
For each of these, one or more modalities, dependeing on how it has been set up, are available.
E.g.
- sensor_data
- front_camera
- rgbcamera
- depth
- segmentation
- lidar
- imu
- ...
- front_camera
Additionally, the following data is always provided:
- weather data
- world location of all actors, environment object and shape of other objects in tar.bz2 format
- trajectory of ego vehicle, with respect to the center of its bounding box with z=0
The calibration folder contains the pose of the sensor in vehicle coordinates.
- calibration
- front_camera.json
- imu.json
- ...
The origin of the vehicle coordinates is the center point of its bounding box at the ground level (z-coord == 0 at the contact point with the ground)
In case of camera sensors, the intrinsics matrix is also provided (can be found as calib_mtx in the files associated with cameras)
Although not always necessary, the reflection matrix needed to transform from unreal to camera coordinates is provided 'unreal_to_camcoordsys_reflection'
Cam coordinates, in the literature are always defined and treated as follows:
- Z front
- X right
- Y down
The transform from unreal engine coords to camera coords is illustrated as follows(credit to the drawing made in the examples/lidar_to_camera.py script) :
^ z . z
| /
| to: +-------> x
| . x |
|/ |
+-------> y v y