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CARLA-Kitti Setup & Installation Guide

A step-by-step guide for installing CARLA-KITTI data collector to generate synthetic data and export it to a KITTI data format.

Some of the features of the "CARLA-KITTI data collector" include:

  • Visualization of 2D/3D bounding boxes around agents in the simulation
  • Visualization of LiDAR point cloud (either "ray_cast or "blickfeld" ) of the world in the simulation
  • Support for the autopilot mode of traversing the city while collecting data

1) CARLA Prerequisites

Before installing CARLA, we need to install the PyGame package. However, before installing PyGame, we need to install the following packages at the (Linux) system-level:

$ sudo apt-get install python3-pip python-dev libsdl-image1.2-dev
$ sudo apt-get install libsdl-mixer1.2 libsdl-mixer1.2-dev libsdl-ttf2.0 libsdl-ttf2.0-dev
$ sudo apt-get install libportmidi-dev libfreetype6-dev

2) Installing Anaconda

Next we will have to install a Python distribution. Here, we will prefer the Anaconda distribution.

$ wget -c https://repo.anaconda.com/archive/Anaconda3-2020.07-Linux-x86_64.sh
$ bash Anaconda3-2020.07-Linux-x86_64.sh

3) Conda environment

For encapsulating the required packages in a self-contained environment, we will use a conda environment and install the Python packages required for running CARLA.

$ conda create -n carla python=3.8
$ conda activate carla

Now, we are ready to install PyGame in the conda env that we just created:

$ pip install -U pygame==2.0

We also need some additional but necessary packages:

$ conda install -c conda-forge numpy matplotlib opencv scikit-learn scipy
$ conda install -c open3d-admin open3d pyyaml addict pandas plyfile networkx

4) Installing CARLA

Now, we are ready to install CARLA from the pre-compiled binaries

After downloading, extract the tar.gz files to the desired location. For example, /home/mario/CARLA_0.9.10.

5) Running CARLA

First, to make the CARLA python distribution binary visible in PYTHONPATH, define the following environment variables in .bashrc or .zshrc:

CARLAHOME="/home/mario/CARLA_0.9.10"
CARLAPYTHONDIST="$CARLAHOME/PythonAPI/carla/dist/carla-0.9.10-py3.7-linux-x86_64.egg"
export PYTHONPATH=$CARLAPYTHONDIST

Since we will also run CARLA simulator to collect (training) data using carla-kitti-data-collector, we need the following paths as well:

CARLAPYAPI="$CARLAHOME/PythonAPI"
export PYTHONPATH=$PYTHONPATH:$CARLAPYAPI:"$CARLAPYAPI/carla":"$CARLAPYAPI/carla/agents":"$CARLAPYAPI/examples"

Another alias needed by carla-kitti-data-collector/data_collector.py:

CARLA_ROOT=$CARLAHOME
export CARLA_ROOT

Compile the changes with the following command or log out the system and login again.

$ source ~/.bashrc

Now, run the CARLA server:

$ cd ~/carla_0.9.10
$ bash ImportAssets.sh
$ bash CarlaUE4.sh -benchmark -fps=10 -quality-level=Epic -opengl

If everything works out fine, you will see a CarlaUE4 window showing buildings, road, trees, street lamps, etc.

6) Running the data collector

Activate the environment and run the data collector python module:

$ conda activate carla
$ python -m data_collector --loop

This will show a PyGame window with default number of actors in the simulation. Check out data_collector.py for further parameters.

7) Visualization

i) To visualize 3D bounding boxes around agents (i.e., vehicles, pedestrians, cyclists, etc.), use the following flag:

$ python -m data_collector --loop --vis_boxes3d

3D Bounding Box Visualization

ii) To visualize LiDAR point cloud of the simulation, use the following flag:

$ python -m data_collector --loop --vis_lidar

LiDAR visualization

For more help with the command-line arguments, use:

$ python data_collector.py --help

usage: data_collector.py [-h] [-v] [--host H] [-p P] [--res WIDTHxHEIGHT] [--filter PATTERN] [--gamma GAMMA] [-l] [-s SEED] [--vis_lidar] [--vis_boxes3d] [--vis_boxes2d] [--vis_ry] [--vis_alpha][--steps_between_recordings STEPS_BETWEEN_RECORDINGS] [--lidar_data_format LIDAR_DATA_FORMAT] [--distance_since_last_recording DISTANCE_SINCE_LAST_RECORDING][--num_recordings_before_reset NUM_RECORDINGS_BEFORE_RESET] [--num_empty_frames_before_reset NUM_EMPTY_FRAMES_BEFORE_RESET] [--lidars LIDARS [LIDARS ...]][--lidar_range LIDAR_RANGE] [--save_data] [--phase PHASE] [--output_dir OUTPUT_DIR] [--fps FPS] [-a]

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         Print debug information
  --host H              IP of the host server (default: 127.0.0.1)
  -p P, --port P        TCP port to listen to (default: 2000)
  --res WIDTHxHEIGHT    Window resolution (default: 1280x720)
  --filter PATTERN      Actor filter (default: "vehicle.*")
  --gamma GAMMA         Gamma correction of the camera (default: 2.2)
  -l, --loop            Sets a new random destination upon reaching the previous one (default: False)
  -s SEED, --seed SEED  Set seed for repeating executions (default: None)
  --vis_lidar           Whether or not to visualize lidar point clouds.
  --vis_boxes3d         Whether or not to visualize 3D bounding boxes for agents.
  --vis_boxes2d         Whether or not to visualize 2D bounding boxes for agents.
  --vis_ry              Whether or not to visualize rotation ry of agents around Y-axis.
  --vis_alpha           Whether or not to visualize the object observation angle (alpha) of the vehicle with camera.
  --steps_between_recordings STEPS_BETWEEN_RECORDINGS
                        How many frames to wait between each capture of screen, bounding boxes and lidar.
  --lidar_data_format LIDAR_DATA_FORMAT
                        Lidar can be saved in bin to comply to kitti, or the standard .ply format
  --distance_since_last_recording DISTANCE_SINCE_LAST_RECORDING
                        How many meters the car must drive before a new capture is triggered.
  --num_recordings_before_reset NUM_RECORDINGS_BEFORE_RESET
                        How many datapoints to record before resetting the scene.
  --num_empty_frames_before_reset NUM_EMPTY_FRAMES_BEFORE_RESET
                        How many frames to render before resetting the environment. For example, the agent may be stuck
  --lidars LIDARS [LIDARS ...]
                        List of lidar types used for visualization and data collection.Available options are "ray_cast" and "blickfeld"
  --lidar_range LIDAR_RANGE
                        Max render depth in meters
  --save_data           Whether or not to save training data
  --phase PHASE         Phase can be one of (training|val|test)
  --output_dir OUTPUT_DIR
                        Path to output directory
  --fps FPS             Simulation FPS
  -a, --autopilot       Enable default autopilot. If not, a behavior agent will be used