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TurtleBot3 AutoRace 2019

The AutoRace 2019 is a competition for autonomous driving robot platforms. To provide various conditions for robot application development, the game gives as less structural regulation as possible. Entire contents are opened in software (source codes for referee system) wise and hardware (stp / dwg files of game map) wise.

Whole robots and even the referee system in the field is run by ROS, so it would support to make many kinds of contents further. Get opened source of what is participated in each competitions!

WARNING: Be aware that you are reading the contents made in the pase. If you are joining AutoRace 2020, see TurtleBot3 AutoRace 2020.

TurtleBot3 AutoRace Tutorials

  • Source code for the AutoRace tutorial: turtlebot3_autorace packages

  • Tutorial 1: Traffic Light

  • Tutorial 2: Lane Tracking

  • Tutorial 3: Parking

  • Tutorial 4: Node Optimization

  • Tutorial 5: Level Crossing

  • Tutorial 6: Tunnel

Tutorials: 1. Requirements

  • TurtleBot3 Burger

    • ROS and dependent ROS packages needs to be installed in the robot
    • All functions of TurtleBot3 Burger which is described in TurtleBot3 e-Manual needs to be tested before running TurtleBot3 Auto source code

  • Remote PC (Laptop, Desktop, etc.)

    • ROS and dependent ROS packages needs to be installed in the computer
    • All functions of TurtleBot3 Burger which is described in TurtleBot3 e-Manual needs to be tested before running TurtleBot3 Auto source code

  • Add-ons on TurtleBot3 Burger

    • Raspberry Pi Camera Type G (Fisheye Lens) : Available Here
      • See Features of 4 screw holes in the page very carefully before mounting on the frame of any conductive materials
    • Raspberry Pi Camera Mount

  • Track structure and Accessories, such as Traffic Signs, Traffic Lights, and other objects.

Tutorials: 2. Install AutoRace package

Remote PC & TurtleBot SBC Open terminal, then install AutoRace package.

$ cd ~/catkin_ws/src/
$ git clone https://github.com/ROBOTIS-GIT/turtlebot3_autorace.git
$ cd ~/catkin_ws && catkin_make

Tutorials: 3. Install Additional Dependent Packages

Remote PC & TurtleBot SBC Open new terminal, then enter

$ sudo apt-get install ros-kinetic-image-transport ros-kinetic-cv-bridge ros-kinetic-vision-opencv python-opencv libopencv-dev ros-kinetic-image-proc

Tutorials: 4. Calibration

Tutorials: 4.1. Camera Imaging Calibration

  1. Remote PC Open new terminal, then enter

    $ roscore

  2. TurtleBot SBC Open new terminal, then enter

    $ roslaunch turtlebot3_autorace_camera turtlebot3_autorace_camera_pi.launch

  3. Remote PC Open new terminal, then enter

    $ rqt_image_view

    then, click /camera/image/compressed or /camera/image/ topic in the select box. If everything works fine, the screen should show you the view from the robot.

  4. Remote PC Open new terminal, then enter

    $ rosrun rqt_reconfigure rqt_reconfigure

    then, click camera, adjust the parameter value that makes the camera show clean, enough bright image to you. After that, overwrite each values on to the turtlebot3_autorace_camera/calibration/camera_calibration/camera.yaml. This will make the camera set its parameters as you set here from next launching.

Tutorials: 4.2. Intrinsic Camera Calibration

  1. Remote PC Open new terminal, then enter

    $ roscore

  2. TurtleBot SBC Open new terminal, then enter

    $ roslaunch turtlebot3_autorace_camera turtlebot3_autorace_camera_pi.launch

  3. Remote PC Print checkerboard for camera calibration on A4 size paper. The checkerboard is in turtlebot3_autorace_camera/data/checkerboard_for_calibration.pdf. See Calibration manual and modify the parameter value written in turtlebot3_autorace_camera/launch/turtlebot3_autorace_intrinsic_camera_calibration.launch.

  4. Remote PC Open new terminal, then enter

    $ export AUTO_IN_CALIB=calibration
$ roslaunch turtlebot3_autorace_camera turtlebot3_autorace_intrinsic_camera_calibration.launch

  5. Remote PC After finishing the calibration, intrinsic camera calibration file will be saved in turtlebot3_autorace_camera/calibration/intrinsic_calibration/camerav2_320x240_30fps.yaml.

Tutorials: 4.3. Extrinsic Camera Calibration

  1. Remote PC Open new terminal, then enter

    $ roscore

  2. TurtleBot SBC Open new terminal, then enter

    $ roslaunch turtlebot3_autorace_camera turtlebot3_autorace_camera_pi.launch

  3. Remote PC Open new terminal, then enter

    $ export AUTO_IN_CALIB=action
$ roslaunch turtlebot3_autorace_camera turtlebot3_autorace_intrinsic_camera_calibration.launch

  4. Remote PC Open new terminal, then enter

    $ export AUTO_EX_CALIB=calibration
$ roslaunch turtlebot3_autorace_camera turtlebot3_autorace_extrinsic_camera_calibration.launch

  5. Remote PC Open new terminal, then enter

    $ rqt

    clicking plugins -> visualization -> Image view on the top of the screen will make extra monnitor for camera view. Make 2 extra monitor in the rqt plate by following it. Then, choose /camera/image_extrinsic_calib/compressed and /camera/image_projected_compensated topics on each of extra monitors. If everything works fine, one of the screen will show the image with red rectangle, and other one will show the ground projected view (bird’s eye view) which is based on the one another.

  6. Remote PC Open new terminal, then enter

    $ rosrun rqt_reconfigure rqt_reconfigure

    then, adjust the parameter value in /camera/image_projection and /camera/image_compensation_projection that carries out visual modifications on the image. The parameter image_projection will change the shape of the red rectangle of /camera/image_extrinsic_calib/compressed image. Intrinsic camera calibration will transform the image surrounded by the red rectangle, and will show the image that looks from over the lane. After that, overwrite each values on to the yaml files in turtlebot3_autorace_camera/calibration/extrinsic_calibration/. This will make the camera set its parameters as you set here from next launching.

Tutorials: 4.4. Settings for Recognition

Until now, all the preprocess of image must have been tested.

  1. Remote PC Open new terminal, then enter

    $ roscore

  2. TurtleBot SBC Open new terminal, then enter

    $ roslaunch turtlebot3_autorace_camera turtlebot3_autorace_camera_pi.launch

  3. Remote PC Open new terminal, then enter

    $ export AUTO_IN_CALIB=action
$ roslaunch turtlebot3_autorace_camera turtlebot3_autorace_intrinsic_camera_calibration.launch

  4. Remote PC Open new terminal, then enter

    $ export AUTO_EX_CALIB=action
$ roslaunch turtlebot3_autorace_camera turtlebot3_autorace_extrinsic_camera_calibration.launch

    From now, the following descriptions will mainly adjust feature detector / color filter for object recognition. Every adjustment after here is independent to each other’s process. However, to make sure, if you want to adjust each parameters in series, finish every adjustment perfectly, then continue to next.

Tutorials: 5. Missions

Tutorials: 5.1 Lane Detection

  1. Put the robot on the lane. If you placed the robot correctly, yellow line should be placed on the left side of the robot, and of course, white line should be placed on the right side of the robot. Make sure that turtlebot3_robot node of turtlebot3_bringup package is not yet launched. If it is on running, the robot will suddenly runs on the track.

  2. Remote PC Open new terminal, then enter

    $ export AUTO_DT_CALIB=calibration
$ roslaunch turtlebot3_autorace_detect turtlebot3_autorace_detect_lane.launch

  3. Remote PC Open new terminal, then enter

    $ rqt

    clicking plugins -> visualization -> Image view on the top of the screen will make extra monnitor for camera view. Make 3 extra monitor in the rqt plate by following it. Then, choose /detect/image_yellow_lane_marker/compressed, /detect/image_lane/compressed and /detect/image_white_lane_marker/compressed topics on each of extra monitors. If everything works fine, left and right screen will show the filtered image of the yellow line and the white line, and the center screen will show the lane of where the robot should go. In the calibration mode, left and right screen will show white, and the center screen may show abnormal result. From here, you should adjust the filter parameters to show up correct lines and the direction.

  4. Remote PC Open new terminal, then enter

    $ rosrun rqt_reconfigure rqt_reconfigure

    then, adjust the parameter value in /camera/image_projection and /camera/image_compensation_projection that carries out visual modifications on the image. The parameter image_projection will change the shape of the red rectangle of /camera/image_extrinsic_calib/compressed image. Intrinsic camera calibration will transform the image surrounded by the red rectangle, and will show the image that looks from over the lane. After that, overwrite value on to the lane.yaml file in turtlebot3_autorace_detect/param/lane/. This will make the camera set its parameters as you set here from next launching.

    TIP: Calibration process of line color filtering is sometimes so-so difficult because of your physical environment which includes the luminance of light in the room, etc. Hence, you should have patience to carry out this procedure. To make everything quickly, put the value of turtlebot3_autorace_detect/param/lane/lane.yaml on the reconfiguration parameter, then start calibration. Calibrate hue low - high value at first. (1) Hue value means the color, and every colors, like yellow, white, have their own region of hue value (refer to hsv map). Then calibrate saturation low - high value. (2) Every colors have also their own field of saturation. Finally, calibrate the lightness low - high value. (3) In the source code, however, have auto-adjustment function, so calibrating lightness low value is meaningless. Just put the lightness high value to 255. Clearly filtered line image will give you clear result of the lane.

  5. Remote PC After overwriting the calibration file, close rqt_rconfigure and turtlebot3_autorace_detect_lane, then enter

    $ export AUTO_DT_CALIB=action
$ roslaunch turtlebot3_autorace_detect turtlebot3_autorace_detect_lane.launch

  6. Check if the results come out well by entering

    Remote PC

    $ roslaunch turtlebot3_autorace_control turtlebot3_autorace_control_lane.launch

    TurtleBot SBC

    $ roslaunch turtlebot3_bringup turtlebot3_robot.launch

    After entering these commands, the robot will kick-off to run.

Tutorials: 5.2. Traffic Sign

  1. Traffic sign detection needs some pictures of the traffic sign. Take their pictures by using rqt_image_view node and edit their size, shape by any of photo editor available in linux. The node finds the traffic sign with SIFT algorithm, so if you want to use your customized traffic signs ( which is not introduced in the autorace_track), just be aware of More edges in the traffic sign gives better recognition results from SIFT.

  2. Put the robot on the lane. At this time, the traffic sign should be placed to where the robot can see it easily. Make sure that turtlebot3_robot node of turtlebot3_bringup package is not yet launched. If it is on run, the robot may suddenly run on the track.

  3. Remote PC Open new terminal, then enter

    $ rqt_image_view

    then, click /camera/image_compensated topic in the select box. If everything works fine, the screen should show you the view from the robot.

  4. Remote PC Take the picture by alt + print screen, edit the captured with your preferred photo editor. After that, place the picture to where the turtlebot3_autorace package you’ve placed /turtlebot3_autorace/turtlebot3_autorace_detect/file/detect_sign/ and rename it as you want. (Although, you should change the file name written in the source detect_sign.py, if you want to change the default file names.)

  5. Remote PC Open new terminal, then enter

    $ roslaunch turtlebot3_autorace_detect turtlebot3_autorace_detect_sign.launch

  6. Remote PC Open new terminal, then enter

    $ rqt_image_view

    then, click /detect/image_traffic_sign/compressed topic in the select box. If everything works fine, the screen will show the result of traffic sign detection, if it succeeds to recognize it.

Tutorials: 5.3. Traffic Light

  1. Put the robot on the lane. If you placed the robot correctly, yellow line should be placed on the left side of the robot, and of course, white line should be placed on the right side of the robot. Make sure that turtlebot3_robot node of turtlebot3_bringup package is not yet launched. If it is on running, the robot will suddenly runs on the track.

  2. Remote PC Open new terminal, then enter

    $ export AUTO_DT_CALIB=calibration
$ roslaunch turtlebot3_autorace_detect turtlebot3_autorace_detect_traffic_light.launch

  3. Remote PC Open new terminal, then enter

    $ rqt

    clicking plugins -> visualization -> Image view on the top of the screen will make extra monnitor for camera view. Make 3 extra monitor in the rqt plate by following it. Then, choose /detect/image_red_light, /detect/image_yellow_light, /detect/image_green_light and /detect/image_traffic_light topics on each of extra monitors. If everything works fine, three screen will show the filtered image of the red / yellow / green light, and the other one will show the recognized color with short string. In the calibration mode, three screen will show white, and the other screen may show plain result. From here, you should adjust the filter parameters to show up correct lines and the direction.

  4. Remote PC Open new terminal, then enter

    $ rosrun rqt_reconfigure rqt_reconfigure

    then, adjust the parameter value in /detect_traffic_light. Changing the value of color filter will show the change of filtered view on each color’s screen. After that, overwrite value on to the traffic_light.yaml file in turtlebot3_autorace_detect/param/traffic_light/. This will set its parameters as you set here from next launching.

    Tip: same as [5.1 Lane Detection][lane_detection]

  5. Remote PC After overwriting the calibration file, close rqt_rconfigure and turtlebot3_autorace_detect_traffic_light, then enter

    $ export AUTO_DT_CALIB=action
$ roslaunch turtlebot3_autorace_detect turtlebot3_autorace_detect_traffic_light.launch

  6. Use rqt_image_view node, and check if the results come out well

Tutorials: 5.4. Parking Lot

  1. Parking needs only one preparation, traffic sign recognition.

  2. Place the dummy robot on either of parking lot.

  3. Place the robot on the lane appropriately.

Tutorials: 5.5. Level Crossing

  1. Level Crossing finds 3 red rectangles on the level, and calculates whether the level is opened or closed, and how much near the robot is come.

  2. Put the robot on the lane correctly. Then, bring the robot in front of closed level.

  3. Remote PC Open new terminal, then enter

    $ export AUTO_DT_CALIB=calibration
$ roslaunch turtlebot3_autorace_detect turtlebot3_autorace_detect_level.launch

  4. Remote PC Open new terminal, then enter

    $ rqt

    clicking plugins -> visualization -> Image view on the top of the screen will make extra monnitor for camera view. Make 3 extra monitor in the rqt plate by following it. Then, choose /detect/image_level_color_filtered and /detect/image_level topics on each of extra monitors. If everything works fine, three screen will show the filtered image of the red rectangles, and another one will draw a line which connects the rectangles. In the calibration mode, a screen will show white, and the other screen may show plain result. From here, you should adjust the filter parameters to show up correct lines and the direction.

  5. Remote PC Open new terminal, then enter

    $ rosrun rqt_reconfigure rqt_reconfigure

    then, adjust the parameter value in /detect_level. Changing the value of color filter will show the change of filtered view on each color’s screen. After that, overwrite value on to the level.yaml file in turtlebot3_autorace_detect/param/level/. This will set its parameters as you set here from next launching.

    Tip: same as [5.1 Lane Detection][lane_detection]

  6. Remote PC After overwriting the calibration file, close rqt_rconfigure and turtlebot3_autorace_detect_level, then enter

    $ export AUTO_DT_CALIB=action
$ roslaunch turtlebot3_autorace_detect turtlebot3_autorace_detect_level.launch

  7. Use rqt_image_view node, and check if the results come out well

Tutorials: 5.6. Tunnel

  1. Tunnel node will bring you from the entrance to the exit by using turtlebot3 navigation package. What you should calibrate is mapping the tunnel (or if you are using the autorace track as it is, you don’t need to modify it by yourself) and check the pose of how the robot should be posed right before it comes out from tunnel (this is also unnecessary when you are using the default map).

  2. Remote PC Check the pose of exit on RViz, while the SLAM or Navigation package is running. After that, overwrite value on to the detect_tunnel.py file line 144

Tutorials: 6. Run Autonomous Driving

  1. From now, all the related nodes will be run in action mode. Close all ROS-related programs and terminals on Remote PC and TurtleBot SBC, if some were not closed yet. Then, put the robot on the lane correctly.

  2. Remote PC Open new terminal, then enter

    $ roscore

  3. TurtleBot SBC Open new terminal, then enter

    $ roslaunch turtlebot3_bringup turtlebot3_robot.launch

  4. TurtleBot SBC Open new terminal, then enter

    $ roslaunch turtlebot3_autorace_camera turtlebot3_autorace_camera_pi.launch

  5. Remote PC Open new terminal, then enter

    $ export AUTO_IN_CALIB=action
$ roslaunch turtlebot3_autorace_camera turtlebot3_autorace_intrinsic_camera_calibration.launch

  6. Remote PC Open new terminal, then enter

    $ export AUTO_EX_CALIB=action
$ export AUTO_DT_CALIB=action
$ export TURTLEBOT3_MODEL=burger
$ roslaunch turtlebot3_autorace_core turtlebot3_autorace_core.launch

  7. Remote PC Open new terminal, then enter

    $ rostopic pub -1 /core/decided_mode std_msgs/UInt8 "data: 2"

    turtlebot3_autorace_core will control all system in the package (open and close the launch, nodes in the package.)

TurtleBot3 AutoRace Online Competition

AutoRace with Gazebo

The AutoRace is provided by Gazebo. We created a environment TurtleBot3 AutoRace 2017 in R-BIZ Challenge.

  • Recommended specification
CPU Intel Core i5 / 2 GHz Dual Core Processor
RAM 4GB
Storage 20Gb of free hard drive space
GPU NVIDIA GeForce GTX 9 series

WARNING: Do not confuse your real camera calibration configure files and Gazebo calibration configure files.

NOTE: The turtlebot3_autorace package requires turtlebot3_simulations package as a prerequisite. If you did not install it in the Installation TurtleBot3 Simulations, install it first.

  1. Remote PC Run AutoRace Gazebo. You can see the AutoRace 2017 map in Gazebo.

    $ roslaunch turtlebot3_gazebo turtlebot3_autorace.launch

  2. Remote PC Run Mission launch. You can see Traffic Light, Parked TurtleBot3 and Toll Gate in Gazebo. When TurtleBot3 approaches the mission area, they operate automatically.

    $ roslaunch turtlebot3_gazebo turtlebot3_autorace_mission.launch

  3. Remote PC Run AutoRace launch. If you want to run AutoRace in real, you have to calibrate your camera.

    $ export GAZEBO_MODE=true
$ export AUTO_IN_CALIB=action
$ roslaunch turtlebot3_autorace_camera turtlebot3_autorace_intrinsic_camera_calibration.launch

  4. Remote PC Open new terminal, then enter

    $ export AUTO_EX_CALIB=action
$ export AUTO_DT_CALIB=action
$ export TURTLEBOT3_MODEL=burger
$ roslaunch turtlebot3_autorace_core turtlebot3_autorace_core.launch

  5. Remote PC Open new terminal, then enter

    $ rostopic pub -1 /core/decided_mode std_msgs/UInt8 "data: 2"

  • Video : AutoRace with Gazebo

NOTE: This feature is available for Kinetic only.

NOTE: This feature is available for Kinetic only.

NOTE: This feature is available for Kinetic only.

NOTE: This feature is available for Kinetic only.