EE346_FinalLab_JefferySherlock

Description

This project is the final lab in course EE346 Mobile Robot Navigation and Control, based on robot turtlebot burger and computer operating system ubuntu18.04.

This project implemented camera calibration, aruco tag detection, automatic navigation, lane following and other functions.

The details are contained in the file Final_Report.pdf. The following tutorial will guide you on how to use these programs.

The main programs are in directory turtlebot3_autorace2020/turtlebot3_autorace_traffic_light

Camera calibration

Calibrating the camera is very important for autonomous driving. The following describes how to simply calibrate the camera step by step.

1. Launch roscore on PC

   roscore
   

2. Trigger the camera on turtlebot

   roslauch turtlebot3_autorace_traffic_light_camera turtlebot3_autorace_camera_pi.launch
   

3. Execute rqt_image_view on PC

   rqt_image_view
   

4. Select /camera/image/compressed (or /camera/image/) topic on the check box

5. Run a intrinsic camera calibration launch file on PC

   export AUTO_IN_CALIB=calibration
   export GAZEBO_MODE=false
   roslaunch turtlebot3_autorace_traffic_light_camera turtlebot3_autorace_intrinsic_camera_calibration.launch
   

6. Use the checkerboard to calibrate the camera, and click CALIBRATE. After calibration, click Save to save the intrinsic calibration data.

7. calibrationdata.tar.gz folder will be created at /tmp folder. Extract calibrationdata.tar.gz folder, and open ost.yaml. Copy and paste the data from ost.yaml to camerav2_320x240_30fps.yaml.

Aruco code detection

1. Launch roscore on PC

   roscore
   

2. Trigger the camera on turtlebot

   roslauch turtlebot3_autorace_traffic_light_camera turtlebot3_autorace_camera_pi.launch
   

3. Run a intrinsic camera calibration launch file on PC

   export AUTO_IN_CALIB=action
   export GAZEBO_MODE=false
   roslaunch turtlebot3_autorace_traffic_light_camera turtlebot3_autorace_intrinsic_camera_calibration.launch
   

4. Run a python file to detect the Aruco code

   # if the file can not be executed, please change the permission by
   # chmod u+x speaker.py
   rosrun turtlebot3_autorace_traffic_light_detect speaker.py

Navigation

Before starting the program, you need to put turtlebot on P1.

1. Launch roscore on PC

   roscore
   

2. If the bringup is not running on turtlebot, launch the bringup on turtlebot

3. Launch the navigation

   export TURTLEBOT3_MODEL=burger
   roslaunch turtlebot3_navigation turtlebot3_navigation.launch map_file:=$HOME/map.yaml
   

4. Run a python file to publish goals to turtlebot

   rosurn turtlebot3_navigation pub_navga_dst.py
   

Lab7

1. Launch roscore on PC

   roscore
   

2. Trigger the camera on turtlebot

   roslauch turtlebot3_autorace_traffic_light_camera turtlebot3_autorace_camera_pi.launch
   

3. Run a intrinsic camera calibration launch file on PC

   export AUTO_IN_CALIB=action
   export GAZEBO_MODE=false
   roslaunch turtlebot3_autorace_traffic_light_camera turtlebot3_autorace_intrinsic_camera_calibration.launch
   

4. Launch Bringup on turtlebot

5. Run the program to finish "lane following"

   rosrun turtlebot3_autorace_traffic_light_detect follow_lane_final3.py
   

6. Run the program to detect aruco tag

   rosrun turtlebot3_autorace_traffic_light_detect speaker.py
   

Demo Video

https://www.bilibili.com/video/BV1At4y1p7kJ?share_source=copy_web