Repository for the development of the JHS Autonomous Race Car Club's ROS Jetson Car (TX2) with RealSense D400 series 3D camera

Check out system flowcharts

Master

Development

• Make sure you have installed git lfs on the jetson, controller, and development computer
  • Git LFS will allow you to easily keep ML models up to date across computers with git version control
  • https://git-lfs.github.com/
  • Install of git lfs
    • This will need to be installed by source on the Jetson (DO NOT follow the instructions exactly. See below)
      • Get latest version of Go for Linux ARMv8 and run the extract command
      • Make sure that the $GOPATH is set to home/$USER/gocode and that Go is version 1.8 or higher go version
      • go get github.com/git-lfs/git-lfs
      • sudo cp ~/gocode/bin/git-lfs /usr/local/bin
      • git lfs install
• Connect the nvidia jetson to the same network as the controller and make sure that you have an internet connection
• Before setup make sure to sudo apt-get update and sudo apt-get upgrade
• Clone/download the repository on both the jetson and the controller
• See the Nvidia Jetson Setup section
• If running on a desktop computer / controller without realsense packages installed run rosdep install --from-paths src --ignore-src -r -y and catkin_make -DCATKIN_BLACKLIST_PACKAGES="realsense2_camera" in workspace
  • If you want to run the realsense2 ROS node on a desktop / controller see Librealsense Setup (D415/D435) section and use the realsense2_camera pkg in the drivers folder by copying it into a local catkin_ws (do not push this change as it will brake the jetson side)
• On the jetson run rosdep install --from-paths src --ignore-src -r -y and catkin_make in the jetsoncar_ws
• Run the controller.sh or jetson.sh scripts to get the devices setup and communicating with each other OR run export ROS_MASTER_URI=http://tegra-ubuntu.local:11311 and export ROS_HOSTNAME=hostname_of_machine.local
  • It is highly recommended that you run the controller.sh and jetson.sh scripts on their respective devices
• See DualShock 4 Controller Setup section for setting up the controller with the correct platform

• When running the jetson car with WiFi make sure that WiFi power saving mode is off. sudo iw dev wlan0 set power_save off and check with sudo iw dev wlan0 get power_save. This needs to be done each time the WiFi reconnects to a new network.

• Go through Librealsense Setup (demo/viewing tools are not required)
• cd ~/Desktop/jetson_car/jetsoncar_ws
• catkin_make (even if this fails)
• soruce devel/setup.bash
• rosdep install racecar_description
• Install Tensor Flow (download GPU version if you have one)
  • sudo pip install tensorflow-gpu
  • If running python program fails with error ImportError: libcudnn.so.7: cannot open shared object file: No such file or directory see [this](https://stackoverflow.com/questions/41991101/importerror-libcudnn-when-running-a-tensorflow-program but make sure to download proper version of cuDNN) version 9.0
• Install Keras sudo pip install keras
• sudo pip install h5py

• Install the intel realsense 2 SDK (needed to catkin_make the catkin workspace if using the simulation of the physical sensor)
• https://github.com/IntelRealSense/librealsense/blob/master/doc/distribution_linux.md
  • sudo apt-key adv --keyserver keys.gnupg.net --recv-key C8B3A55A6F3EFCDE || sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-key C8B3A55A6F3EFCDE
  • sudo add-apt-repository "deb http://realsense-hw-public.s3.amazonaws.com/Debian/apt-repo xenial main" -u
  • sudo apt-get update
  • sudo apt-get install librealsense2 librealsense2-dev librealsense2-dbg
  • sudo apt-get install librealsense2-dkms For demo
  • sudo apt-get install librealsense2-utils Tools for viewing physical camera data (See /usr/bin/realsense-viewer)
• When ready plug in the realsense camera and test it using the /usr/bin/realsense-viewer
  • If needed update the firmware using the instruction found here

• Follow Nvidia Jetson setup and run as user nvidia (password nvidia)
• Check to make sure you are running L4T version 28.2.1 for use with convenience scripts
  • If you version is off download jetpack here
  • Follow this tutorial on reflashing the Jetson TX2
• make sure you have cloned https://github.com/JHS-ARCC-Club/jetson_car.git to Desktop
• Run the following scripts in this order (in scripts/jetsonhacks): installLibrealsense.sh, buildPatchedKernal.sh, installROS.sh
• Install USB driver for some arduino nano
• Run the /scripts/jetson.sh script to setup network
• Setup pip and download keras and tensorflow for python
  • sudo apt-get install -y python-pip
  • pip install keras
  • sudo pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/jp33 tensorflow-gpu
• The following are done in the jetson.sh script but provided here for reference
  • sudo usermod -a -G dialout $USER to give proper permissions to the USB peripherals
  • Add a udev rule for i2c-1 so that the IMU interface program can access it
    • Go to /etc/udev/rules.d
    • Add a file called 50-i2c.rules
    • Add the line ACTION=="add", KERNEL=="i2c-[0-1]*", MODE="0666" to the 50-i2c.rules file
    • See this for additional information

• sudo apt-get install ros-kinetic-rtabmap-ros ros-kinetic-robot-localization ros-kinetic-pointcloud-to-laserscan ros-kinetic-depthimage-to-laserscan

• Connect to the controller by pressing and holding the playstation button and the share button until the light begins flashing white
• Go into Bluetooth settings and add the controller named wireless controller
• Test the connection by making sure the /dev/input/js0 file exits
• Run the joy_node ROS node and echo the /joy topic to confirm data is being sent properly from the controller to the computer

• Install bluez driver sudo apt-get install bluez
• https://github.com/macunixs/dualshock4-pi/blob/master/README.md
• Follow the steps for Ubutnu 16.04 above

See the travis.yaml for running install/make tests and verifying a proper build.

• ROS - Modern robot framework for running software
• librealsense - Intel Realsense library for D415/D435
• Git LFS - Manage and store trained AI models
• Travis CI - Perform build tests

• Michael Equi - Initial work

See also the list of contributors who participated in this project.

• README Tempalte source https://gist.github.com/PurpleBooth/109311bb0361f32d87a2