3. Docker Setup for ROS 1

3.1. Set Up Docker Environment on the Target

In the ROS 1 Docker container environment, ROS Noetic, necessary libraries and tools are installed.

1. To generate the scripts for building and running a Docker image for ROS 1 Noetic:

   root@am6x-sk:~/j7ros_home$ make scripts ROS_VER=1
   

   Make sure that two bash scripts, docker_build_ros1.sh and docker_run_ros1.sh, are generated.

2. To build the Docker image:

   root@am6x-sk:~/j7ros_home$ ./docker_build_ros1.sh
   

   This step may take some time depending on the network speed. Once the “docker build” is completed, you can check the resulting Docker image with docker images.

3. To start/run the Docker container:

   root@am6x-sk:~/j7ros_home$ ./docker_run_ros1.sh
   

   It is important to use docker_run_ros1.sh script to start a Docker container since the script includes all the necessary settings to leverage all the cores and hardware accelerators of the TI Processor.

4. To build the ROS applications, inside the Docker container:

   root@j7-docker:~/j7ros_home/ros_ws$ catkin_make --source /opt/robotics_sdk/ros1 --force-cmake
   # TDA4VM: In case catkin_make fails due to limited memory, "-j1" option can be added as follows
   root@j7-docker:~/j7ros_home/ros_ws$ catkin_make -j1 --source /opt/robotics_sdk/ros1 --force-cmake
   root@j7-docker:~/j7ros_home/ros_ws$ source devel/setup.bash
   

3.2. Set Up Docker Environment on the Remote PC for Visualization

You can choose any folder, but init_setup.sh script sets up ${HOME}/j7ros_home as the working directory.

1. To generate the scripts for building and running a Docker image for ROS 1 Noetic:

   user@pc:~/j7ros_home$ make scripts ROS_VER=1
   

   If the Ubuntu PC uses an Nvidia GPU driver, please add one more argument GPUS=y as follows:

   user@pc:~/j7ros_home$ make scripts ROS_VER=1 GPUS=y
   

   Make sure that two bash scripts, docker_build_ros1.sh and docker_run_ros1.sh, are generated.

2. To build the ROS 1 Docker image:

   user@pc:~/j7ros_home$ ./docker_build_ros1.sh
   

   It may take some time to build the Docker image. Once the “docker run” completes, the resulting Docker image can be listed with docker images.

3. ROS network setting: We need to set up two environment variables that will be passed to the Docker container and used to configure ROS network settings. Please update the following two lines in setup_env_pc.sh:

   export J7_IP_ADDR=<target_IP_address>
   export PC_IP_ADDR=<PC_IP_address>
   

   <target_IP_address> can be found by running make ip_show on a target terminal.

   Then, source the updated script before executing docker_run_ros1.sh (in the next step):

   user@pc:~/j7ros_home$ source ./setup_env_pc.sh
   

4. Start/Run the ROS 1 Docker container:

   user@pc:~/j7ros_home$ ./docker_run_ros1.sh
   

5. Build the ROS nodes for visualization:

   root@pc-docker:~/j7ros_home/ros_ws$ catkin_make --source src/robotics_sdk/ros1
   root@pc-docker:~/j7ros_home/ros_ws$ source devel/setup.bash
   

3.3. Run Demo Applications

The table below summarizes the launch commands that you can use in the Docker container for each demo, on the target SK board, and on the remote visualization PC. For more details, see the following subsections.

Launch arguments can be passed to the following launch commands.

• To specify a camera recognized as /dev/videoX, use the argument video_id:=X.

• To specify the serial number of the ZED camera (found in the original box), use the argument zed_sn, which should start with ‘SN’ followed by the serial number.

Demo (Input Source)	Launch command on Target	Launch command on Remote Visualization PC
Stereo Vision (ROSBAG)	roslaunch ti_sde bag_sde.launch	roslaunch ti_viz_nodes rviz_sde.launch
Stereo Vision (ZED camera)	roslaunch ti_sde zed_sde.launch video_id:=x zed_sn:=SNxxxxx	same as above
Stereo Vision with point-cloud (ROSBAG)	roslaunch ti_sde bag_sde_pcl.launch video_id:=x zed_sn:=SNxxxxx	roslaunch ti_viz_nodes rviz_sde_pcl.launch
Stereo Vision with point-cloud (ZED camera)	roslaunch ti_sde zed_sde_pcl.launch video_id:=x zed_sn:=SNxxxxx	same as above
Semantic Segmentation CNN (ROSBAG)	roslaunch ti_vision_cnn bag_semseg_cnn.launch	roslaunch ti_viz_nodes rviz_semseg_cnn.launch
Semantic Segmentation CNN (ZED camera)	roslaunch ti_vision_cnn zed_semseg_cnn.launch video_id:=x zed_sn:=SNxxxxx	same as above
Semantic Segmentation CNN (Mono camera)	roslaunch ti_vision_cnn gscam_semseg_cnn.launch video_id:=x	same as above
Semantic Segmentation CNN (IMX219 camera)	roslaunch ti_vision_cnn gscam_semseg_cnn_imx219.launch video_id:=x subdev_id:=y	same as above
Semantic Segmentation CNN (IMX390 camera)	roslaunch ti_vision_cnn gscam_semseg_cnn_imx390.launch video_id:=x subdev_id:=y	same as above
Object Detection CNN (ROSBAG)	roslaunch ti_vision_cnn bag_objdet_cnn.launch	roslaunch ti_viz_nodes rviz_objdet_cnn.launch
Object Detection CNN (ZED camera)	roslaunch ti_vision_cnn zed_objdet_cnn.launch video_id:=x zed_sn:=SNxxxxx	same as above
Object Detection CNN (Mono camera)	roslaunch ti_vision_cnn gscam_objdet_cnn.launch video_id:=x	same as above
Object Detection CNN (IMX219 camera)	roslaunch ti_vision_cnn gscam_objdet_cnn_imx219.launch video_id:=x subdev_id:=y	same as above
Object Detection CNN (IMX390 camera)	roslaunch ti_vision_cnn gscam_objdet_cnn_imx390.launch video_id:=x subdev_id:=y	same as above
3D Obstacle Detection (ROSBAG)	roslaunch ti_estop bag_estop.launch	roslaunch ti_viz_nodes rviz_estop.launch
3D Obstacle Detection (ZED camera)	roslaunch ti_estop zed_estop.launch video_id:=x zed_sn:=SNxxxxx	same as above
Object Detection with 3D Spatial Information (ROSBAG)	roslaunch ti_objdet_range bag_objdet_range.launch	roslaunch ti_viz_nodes rviz_objdet_range.launch
Object Detection with 3D Spatial Information (ZED camera)	roslaunch ti_objdet_range zed_objdet_range.launch video_id:=x zed_sn:=SNxxxxx	same as above
Visual Localization (ROSBAG)	roslaunch ti_vl bag_visloc.launch	roslaunch ti_viz_nodes rviz_visloc.launch

In the following, [SK] and [PC] indicate the steps that should be launched, either on the target SK board or on the PC.

Run Stereo Vision Application

1. [SK] To launch ti_sde node with playing back a ROSBAG file, run the following roslaunch command inside the Docker container:

   root@j7-docker:~/j7ros_home/ros_ws$ roslaunch ti_sde bag_sde.launch
   

   Alternatively, you can run the following directly on the target host Linux:

   root@am6x-sk:~/j7ros_home$ ./docker_run_ros1.sh roslaunch ti_sde bag_sde.launch
   

   To process the image stream from a ZED stereo camera, replace the launch file with zed_sde.launch in the above.

2. [PC] For visualization, in the ROS 1 container on the PC:

   root@pc-docker:~/j7ros_home/ros_ws$ roslaunch ti_viz_nodes rviz_sde.launch
   

Run Stereo Vision Application with Point-Cloud Enabled

1. [SK] To launch ti_sde node with point-cloud enabled on a ROSBAG file, run the following roslaunch command inside the Docker:

   root@j7-docker:~/j7ros_home/ros_ws$ roslaunch ti_sde bag_sde_pcl.launch
   

   Alternatively, you can run the following directly on the target host Linux:

   root@am6x-sk:~/j7ros_home$ ./docker_run_ros1.sh roslaunch ti_sde bag_sde_pcl.launch
   

   To process the image stream from a ZED stereo camera, replace the launch file with zed_sde_pcl.launch in the above.

2. [PC] For visualization, in the ROS 1 container on the PC:

   root@pc-docker:~/j7ros_home/ros_ws$ roslaunch ti_viz_nodes rviz_sde_pcl.launch
   

Run Semantic Segmentation CNN Application

1. [SK] To launch semantic segmentation demo with playing back a ROSBAG file, run the following roslaunch command inside the Docker:

   root@j7-docker:~/j7ros_home/ros_ws$ roslaunch ti_vision_cnn bag_semseg_cnn.launch
   

   Alternatively, you can run the following directly on the target host Linux:

   root@am6x-sk:~/j7ros_home$ ./docker_run_ros1.sh roslaunch ti_vision_cnn bag_semseg_cnn.launch
   

   To process the image stream from a ZED stereo camera or USB mono camera, replace the launch file with zed_semseg_cnn.launch or gscam_semseg_cnn.launch in the above.

2. [PC] For visualization, in the ROS 1 container on the PC::

   root@pc-docker:~/j7ros_home/ros_ws$ roslaunch ti_viz_nodes rviz_semseg_cnn.launch
   

Run Object Detection CNN Application

1. [SK] To launch object detection demo with playing back a ROSBAG file, run the following roslaunch command inside the Docker:

   root@j7-docker:~/j7ros_home/ros_ws$ roslaunch ti_vision_cnn bag_objdet_cnn.launch
   

   Alternatively, you can run the following directly on the target host Linux:

   root@am6x-sk:~/j7ros_home$ ./docker_run_ros1.sh roslaunch ti_vision_cnn bag_objdet_cnn.launch
   

   To process the image stream from a ZED stereo camera or USB mono camera, replace the launch file with zed_semseg_cnn.launch or gscam_semseg_cnn.launch in the above.

2. [PC] For visualization, in the ROS 1 container on the PC:

   root@pc-docker:~/j7ros_home/ros_ws$ roslaunch ti_viz_nodes rviz_objdet_cnn.launch
   

Run 3D Obstacle Detection Application

1. [SK] To launch ti_estop node with playing back a ROSBAG file, run the following roslaunch command inside the Docker:

   root@j7-docker:~/j7ros_home/ros_ws$ roslaunch ti_estop bag_estop.launch
   

   Alternatively, you can run the following directly on the target host Linux:

   root@am6x-sk:~/j7ros_home$ ./docker_run_ros1.sh roslaunch ti_estop bag_estop.launch
   

   To process the image stream from a ZED stereo camera, replace the launch file with zed_estop.launch in the above.

2. [PC] For visualization, in the ROS 1 container on the PC:

   root@pc-docker:~/j7ros_home/ros_ws$ roslaunch ti_viz_nodes rviz_estop.launch
   

Run Object Detection with 3D Spatial Information

1. [SK] To launch ti_objdet_range node with playing back a ROSBAG file, run the following roslaunch command inside the Docker:

   root@j7-docker:~/j7ros_home/ros_ws$ roslaunch ti_objdet_range bag_objdet_range.launch
   

   Alternatively, you can run the following directly on the target host Linux:

   root@am6x-sk:~/j7ros_home$ ./docker_run_ros1.sh roslaunch ti_objdet_range bag_objdet_range.launch
   

   To process the image stream from a ZED stereo camera, replace the launch file with zed_objdet_range.launch in the above.

2. [PC] For visualization, in the ROS 1 container on the PC:

   root@pc-docker:~/j7ros_home/ros_ws$ roslaunch ti_viz_nodes rviz_objdet_range.launch
   

Run Visual Localization Application

1. [SK] To launch ti_vl node with playing back a ROSBAG file, run the following roslaunch command inside the Docker:

   root@j7-docker:~/j7ros_home/ros_ws$ roslaunch ti_vl bag_visloc.launch
   

   Alternatively, you can run the following directly on the target host Linux:

   root@am6x-sk:~/j7ros_home$ ./docker_run_ros1.sh roslaunch ti_vl bag_visloc.launch
   

2. [PC] For visualization, in the ROS 1 container on the PC:

   root@pc-docker:~/j7ros_home/ros_ws$ roslaunch ti_viz_nodes rviz_visloc.launch
   

Build and Run Hector SLAM Application

Many open-source SLAM algorithms can run on the target SK board. Among them, it is demonstrated how to setup and run Hector SLAM on 2D Lidar data. For details, please refer to Hector SLAM Application.