Docker Setup for ROS 2¶

In the ROS 2 Docker images both for TDA4 and Ubuntu PC, following two popular DDS implementations are installed:

eProsima Fast DDS (default DDS for ROS 2 Foxy)
Eclipse Cyclone DDS (additionally installed)

To make the out-of-box demos run smooth, the following DDS selection are made in the ROS 2 Docker containers by default on each platform.

Platform	DDS Choice	Note
TDA4 target	Eclipse Cyclone DDS	Provides better performance, especially with "rosbag play"
Visualization PC	eProsima Fast DDS	Provides better `Ctrl+C` response

By setting an environment variable RMW_IMPLEMENTATION you can switch the DDS implementation in each launch session. For example,

To use Fast DDS,

RMW_IMPLEMENTATION=rmw_fastrtps_cpp ros2 ...

To use Cyclone DDS,

RMW_IMPLEMENTATION=rmw_cyclonedds_cpp ros2 ...

1. Set Up Docker Environment on J7 host¶

In ROS 2 Docker environment, ROS Foxy and necessary libraries and tools are installed.

To generate bash scripts for building and running a Docker image for the Robotics Kit:
```
root@j7-evm:~/j7ros_home$ make scripts ROS_VER=2
```
Make sure that two bash scripts, docker_build_ros2.sh and docker_run_ros2.sh, are generated.
To build the Docker image:
```
root@j7-evm:~/j7ros_home$ ./docker_build_ros2.sh
```
This step will take several minutes, and once “docker build” is completed, you can check the resulting Docker image with docker images.

To start the Docker container:

root@j7-evm:~/j7ros_home$ ./docker_run_ros2.sh

To build the ROS applications, inside the Docker container:
```
root@j7-docker:~/j7ros_home/ros_ws$ colcon build --base-paths src/jacinto_ros_perception/ros2
root@j7-docker:~/j7ros_home/ros_ws$ source install/setup.bash
```
NOTE: In case when “colcon build” fails, you can try again after adding --executor sequential to the colcon build command above.
The ROSBAG data downloaded is in ROS 1 specific format and the format and storage mechanism has changed in ROS 2. For converting the ROS 1 bag data to ROS 2 format, use rosbags-convert <ros1_bag_name.bag>. This will create a directory <ros_bag_name> and contains the data under this directory. To convert the downloaded ROSBAG file, run the following inside the ROS 2 Docker container.
```
root@j7-docker:~/j7ros_home$ rosbags-convert $WORK_DIR/data/ros_bag/zed1_2020-11-09-18-01-08.bag
```

2. Set Up Docker Environment on PC for Visualization¶

You can choose any folder, but this section assumes installation is made under ${HOME}/j7ros_home.

To generate bash scripts for building and running a Docker image for the ROS 2 Docker container:
```
user@pc:~/j7ros_home$ make scripts ROS_VER=2
```
If the Ubuntu PC uses a Nvidia GPU driver, please add one more argument GPUS=y:
```
user@pc:~/j7ros_home$ make scripts ROS_VER=2 GPUS=y
```
Make sure that two bash scripts, docker_build_ros2.sh and docker_run_ros2.sh, are generated.
To build the ROS 2 Docker image:
```
user@pc:~/j7ros_home$ ./docker_build_ros2.sh
```
It will take several minutes to build the Docker image. The Docker image built can be listed with docker images.

Run the ROS 2 Docker container:

user@pc:~/j7ros_home$ ./docker_run_ros2.sh

Build the ROS nodes for visualization:

root@pc-docker:~/j7ros_home/ros_ws$ colcon build
root@pc-docker:~/j7ros_home/ros_ws$ source install/setup.bash

3. Run Demo Applications¶

Table below summarizes the launch commands that you can use in the Docker container for each demo, on the TDA4/J7, and on the remote visualization PC. For more details, see the following subsections.

Demo (Input Source)	Launch command on TDA4	Launch command on Remote Visualization PC
Stereo Vision (ZED camera)	ros2 launch ti_sde zed_sde_launch.py	ros2 launch ti_viz_nodes rviz_sde_launch.py
Stereo Vision with point-cloud (ZED camera)	ros2 launch ti_sde zed_sde_pcl_launch.py	ros2 launch ti_viz_nodes rviz_sde_pcl_launch.py
Semantic Segmentation CNN (ZED camera)	ros2 launch ti_vision_cnn zed_semseg_cnn_launch.py	ros2 launch ti_viz_nodes rviz_semseg_cnn_launch.py
Semantic Segmentation CNN (Mono camera)	ros2 launch ti_vision_cnn mono_semseg_cnn_launch.py	same as above
Object Detection CNN (ZED camera)	ros2 launch ti_vision_cnn zed_objdet_cnn_launch.py	ros2 launch ti_viz_nodes rviz_objdet_cnn_launch.py
Object Detection CNN (Mono camera)	ros2 launch ti_vision_cnn mono_objdet_cnn_launch.py	same as above
3D Obstacle Detection (ZED camera)	ros2 launch ti_estop zed_estop_launch.py	ros2 launch ti_viz_nodes rviz_estop_launch.py

Running Demos with ROSBAG

It is recommended to launch demos in two terminals on the TDA4 target, and launch ros2 bag play in a separate terminal as shown is the following table.

Demo	ROSBAG launch command on TDA4 (Terminal 1)	Demo launch command on TDA4 (Terminal 2)	Launch command on Remote Visualization PC
Stereo Vision	ros2 launch ti_sde rosbag_launch.py	ros2 launch ti_sde sde_launch.py	ros2 launch ti_viz_nodes rviz_sde_launch.py
Stereo Vision with point-cloud	ros2 launch ti_sde rosbag_launch.py	ros2 launch ti_sde sde_pcl_launch.py	ros2 launch ti_viz_nodes rviz_sde_pcl_launch.py
Semantic Segmentation CNN	ros2 launch ti_sde rosbag_remap_launch.py	ros2 launch ti_vision_cnn semseg_cnn_launch.py	ros2 launch ti_viz_nodes rviz_semseg_cnn_launch.py
Object Detection CNN	ros2 launch ti_sde rosbag_remap_launch.py	ros2 launch ti_vision_cnn objdet_cnn_launch.py	ros2 launch ti_viz_nodes rviz_objdet_cnn_launch.py
3D Obstacle Detection	ros2 launch ti_sde rosbag_launch.py	ros2 launch ti_estop estop_launch.py	ros2 launch ti_viz_nodes rviz_estop_launch.py

You can use TMUX inside the ROS Docker container to split the current terminal window into multiple panes. Below are some of useful basic keys in using TMUX. Full list of keys can be found, e.g., in this link.

tmux: start a tmux session
Ctrl + b, followed by ": split pane vertically
Ctrl + b, followed by ↑ or ↓: switch to pane to the direction
Ctrl + b, followed by x: close the current pane

In the following, [TDA4] and [PC] indicate where the step(s) should be launched: either on the TDA4 target, or on the PC.