ROS2 wrapper for hdl-localization package with docker
Before you start hdl-localization with docker, you should install docker and nvidia-docker in your PC.
You could also make docker image directly with provieded Dockerfile.
Move the terminal path to /docker
and execute the following command.
cd docker
docker build -t hdl-localization-ros2:latest .
hdl-localization-ros2:latest
is just example of this docker image, you can replace it with the image name you want.
After the image is created, you can execute docker images
command to view the following results from the terminal.
output :
REPOSITORY TAG IMAGE ID CREATED SIZE
hdl-localization-ros2 latest 812ae31625b3 48 minutes ago 3.36GB
When you create a docker container, you need several options to use the GUI and share folders.
First, you should enter the command below in the local terminal to enable docker to communicate with Xserver on the host.
xhost +local:docker
After that, make your own container with the command below.
nvidia-docker run --privileged -it \
-e NVIDIA_DRIVER_CAPABILITIES=all \
-e NVIDIA_VISIBLE_DEVICES=all \
--volume=${hdl_localization_repo_root}:/root/workspace/src \
--volume=/tmp/.X11-unix:/tmp/.X11-unix:rw \
--net=host \
--ipc=host \
--name=${docker container name} \
--env="DISPLAY=$DISPLAY" \
${docker image} /bin/bash
For example,
nvidia-docker run --privileged -it \
-e NVIDIA_DRIVER_CAPABILITIES=all \
-e NVIDIA_VISIBLE_DEVICES=all \
--volume=/home/taeyoung/Desktop/hdl-localization-ROS2:/root/workspace/src \
--volume=/tmp/.X11-unix:/tmp/.X11-unix:rw \
--net=host \
--ipc=host \
--name=hdl-localization-ros2 \
--env="DISPLAY=$DISPLAY" \
--env="QT_X11_NO_MITSHM=1" \
hdl-localization-ros2:latest /bin/bash
If you have successfully created the docker container, the terminal output will be similar to the below.
output :
================HDL localization ROS2 Docker Env Ready================
root@taeyoung-cilab:/root/workspace#
When we created our docker container, we utilized the --volume
option, so the files we modified locally are directly available inside docker.
All you need is .pcd
file, lidar topic and imu topic!
See hdl_localization_turtlebot.launch.py
and run the example bagfile to get a better understanding.
points_topic = LaunchConfiguration('points_topic', default='/velodyne_points') # velodyne topic name
odom_child_frame_id = LaunchConfiguration('odom_child_frame_id', default='base_link') # velodyne_points frame ID
imu_topic = LaunchConfiguration('imu_topic', default='/imu') # optional, you should know noise parameter
globalmap_pcd = DeclareLaunchArgument('globalmap_pcd', default_value='/root/workspace/src/hdl_localization/data/turtlebot3.pcd', description='Path to the global map PCD file')
Within Docker, we need 3 containers.
To access the same container, use the command below
docker exec -it hdl-localization-ros2 /bin/bash
Within the connected container,
source /opt/ros/foxy/setup.bash
root@taeyoung-cilab:~/workspace#
All should stay on the above path.
- 1st container (For hdl localization package)
colcon build
source install/setup.bash
ros2 launch hdl_localization hdl_localization_turtlebot.launch.py
- 2nd container (For rviz)
rviz2 -d src/hdl_localization/rviz/hdl_localization_ros2.rviz
- 3rd container (For sample bagfile)
ros2 bag play src/hdl_localization/sample-bag/subset/
- Support hdl_global_localization ROS2 launch file
- Code refactoring
- Support ROS2 humble
- Suuport NDT CUDA
We based ours on the following packages.
This repository just provides a detailed guide to using docker to reduce dependencies.