ROS Index
Repository Summary
| Description | |
| Checkout URI | https://github.com/pyc5714/hdl-localization-ros2.git |
| VCS Type | git |
| VCS Version | master |
| Last Updated | 2025-06-09 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Packages
| Name | Version |
|---|---|
| fast_gicp | 0.0.0 |
| hdl_global_localization | 0.0.0 |
| hdl_localization | 0.0.0 |
| ndt_omp | 0.0.0 |
README
hdl-localization-ROS2
ROS2 wrapper for hdl-localization package with docker.
You can refer to the original hdl localization repository for a detailed explanation of the ROS1 version.
1. Build docker image with Dockerfile
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
2. Make HDL-Localization-ROS2 docker container
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" \
${docker image} /bin/bash
⚠️ You should change {hdl_localization_repo_root}, {docker container name}, {docker image} to suit your environment.
For example,
nvidia-docker run --privileged -it \
-e NVIDIA_DRIVER_CAPABILITIES=all \
-e NVIDIA_VISIBLE_DEVICES=all \
--volume=/home/taeyoung/Desktop/hdl-localization-ROS2:/root/workspace/src \
--volume=/tmp/.X11-unix:/tmp/.X11-unix:rw \
--net=host \
--ipc=host \
--name=hdl-localization-ros2 \
--env="DISPLAY=$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#
3. Make custom HDL-Localization-ROS2 launch file
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')
Build and Run it!
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