# hdl-localization-ROS2

**Repository Path**: duanyuanchao123/hdl-localization-ROS2

## Basic Information

- **Project Name**: hdl-localization-ROS2
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-01-02
- **Last Updated**: 2025-01-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# hdl-localization-ROS2

ROS2 wrapper for hdl-localization package with docker

## 1. Build docker image with Dockerfile  

Before you start hdl-localization with docker, you should install [docker](https://www.docker.com/) and [nvidia-docker](https://github.com/NVIDIA/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!

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/
```



## TODO

- [ ] Support hdl_global_localization ROS2 launch file 
- [ ] Code refactoring  
- [ ] Support ROS2 humble  
- [ ] Suuport NDT CUDA


## Acknowledgement

We based ours on the following packages.    
This repository just provides a detailed guide to using docker to reduce dependencies.  

- [DataspeedInc/hdl_localization](https://github.com/DataspeedInc/hdl_localization/tree/ros2)   
- [DataspeedInc/hdl_global_localization](https://github.com/DataspeedInc/hdl_global_localization/tree/ros2)  
- [DataspeedInc/fast_gicp](https://github.com/DataspeedInc/fast_gicp/tree/ros2)
- [tier4/ndt_omp](https://github.com/tier4/ndt_omp)