This repository contains a dockerized comprehensive wrapper for ORB-SLAM3 on ROS 2 Humble for Ubuntu 22.04.
git clone https://github.com/suchetanrs/ORB-SLAM3-ROS2-Dockercd ORB-SLAM3-ROS2-Dockergit submodule update --init --recursive --remote
cd ORB-SLAM3-ROS2-Docker
sudo chmod +x container_root/shell_scripts/docker_install.sh
./container_root/shell_scripts/docker_install.sh- Build the image:
sudo docker build --build-arg USE_CI=false -t orb-slam3-humble:22.04 . - Add
xhost +to your.bashrcto support correct x11-forwarding usingecho "xhost +" >> ~/.bashrc source ~/.bashrc- You can see the built images on your machine by running
sudo docker images.
cd ORB-SLAM3-ROS2-Docker(ignore if you are already in the folder)sudo docker compose run orb_slam3_22_humble- This should take you inside the container. Once you are inside, run the command
xeyesand a pair of eyes should pop-up. If they do, x11 forwarding has correctly been setup on your computer.
Launch the container using steps in (4).
cd /home/orb/ORB_SLAM3/ && sudo chmod +x build.sh && ./build.sh
cd /root/colcon_ws/ && colcon build --symlink-install && source install/setup.bashLaunch the container using steps in (4). If you are inside the container, run the following:
ros2 launch orb_slam3_ros2_wrapper unirobot.launch.py- You can adjust the robot namespace in the
unirobot.launch.pyfile.
- Setup the ORB-SLAM3 ROS2 Docker using the steps above. Once you do (1) step in the
Launching ORB-SLAM3section, you should see a window popup which is waiting for images. This is partially indicative of the setup correctly done. - Setup the simulation by following the README here
- Once you are able to teleop the robot, you should be able to run ORB-SLAM3 with both the containers (simulation and wrapper) running parallely.
This package can be used to generate a global pointcloud from the SLAM.
It subscribes to the published map_data and an input pointcloud either from a LiDAR or from a depth camera. It stitches the input pointclouds together based on the latest pose-graph data.
To run the package, these steps can be followed:
- Make sure the simulation is running. Run the orb_slam3 container and once you are in the bash shell, run the following:
./launch_slam.sh - The top-left terminal contains the launch file to run the slam. This must be launched first.
- The bottom-left terminal contains the launch file to start the pointcloud_stitcher node. This should run soon after you launch the SLAM.
- If you wish to publish the global pointcloud at any point during the SLAM's operation, simply run the python file in the top-right terminal. You should be able to view the global pointcloud in rviz (you can launch RViz with the correct configuration from the bottom-right terminal).
The simulation and the wrapper both have their ROS_DOMAIN_ID set to 55 so they are meant to work out of the box. However, you may face issues if this environment variable is not set properly. Before you start the wrapper, run ros2 topic list and make sure the topics namespaced with robot_0 are visible inside the ORB-SLAM3 container provided the simulation is running along the side.
| Service Name | Purpose | type |
|---|---|---|
orb_slam3/get_map_data |
Sends the map_data in the response. | slam_msgs::srv::GetMap |
orb_slam3/get_landmarks_in_view |
Takes an input pose and publishes the feature points visible from that pose. | slam_msgs::srv::GetLandmarksInView |
orb_slam3/get_all_landmarks_in_map |
Publishes all feature points in the map and fills the same pointcloud in the response. | slam_msgs::srv::GetAllLandmarksInMap |
orb_slam3/reset_mapping |
Resets the current mapping instance and clears all keyframes. | std_srvs::srv::SetBool |
| Topic Name | Purpose | type |
|---|---|---|
map_points |
Publishes the point cloud representing feature points collected from the SLAM process. This is published when orb_slam3/get_all_landmarks_in_map service is called. |
sensor_msgs::msg::PointCloud2 |
visible_landmarks |
Publishes the point cloud of feature points (landmarks) visible from a given pose. This is published when orb_slam3/get_landmarks_in_view service is called. |
sensor_msgs::msg::PointCloud2 |
slam_info |
Publishes overall SLAM-related information. | slam_msgs::msg::SlamInfo |
map_data |
Continuously publishes the map data generated by the SLAM algorithm. | slam_msgs::msg::MapData |
robot_pose_slam |
Publishes the robot's pose expressed in the global frame. | geometry_msgs::msg::PoseStamped |
| Parameter Name | Default Value | Description |
|---|---|---|
robot_base_frame |
base_footprint |
The name of the frame attached to the robot's base. |
global_frame |
map |
The name of the global frame of reference. It represents a fixed world coordinate frame in which the robot navigates. |
odom_frame |
odom |
The name of the odometry frame. |
rgb_image_topic_name |
rgb_camera |
The topic to recieve rgb images. |
depth_image_topic_name |
depth_camera |
The topic to recieve depth images. |
imu_topic_name |
imu |
The topic to recieve IMU messages (Not used in RGB-D mode). |
odom_topic_name |
odom |
The topic to recieve on board odometry messages (Used only if odometry_mode is set to true). |
visualization |
true |
A boolean flag to enable or disable visualization. When set to true, the ORB-SLAM3 viewer will show up with the tracked points and the keyframe trajectories. |
odometry_mode |
false |
A boolean flag to toggle odometry mode. When false, the system operates without relying on odometry data, which might be used in scenarios where odometry information is unavailable or unreliable. In this case, it publishes the transform directly between the global_frame and the robot_base_frame |
publish_tf |
true |
Publishes the map->odom tf in case odometry_mode is set to true and map->odom->base_link in case odometry_mode is set to false. |
map_data_publish_frequency |
1000 |
Time interval at which map_data should be published (ms). |
do_loop_closing |
true |
Enable or disable loop closing in ORB-SLAM3. This will also disable re-localisation and multi-map if false |
ORB-SLAM3 is launched from orb_slam3_docker_20_humble/orb_slam3_ros2_wrapper/launch/rgbd.launch.py which inturn is launched from orb_slam3_docker_20_humble/orb_slam3_ros2_wrapper/launch/unirobot.launch.py
Currently the rgbd.launch.py launch file defaults to orb_slam3_ros2_wrapper/params/gazebo_rgbd.yaml. You can modify this with your own parameter file in case you wish to use your own camera.
The very initial versions of this code were derived from thien94/orb_slam3_ros_wrapper and zang9/ORB_SLAM3_ROS2