Rover Simulation

Setup

1. Clone the repository:

git clone https://github.com/nickoenig37/rover_simulation.git

Getting started with updating and fetching Submodules:

git submodule update --init --recursive --remote

Install Docker on your system (IF YOU DON'T HAVE IT ALREADY)

cd ORB-SLAM3-ROS2-MONO-Docker
sudo chmod +x container_root/shell_scripts/docker_install.sh
./container_root/shell_scripts/docker_install.sh

Notes from the OG repo:

3. Build the image with ORB_SLAM3

1. Build the image: sudo docker build --build-arg USE_CI=false -t orb-slam3-humble:22.04 .
2. Add xhost + to your .bashrc to support correct x11-forwarding using echo "xhost +" >> ~/.bashrc
3. source ~/.bashrc
4. You can see the built images on your machine by running sudo docker images.

4. Running the container

1. cd ORB-SLAM3-ROS2-Docker (ignore if you are already in the folder)
2. sudo docker compose run orb_slam3_22_humble
3. This should take you inside the container. Once you are inside, run the command xeyes and a pair of eyes should pop-up. If they do, x11 forwarding has correctly been setup on your computer.

5. Building the ORB-SLAM3 Wrapper

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.bash

Launching ORB-SLAM3

Launch the container using steps in (4). If you are inside the container, run the following:

1. ros2 launch orb_slam3_ros2_wrapper unirobot.launch.py
2. You can adjust the robot namespace in the unirobot.launch.py file.

Running this with a Gazebo Sim simulation.

1. clone the repo for the simulation environment:

git clone -b humble https://github.com/suchetanrs/gz-sim-environment.git

2. Follow:

cd gz-sim-environment
sudo chmod +x install-nvidia-container-toolkit.sh
./install-nvidia-container-toolkit.sh

3. Pull the latest image:

sudo docker pull nvidia/cuda:11.4.2-cudnn8-runtime-ubuntu20.04
sudo docker build -t gazebo-vehicle-ros2-gpu-harmonic:humble .

4. Run this:

sudo docker compose run vehicle_simulator_gz_sim

8. sudo chmod +x launch_simulation.sh
9. ./launch_simulation.sh
10. This will build the packages in the top-right terminal.
11. After the build is complete you can run what was inputted in the top-left terminal. This will launch gazebo and spawn the robot.
12. The GUI is disabled by default.
13. You should be able to teleop the robot through the teleop window.
14. You can run the default simulation RViz from the bottom-right terminal. As long as you are able to see the lidar pointcloud, the rgb and depth images on RViz, you can safely ignore the texture error messages on the simulation terminal window.
15. If you wish to launch cave world, you can do run the following in the top right terminal: ros2 launch vehicle_bringup unirobot.launch.py world:=cave_world.sdf
16. If you wish to launch the corridor world, you can do run the following in the top right terminal: ros2 launch vehicle_bringup unirobot.launch.py world:=indoor.sdf

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.

Important information

The ROS_DOMAIN_ID is set to 55 in the .bashrc

There are currently two model types. More will be available soon! (Drones, Legged Robots, Multi-Robot Simulations)

You can modify the spawn_robot.launch.py in the vehicle_packages to select your desired model.

Edit the robot_model_type variable.

Running the map_generator package.

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:

1. 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
2. The top-left terminal contains the launch file to run the slam. This must be launched first.
3. The bottom-left terminal contains the launch file to start the pointcloud_stitcher node. This should run soon after you launch the SLAM.
4. 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).

Potential issues you may face.

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 /rgb_camera and /depth_camera are visible inside the ORB-SLAM3 container provided the simulation is running along the side.

Services

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

Published Topics

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

ROS Parameters

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).
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. Further information can be found on the FAQ
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. Further information can be found on the FAQ
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

Important notes

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