rmcl_examples

Examples to test and demonstrate the functionalities of the rmcl ROS package.

Simulation

In the rmcl_examples_sim packages are placed several simulation environments to spawn a mobile robot in.

The robot can be loaded into one simulation by calling

ros2 launch rmcl_examples_sim start_robot_launch.py map:=tray

The environment can be changed by either changing the launch file's default arguments or via command line. For further details and what maps are available, see rmcl_examples_sim.

Maps

The rmcl_examples_maps contains mesh maps each of which corresponds to one (same named) simulation environment.

• mesh_tools

ros2 launch rmcl_examples_maps show_map.launch map:=tray

Those maps can are used throughout the examples as reference map for RMCL to localize a robot. See rmcl_examples_maps for further details.

MICP-L - Quickstart

To start MICP-L (Mesh ICP Localization), run

ros2 launch rmcl_examples rmcl_micp.launch map:=tray gui:=True

Note: the map has to match the environment that have been used with the simulation!

The argument gui:=True courses a preconfigured RViz windows to open. After that you can set a pose in RViz via 2D Pose Estimate and see the robot localizing itself given the range measurements of the Velodyne LiDAR. Alternatively, you can use the Mesh Pose Guess tool of mesh_tools to provide a pose guess on the mesh.

MICP-L - Examples

1. Overview

Three sensors, four wheels, and many worlds.

2. Conversions

Use you own data for MICP-L. Message conversions explained.

3. Segmentation

Filter expected parts of you sensor data and mark the unexpected parts of the data and the map.

4. Node Composition

Let the sensor data flow more efficiently to MICP-L.

5. Sensor Combinations

Combine several sensors and use all at once.

6. GPU Acceleration

Accelerate the registration process using hardware-accelerated ray tracing.

7. Parameters

Tune the parameters of MICP-L for your needs.

Branch Compatibility

RMCL Branch	Supported ROS 2 versions
main	humble, jazzy