IRT C++/ROS 2 IMU-Simulator

Author:

• Maximilian Nitsch [email protected] (Institute of Automatic Control - RWTH Aachen University)

Maintainer:

• Maximilian Nitsch [email protected] (Institute of Automatic Control - RWTH Aachen University)

Contributors:

• Dmitrii Likhachev [email protected] (Institute of Automatic Control - RWTH Aachen University)
• Philippe Panten [email protected] (Institute of Flight Guidance - Technical University of Braunschweig)

Description

This project provides a high-fidelity IMU simulator written in C++.

The simulator implements the following features:

• Accelerometer and gyroscope measurement simulation
• WELMEC gravity model (accelerometer)
• WGS84 Earth angular velocity model (gyroscope)
• Transport rate angular velocity model (gyroscope)
• Turn-on bias
• Scaling errors
• Misalignment and orthogonality errors
• Stochastic noise (colored/non-white noise)
  • Velocity/angular random walk
  • Bias instability
  • Acceleration/rate random walk
• Saturation
• Quantization errors
• All parameters for an IMU can be configured in a YAML file
• All models and effects can be enabled/disabled separately

An example configuration from real data of a STIM300 IMU is provided.

MATLAB scripts are provided to extract the stochastic noise components using Allan variance analysis. For this, you must provide a long-term (minimum six hours) dataset of acceleration/gyroscope measurements. The IMU must be static, temperature- and vibration-compensated during the recording.

Table of Contents

• Dependencies
• Installation
• Usage
• ROS 2 Nodes
  • Publisher Node
  • Subscriber Node
• Coding Guidelines
• References
• Contributing
• License

Dependencies

This project depends on the following literature and libraries:

• Eigen3: Eigen is a C++ template library for linear algebra: Eigen website.
• ROS 2 Humble: ROS 2 is a set of software libraries and tools for building robot applications: ROS 2 Installation page).

Installation

To install the imu_simulator_package, you need to follow these steps:

1. Install Eigen3: Eigen3 is a dependency for your package. You can install it using your package manager. For example, on Ubuntu, you can install it using the following command:

   sudo apt-get install libeigen3-dev

2. Install ROS 2 Humble: Make sure you have ROS 2 (Humble) installed. You can follow the official installation instructions provided by ROS 2. Visit ROS 2 Humble Installation page for detailed installation instructions tailored to your platform.

3. Clone the Package: Clone the package repository to your ROS 2 workspace. If you don't have a ROS 2 workspace yet, you can create one using the following commands:

   mkdir -p /path/to/ros2_workspace/src
   cd /path/to/ros2_workspace/src

   Now, clone the package repository:

   git clone <repository_url>

   Replace <repository_url> with the URL of your package repository.

4. Build the Package: Once the package is cloned, you must build it using colcon, the default build system for ROS 2. Navigate to your ROS 2 workspace and run the following command:

   cd /path/to/ros2_workspace
   colcon build

   This command will build all the packages in your workspace, including the newly added package.

5. Source the Workspace: After building the package, you need to source your ROS 2 workspace to make the package available in your ROS 2 environment. Run the following command:

   source /path/to/ros2_workspace/install/setup.bash

   Replace /path/to/ros2_workspace with the actual path to your ROS 2 workspace.

That's it! Your imu_simulator_package should now be installed along with its dependencies and ready to use in your ROS 2 environment.

Usage

1. Configure your YAML file for your IMU or use the default file.

2. Start the IMU simulator with the launch file:

   ros2 launch imu_simulator_package imu_simulator.launch.py

The IMU simulator prints your settings and waits for a ground truth odometry message.

3. Provide an odometry publisher from you vehicle simulation. For testing, you can launch the odometry_test_publisher node:

   ros2 launch imu_simulator_package odometry_test_publisher.py

4. The IMU values should now be published.

5. 

Important Usage Information:

• The odometry message must be published with at least the IMU data rate/sample time.
• The message /imu/diagnostic will show WARN if the odometry rate is lower.
• If no odometry message is published, the message /imu/diagnostic will show STALE.
• If everything is correct, /imu/diagnostic will show OK.

ROS 2 Nodes

The IMU simulator node implements five publishers and subscribes to one topic. ROS 2 services or actions are not provided.

Publisher Node

This node publishes the following topics:

Topic Name	Message Type	Description
*/imu/data	sensor_msgs/Imu.msg	Publishes IMU sensor data.
*/imu/data_visualization	geometry_msgs/AccelStamped.msg	Publishes IMU sensor data for visualization (rviz).
*/imu/true_linear_acceleration	geometry_msgs/Vector3.msg	Publishes Itrue linear acceleration.
*/imu/true_linear_angular_velocity	geometry_msgs/Vector3.msg	Publishes true angular velocity.
*/imu/diagnostic	diagnostic_msgs/DiagnosticStatus.msg	Publishes diagnostic status of IMU data.

Subscriber Node

This node subscribes to the following topics:

Topic Name	Message Type	Description
*/odometry	nav_msgs/Odometry.msg	Subscribes to ground truth vehicle odometry.

Coding Guidelines

This project follows these coding guidelines:

• https://google.github.io/styleguide/cppguide.html
• http://docs.ros.org/en/humble/The-ROS2-Project/Contributing/Code-Style-Language-Versions.html

References

The IMU simulator implementation closely follows the work:

• M. Nitsch, "Navigation of a miniaturized autonomous underwater vehicle exploring waters under ice," Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, RWTH Aachen University, 2024. DOI: 10.18154/RWTH-2024-05964.
• J. A. Farrell, F. O. Silva, F. Rahman and J. Wendel, "Inertial Measurement Unit Error Modeling Tutorial: Inertial Navigation System State Estimation with Real-Time Sensor Calibration," in IEEE Control Systems Magazine, vol. 42, no. 6, pp. 40-66, Dec. 2022, DOI: 10.1109/MCS.2022.3209059.
• J. A. Farrell, "Aided Navigation Systems: GPS and High Rate Sensors," New York, NY, McGraw-Hill, 552 pages, 2008.

The MATLAB scripts are in parts taken from:

• AV-MATLAB-SW

Contributing

If you want to contribute to the project, see the CONTRIBUTING file for details.

License

This project is licensed under the BSD-3-Clause License. See the LICENSE file for details.