EIMU Documentation

Easy IMU (EIMU) User Documentation

Anyone using an Inertial Measurement Unit (IMU) generally wants it to spit out filtered orientation readings and integrate them into their preferred project(s) without the unnecessary stress and headaches of the mathematics and computational algorithms behind it. This is what the Easy IMU does.

The Easy IMU is an easy-to-use Advanced Inertial Measurement Unit (IMU) built on and based on the popular MPU9250 IMU. It consists of the MPU9250 IMU, an ATmega328 microcontroller computational system, and a GUI Application to ease the calibration, reference frame setup (e.g. NED, ENU, or NWU), and filter setup process for the MPU9250 IMU (it uses the Madgwick filter).

It also provides a USB serial and I2C communication interface with ROS2, Arduino, Python, and Cpp libraries for easy interfacing with one's preferred project.

The Common MPU9250 IMU Problem

The MPU9250 IMU is a widely used Inertial Measurement Unit (IMU) among hobbyists, researchers, engineers, and developers in robotics, drones, virtual reality, augmented reality, and wearable technology. It is renowned for its accuracy, reliability, and affordability, making it a popular choice for projects requiring motion tracking and orientation sensing.

While there are libraries available to assist in using the MPU9250 IMU, common issues encountered with it typically revolve around calibration (especially of the magnetometer), setup, noise filtering, choosing reference frame (e.g. NED, ENU, or NWU reference frame), and seamless integration into preferred projects.

Calibrating its sensors, especially the magnetometer, is usually an issue and can be frustrating as wrong calibration will result in faulty orientation readings. Also, implementing filtering algorithms like the popular Kalman or Madgwick filter to get less noisy filtered orientation readings can be another problem, not to mention choosing a reference frame (e.g. NED, ENU, or NWU reference frame) and trying to add the whole code into your preferred project (which is usually bulky).

Ultimately, anyone using an IMU desires filtered orientation readings, in a preferred reference frame (e.g. NED, ENU, or NWU) without the unnecessary stress and headaches of calibration and noise filtering as well as frame transformation when integrating it into their projects.

The Easy IMU easily solves and abstracts all these problems so you can focus on using the filtered readings in your projects.

In summary, the Easy IMU provides the following:

• Easy calibration via its GUI application - eimu_setup_application.
• Easy Madgwick filter gain setup (and visualization) via its GUI application.

• Easy quick reference frame setup (NED, ENU, NWU) via its GUI application.

• Easy integration with ROS2 (microcomputer or PC) projects with its ROS2 package - eimu_ros2.
• Easy integration with Arduino projects with its I2C library - eimu_arduino.
• Easy integration with a microcomputer-based (Python or Cpp) project with its eimu_python and eimu_cpp library.

If you are using Ubuntu 22.04 LTS, you can download the EIMU executable application from here

The EIMU Specification

• Supply Voltage - 5V (typical)

• Low Power Consumption.

• Communication Interface - USB Serial and I2C

• It can be configured to use any of the NED, ENU, or NWU Reference Frame.

• Easily integrates with ROS2 and Arduino (as well as Python and Cpp) projects.

• Orientation readings (Roll, Pitch, and Yaw) are in radians. It also outputs quaternion orientation readings.

• Angular rate readings are in rad/sec.

• Acceleration readings are in m/s2.

Only orientation readings are filtered, not the rate or acceleration readings but it also provides the orientation, angular rates, and acceleration variances which can be used in more advanced filter algorithms as covariance matrices.

EIMU Reference Frame Use Cases

• ENU (EAST [+X], NORTH [+Y], UP [+Z]) Frame - Default Frame

  • Use For Ground Robots - e.g mobile robots, stationary robots, etc.

  • Default ROS2 Frame based on the REP 103 and REP 105 Standard.
  • Use in ROS2 indoor and outdoor ground robotics projects

• NWU (NORTH [+X], WEST [+Y], UP [+Z]) Frame

  • An alternative for use with Ground robots - e.g mobile robots, stationary robots, etc.

• NED (NORTH [+X], EAST [-Y], DOWN [-Z]) Frame

  • Use for Drones or Aircracfts

  • Can also be configure for ROS2 robotics projects for Drones and outdoor robots (based on the REP 103 and REP 105 Standard)

EIMU Applications

• Education - Teaching IMU concepts, Sensor fusion, and Robotics

• Robotics Project (Arduino, ROS2, Python, and C++)

• Precise Motion tracking

• Drones

• GPS Guided systems

• Heading reference system

EIMU Tutorials (Usage Guide)

• How to calibrate and setup the EIMU using its GUI apllication
• How to use the EIMU with ROS2
• How to use the EIMU with Arduino
• How to use the EIMU with Python and Cpp projects

Checkout These Libraries that Made The EPMC Project a Success

Check out these Github repos of libraries that went a long way in developing the Easy IMU ATmega328 microcontroller computational system. You can use them in your projects also (Ensure to star them).

• imu_madgwick_filter - Madgwick filter Arduino library based on (and adapted from) the imu_tool ROS2 Madgwick code by CCNYRoboticsLab, adapted by samuko-things.
• arduino_matrix_vector_lab - Arduino library that helps you perform vector and matrix operations and transformation with arrays, developed by samuko-things.
• invensense_imu - Arduino and CMake library for communicating with the InvenSense MPU-6500, MPU-9250, and MPU-9255 nine-axis IMUs, by Bolder Flight.
• Serial_comm_pyserial_and_arduino - a backend-API-style serial communication code between Pyserial and Arduino that can be adapted to any project, developed by samuko-things.