imucal

This package provides methods to calculate and apply calibrations for 6 DOF IMUs based on multiple different methods.

So far supported are:

• Ferraris Calibration (Ferraris1994 / Ferraris1995)
• Ferraris Calibration using a Turntable

Installation

pip install imucal

To use the included calibration GUI you also need matplotlib (version >2.2). You can install it using:

pip install imucal[calplot]

Quickstart

This package implements the IMU-infield calibration based on Ferraris1995. This calibration method requires the IMU data from 6 static positions (3 axes parallel and antiparallel to the gravitation vector) for calibrating the accelerometer and 3 rotations around the 3 main axes for calibrating the gyroscope. In this implementation, these parts are referred to as {acc,gyr}_{x,y,z}_{p,a} for the static regions and {acc,gyr}_{x,y,z}_rot for the rotations. As example, acc_y_a would be the 3D-acceleration data measured during a static phase, where the y-axis was oriented antiparallel to the gravitation vector.

To annotate a Ferraris calibration session that was recorded in a single go, you can use the following code snippet.
Note: This will open an interactive Tkinter plot. Therefore, this will only work on your local PC and not on a server or remote hosted Jupyter instance.

from imucal import ferraris_regions_from_interactive_plot

# Your data as a 6 column dataframe
data = ...

section_data, section_list = ferraris_regions_from_interactive_plot(
    data, acc_cols=["acc_x", "acc_y", "acc_z"], gyr_cols=["gyr_x", "gyr_y", "gyr_z"]
)
# Save the section list as reference for the future
section_list.to_csv('./calibration_sections.csv')  # This is optional, but recommended

Now you can perform the calibration:

from imucal import FerrarisCalibration

sampling_rate = 100 #Hz 
cal = FerrarisCalibration()
cal_mat = cal.compute(section_data, sampling_rate, from_acc_unit="m/s^2", from_gyr_unit="deg/s")
# `cal_mat` is your final calibration matrix object you can use to calibrate data
cal_mat.to_json_file('./calibration.json')

Applying a calibration:

from imucal.management import load_calibration_info

cal_mat = load_calibration_info('./calibration.json')
new_data = pd.DataFrame(...)
calibrated_data = cal_mat.calibrate_df(new_data, acc_unit="m/s^2", gyr_unit="deg/s")

For further information on how to perform a calibration check the User Guides or the Examples.

Further Calibration Methods

At the moment, this package only implements calibration methods based on Ferraris1994/95, because this is what we use to calibrate our IMUs. We are aware that various other methods exist and would love to add them to this package as well. Unfortunately, at the moment we can not justify the time investment.

Still, we think that this package provides a suitable framework to implement other calibration methods with relative ease. If you would like to contribute such a method, let us know via GitHub Issue, and we will try to help you as good as possible.

Citation

If you are using imucal in your scientific work, we would appreciate if you would cite or link the project:

Küderle, A., Roth, N., Richer, R., & Eskofier, B. M., 
imucal - A Python library to calibrate 6 DOF IMUs (Version 2.0.2) [Computer software].
https://doi.org/10.5281/zenodo.56392388

Contributing

All project management and development happens through this GitHub project. If you have any issues, ideas, or any comments at all, just open a new issue. We are always happy when people are interested to use our work and would like to support you in this process. In particular, we want to welcome contributions of new calibration algorithms, to make this package even more useful for a wider audience.