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

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]

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.

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.

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

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.

We use poetry to manage our dependencies. Therefore, you need to first install Poetry locally on you machine.

Then you can run the following command to install a local development version of this library in a dedicated venv.

poetry install -E calplot -E h5py

To run tests/the linter/... we use doit. You can see all available commands by running:

poetry run doit list

and execute any command by running

poetry run doit <command-name>

If you update or add dependencies using (poetry add or poetry update) you will see that the pyproject.toml and the poetry.lock files are both updated. This might take a while (>10 min) depending on the dependency you updated. Unfortunately, we can not do anything about that at the moment. Make sure you commit the changes to both files. Otherwise, wrong versions of dependencies will be used in the CI and by other developers.

In case you update dependencies by directly editing the pyproject.toml file, you need to be very careful and make sure, you run poetry lock [--no-update] afterwards. Otherwise, the lock file will be out of date.

In general, it is a good idea to just run poetry update from time to time. This will install the latest version of all dependencies that are still allowed by the version constrains in the pyproject.toml. This allows to check, if everything still works well with the newest versions of all libraries.