https://github.com/hovren/crisp

Camera-to-IMU calibration and synchronization toolbox
https://github.com/hovren/crisp

Last synced: about 1 month ago
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Camera-to-IMU calibration and synchronization toolbox

• Host: GitHub
• URL: https://github.com/hovren/crisp
• Owner: hovren
• License: gpl-3.0
• Created: 2013-12-13T17:05:28.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2017-09-05T07:36:39.000Z (almost 7 years ago)
• Last Synced: 2024-03-28T08:06:00.478Z (3 months ago)
• Language: Python
• Size: 107 KB
• Stars: 197
• Watchers: 17
• Forks: 74
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION

Lists

• Awesome-SLAM - crisp - to-IMU calibration and synchronization toolbox (8. Tutorials / 8.5 Calibration)

README

        
# Camera-to-IMU calibration toolbox

This toolbox provides a python library to perform joint calibration of a rolling shutter camera-gyroscope system.

Given gyroscope and video data, this library can find the following parameters

* True gyroscope rate

* Time offset

* Rotation between camera and gyroscope coordinate frames

* Gyroscope measurement bias

If you use the package for your work, please cite the following paper

> Ovrén, H and Forssén, P.-E. "Gyroscope-based video stabilisation with auto-calibration." In 2015 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2090–2097). Seattle, WA

## Can I use these methods for my application?

The calibration methods in this package assumes the following

- Your camera is calibrated, including known readout time

- The camera frame rate is constant, and known

- The gyroscope frame rate is constant, and approximately known (within a few Hz, or percent)

If the video and gyroscope data are *not uniformly sampled*, but you have access

to somewhat reliable timestamps, then you can still use the method

if you resample the data to be uniform.

By "reliable" we mean timestamps without drift, and no (or negligble) jitter.

## Changes from 1.0

The 2.0 version of crisp features a new fully automatic calibrator.

This means that there is no compelling reason to use the semi-manual methods in the previous version of crisp.

Therefore the old example scripts have been removed, and the old functions are not imported into the module namespace.

No old functions have been removed, so if you want to use them they are still available in submodules.

## Installation

To use the package you need the following Python packages:

* NumPy

* SciPy

* OpenCV

* matplotlib

The easiest way is to install from PyPI:

    $ pip install crisp

If you want to build the package from source, you also need the *Cython* package.

To build and install the `crisp` module just run the following commands:

    $ python setup.py build

    $ python setup.py install

    

For a user-only installation add `--user` to the install command.

## Usage

The gyroscope and video data are first loaded into a stream object (`GyroStream`, and a subclass of `VideoStream` respectively).

To be able to understand how points are mapped from the real world to the image, the video stream also need a `CameraModel` (-subclass) instance.

    import crisp

    

    gyro = crisp.GyroStream.from_data(some_data_array)

    camera_model = crisp.AtanCameraModel(...) # One specific choice of camera model

    video = crisp.VideoStream.from_file(camera_model, video_file_path)

We then tie the streams together using a `AutoCalibrator` instance.

Since the calibration proces need to have estimates of the time offset and relative rotation,

these are first estimated using the `initialize()` member. This initialization only requires that

you give an approximate gyroscope sample rate (in Hz).

    calibrator = crisp.AutoCalibrator(video, gyro)

    calibrator.initialize(guessed_gyro_rate)

    result = calibrator.calibrate() # Dict of calibrated parameters

Initialization and calibration errors can be caught by handling `InitializationError` and `CalibrationError`.

### Example scripts

We bundle one example script `gopro_dataset_example.py` which shows how to use the 

library with the data in our dataset (http://www.cvl.isy.liu.se/research/datasets/gopro-gyro-dataset/).

This is the same dataset that was used to produce the above mentioned ICRA 2015 paper.

## Feedback

* For any questions regarding the method and paper, please send an e-mail to [email protected].

* For issues about the code, you are welcome to either use the tools (issue reporting, etc.) provided by GitHub, or send an e-mail.

## License

All code in this repository is licensed under the GPL version 3.