Metadata-Version: 2.1
Name: droneposelib
Version: 0.0.0a4
Summary: A Python package for Visual-Inertial Odometry
Home-page: https://github.com/marcusvaltonen/python-droneposelib
Author: Marcus Valtonen Ornhag
Author-email: marcusvaltonen@gmail.com
License: MIT
Description: # droneposelib
        
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        Python library for Visual-Inertial Odometry (VIO).
        Wrapps the C++/Eigen library [DronePoseLib](https://github.com/marcusvaltonen/DronePoseLib).
        
        ## Solvers available
        The current list of solvers are the following:
        
        | Solver  | Approx. runtime\* | Max. solutions | Comment |
        | --- | :---: | :---: | --- |
        | `fEf` | 2.5 us | 4 | Valtonen Örnhag et al. (ArXiV 2021) |
        | `frEfr` | 23\*\* us | 11 | Valtonen Örnhag et al. (ArXiV 2021) |
        | `rEr` | 2.2 us | 4 | Valtonen Örnhag et al. (ArXiV 2021)\*\*\* |
        
        \* Measured on a laptop with an Intel(R) Core(TM) i5-6200U CPU @ 2.30GHz
        
        \*\* If using the `use_fast_solver` option
        
        \*\*\* Undocumented
        
        Note that all solvers assume that all three degrees of freedom for the rotation matrices are available,
        i.e. potential IMU drift is ignored. By using e.g. magnetometer data one can often get good estimates,
        if fused with accelerometer data and gyroscope data. This approach is e.g. used in the Madgwick filter.
        Futhermore, we have succesfully used pre-integration techniques to achieve robust and accurate performance
        for shorter time frames without the need of magnetometer data.
        
        ## Installation
        A pre-alpha release is available at PyPi, and can be installed using
        ```console
            $ pip install droneposelib
        ```
        You may also compile the source code, see "Development".
        
        ## Examples
        See the `example` directory for examples on how to use the solvers.
        
        ## Development
        You are more than welcome to contribute your our other relevant solvers. More info soon.
        
        ## References
        The code is related to the ArXiV paper [[link](https://arxiv.org/abs/2103.08286)]:
        
        ```
        @misc{valtonenornhag-etal-2021-arxiv,
              title={Trust Your IMU: Consequences of Ignoring the IMU Drift},
              author={Marcus {Valtonen~Örnhag} and Patrik Persson and Mårten Wadenbäck and Kalle Åström and Anders Heyden},
              year={2021},
              eprint={2103.08286},
              archivePrefix={arXiv},
              primaryClass={cs.CV}
        }
        ```
        
        Please cite the paper if you are using the code for (academic) publications.
        
Keywords: computer vision visual-inertial odometry IMU UAV
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Image Processing
Requires-Python: >=3
Description-Content-Type: text/markdown
Provides-Extra: dev
