semvec/pypi-packages huggingface dataset:
droneposelib 0.0.0a4
A Python package for Visual-Inertial Odometry
License: MIT
Requires Python: >=3
Keywords: computer vision visual-inertial odometry IMU UAV

# 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.
ffmaxflow 0.0.9
Implementation of the  Fork Fulkerson method to find the max posible flow of a network
License: MIT
Keywords: max flow

This package implements the Fork Fulkerson algorithm to find the maximum posible flow in a network.

The Ford-Fulkerson method answers the question of given a network with vertices and edges between those vertices that have certain weights, how much "flow" can the network process at a time?


Change Log
==========

0.0.1 (17/03/21)
----------------
- First release

0.0.2 (18/03/21)
----------------
- Name change

0.0.3 (18/03/21)
----------------
- Better docstrings

0.0.4 (27/03/21)
----------------
- Trying out the long description

0.0.5 (21/04/21)
----------------
- Reimplementations and reinforcement

0.0.6 (24/04/21)
----------------
- Change in warning for loops

0.0.7 (26/04/21)
----------------
- Prints warnings instead of returning them.

0.0.8 (27/04/21)
----------------
- Adds self-explanatory messages in loop errors.

0.0.9 (27/04/21)
----------------
- Change in warning messages for repeated sink and source nodes.