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https://github.com/MichaelGrupp/evo

Python package for the evaluation of odometry and SLAM
https://github.com/MichaelGrupp/evo

benchmark euroc evaluation kitti mapping metrics odometry robotics ros ros2 slam trajectory trajectory-analysis trajectory-evaluation tum

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Python package for the evaluation of odometry and SLAM

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README

        

# evo

***Python package for the evaluation of odometry and SLAM***

| Linux / macOS / Windows / ROS / ROS2 |
| :---: |
| [![Build Status](https://dev.azure.com/michl2222/michl2222/_apis/build/status/MichaelGrupp.evo?branchName=master)](https://dev.azure.com/michl2222/michl2222/_build/latest?definitionId=1&branchName=master) |

This package provides executables and a small library for handling, evaluating and comparing the trajectory output of odometry and SLAM algorithms.

Supported trajectory formats:

* 'TUM' trajectory files
* 'KITTI' pose files
* 'EuRoC MAV' (.csv groundtruth and TUM trajectory file)
* ROS and ROS2 bagfile with `geometry_msgs/PoseStamped`, `geometry_msgs/TransformStamped`, `geometry_msgs/PoseWithCovarianceStamped`, `geometry_msgs/PointStamped` or `nav_msgs/Odometry` topics or [TF messages](https://github.com/MichaelGrupp/evo/wiki/Formats#bag---ros-bagfile)

See [here](https://github.com/MichaelGrupp/evo/wiki/Formats) for more infos about the formats.


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---

## Why?

evo has several advantages over other public benchmarking tools:

* common tools for different formats
* algorithmic options for association, alignment, scale adjustment for monocular SLAM etc.
* flexible options for output, [plotting](https://github.com/MichaelGrupp/evo/wiki/Plotting) or export (e.g. LaTeX plots or Excel tables)
* a powerful, configurable CLI that can cover many use cases
* modular `core` and `tools` libraries for custom extensions
* faster than other established Python-based tools ([see here](https://github.com/MichaelGrupp/evo/blob/master/doc/performance.md))

**What it's not:** a 1-to-1 re-implementation of a particular evaluation protocol tailored to a specific dataset.

---

## Installation / Upgrade

Installation is easy-peasy if you're familiar with this: https://xkcd.com/1987/#

evo supports **Python 3.8+**. The last evo version that supports **Python 2.7** is `1.12.0`.
You might also want to use a [virtual environment](https://github.com/MichaelGrupp/evo/blob/master/doc/install_in_virtualenv.md).

### From PyPi
If you just want to use the executables of the latest release version, the easiest way is to run:
```bash
pip install evo --upgrade --no-binary evo
```
This will download the package and its dependencies from [PyPI](https://pypi.org/project/evo/) and install or upgrade them. Depending on your OS, you might be able to use `pip2` or `pip3` to specify the Python version you want. Tab completion for Bash terminals is supported via the [argcomplete](https://github.com/kislyuk/argcomplete/) package on most UNIX systems - open a new shell after the installation to use it (without `--no-binary evo` the tab completion might not be installed properly). If you want, you can subscribe to new releases via https://libraries.io/pypi/evo.

### From Source
Run this in the repository's base folder:
```bash
pip install --editable . --upgrade --no-binary evo
```

### Dependencies

**Python packages**

evo has some required dependencies that are ***automatically resolved*** during installation with pip.
They are specified in the `install_requires` part of the `setup.py` file.

**PyQt5 (optional)**

PyQt5 will give you the enhanced GUI for plot figures from the "*Qt5Agg*" matplotlib backend (otherwise: "*TkAgg*"). If PyQt5 is already installed when installing this package, it will be used as a default (see `evo_config show`). To change the plot backend afterwards, run `evo_config set plot_backend Qt5Agg`.

**ROS (optional)**

Some ROS-related features require a ROS installation, see [here](http://www.ros.org/). We are testing this package with ROS Noetic and Iron. Previous versions (`<= 1.12.0`) work with Melodic, Kinetic and Indigo.

---

## Command Line Interface

After installation with setup.py or from pip, the following executables can be called globally from your command-line:

**Metrics:**

* `evo_ape` - absolute pose error
* `evo_rpe` - relative pose error

**Tools:**

* `evo_traj` - tool for analyzing, plotting or exporting one or more trajectories
* `evo_res` - tool for comparing one or multiple result files from `evo_ape` or `evo_rpe`
* `evo_fig` - (experimental) tool for re-opening serialized plots (saved with `--serialize_plot`)
* `evo_config` - tool for global settings and config file manipulation

Call the commands with `--help` to see the options, e.g. `evo_ape --help`. Tab-completion of command line parameters is available on UNIX-like systems.

**More documentation**
Check out the [Wiki on GitHub](https://github.com/MichaelGrupp/evo/wiki).

---

## Example Workflow

There are some example trajectories in the source folder in `test/data`.

### 1.) Plot multiple trajectories

Here, we plot two KITTI pose files and the ground truth using `evo_traj`:
```
cd test/data
evo_traj kitti KITTI_00_ORB.txt KITTI_00_SPTAM.txt --ref=KITTI_00_gt.txt -p --plot_mode=xz
```


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### 2.) Run a metric on trajectories

For example, here we calculate the absolute pose error for two trajectories from ORB-SLAM and S-PTAM using `evo_ape` (`KITTI_00_gt.txt` is the reference (ground truth)) and plot and save the individual results to .zip files for `evo_res`:

*First trajectory (ORB Stereo):*

```
mkdir results
evo_ape kitti KITTI_00_gt.txt KITTI_00_ORB.txt -va --plot --plot_mode xz --save_results results/ORB.zip
```


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*Second trajectory (S-PTAM):*

```
evo_ape kitti KITTI_00_gt.txt KITTI_00_SPTAM.txt -va --plot --plot_mode xz --save_results results/SPTAM.zip
```


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### 3.) Process multiple results from a metric

`evo_res` can be used to compare multiple result files from the metrics, i.e.:
* print infos and statistics (default)
* plot the results
* save the statistics in a table

Here, we use the results from above to generate a plot and a table:
```
evo_res results/*.zip -p --save_table results/table.csv
```


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---

## IPython / Jupyter Resources

For an interactive source code documentation, open the [Jupyter notebook](http://jupyter.readthedocs.io/en/latest/install.html) `metrics_tutorial.ipynb` in the `notebooks` folder of the repository. More infos on Jupyter notebooks: see [here](https://github.com/MichaelGrupp/evo/blob/master/doc/jupyter_notebook.md)

If you have IPython installed, you can launch an IPython shell with a custom evo profile with the command `evo_ipython`.

---

## Contributing Utilities

A few "inoffical" scripts for special use-cases are collected in the `contrib/` directory of the repository. They are inofficial in the sense that they don't ship with the package distribution and thus aren't regularly tested in continuous integration.

---

## Trouble
*":scream:, this piece of :shit: software doesn't do what I want!!1!1!!"*

**First aid:**
* append `-h`/ `--help` to your command
* check the [Wiki](https://github.com/MichaelGrupp/evo/wiki)
* check the [previous issues](https://github.com/MichaelGrupp/evo/issues?q=is%3Aissue+is%3Aclosed)
* open a [new issue](https://github.com/MichaelGrupp/evo/issues)

---

## Contributing

Patches are welcome, preferably as pull requests.

## License

[GPL-3.0 or later](https://www.gnu.org/licenses/gpl-3.0.html)

If you use this package for your research, a footnote with the link to this repository is appreciated: `github.com/MichaelGrupp/evo`.

...or, for citation with BibTeX:
```
@misc{grupp2017evo,
title={evo: Python package for the evaluation of odometry and SLAM.},
author={Grupp, Michael},
howpublished={\url{https://github.com/MichaelGrupp/evo}},
year={2017}
}
```