| Name | evalio JSON |
| Version |
0.4.1
JSON |
| download |
| home_page | None |
| Summary | Evaluate Lidar-Inertial Odometry on public datasets |
| upload_time | 2025-10-26 01:24:18 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | Copyright (c) 2025 Easton Potokar
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
lidar
odometry
evaluation
inertial
dataset
robotics
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
## evalio
evalio is a tool for **Eval**uating **L**idar-**I**nertial **O**dometry.
Specifically, it provides a common interface for connecting LIO datasets and LIO pipelines. This allows for easy addition of new datasets and pipelines, as well as a common location to evaluate them making benchmarks significantly easier to run. It features,
- No ROS dependency! (though it can still load rosbag datasets using the wonderful [rosbags](https://ternaris.gitlab.io/rosbags/) package)
- Easy to add new datasets and pipelines, see the [example](https://github.com/contagon/evalio-example)
- Unified representation of lidar scan, e.g. row (scan-line) major order, stamped at the start of the scan, point stamps are relative from the start of the scan.
- Download and manage datasets via the CLI interface
- Simple to use API for friction-free access to data
- Run pipelines via the CLI interface and yaml config files
- Compute statistics for resulting trajectory runs
## Installation
evalio is available on PyPi (with all pipelines compiled in!), so simply install via your favorite python package manager,
```bash
uv add evalio # uv
pip install evalio # pip
```
## Basic Usage
evalio can be used both as a python library and as a CLI for both datasets and pipelines. We cover just the tip of the iceberg here, so please check out the [docs](https://contagon.github.io/evalio/) for more information.
### Datasets
Once evalio is installed, datasets can be listed and downloaded via the CLI interface. For example, to list all datasets and then download a sequence from the hilti-2022 dataset,
```bash
evalio ls datasets
evalio download hilti_2022/basement_2
```
Once downloaded, a trajectory can then be easily used in python,
```python
from evalio import datasets as ds
# for all data
for mm in ds.Hilti2022.basement_2:
print(mm)
# for lidars
for scan in ds.Hilti2022.basement_2.lidar():
print(scan)
# for imu
for imu in ds.Hilti2022.basement_2.imu():
print(imu)
```
### Pipelines
The other half of evalio is the pipelines that can be run on various datasets. All pipelines and their parameters can be shown via,
```bash
evalio ls pipelines
```
For example, to run KissICP on a dataset,
```bash
evalio run -o results -d hilti_2022/basement_2 -p kiss
```
This will run the pipeline on the dataset and save the results to the `results` folder. The results can then be used to compute statistics on the trajectory,
```bash
evalio stats results
```
More complex experiments can be run, including varying pipeline parameters, via specifying a config file,
```yaml
output_dir: ./results/
datasets:
# Run on all of hilti trajectories
- hilti_2022/*
# Run on first 1000 scans of multi campus
- name: multi_campus/ntu_day_01
length: 1000
pipelines:
# Run vanilla kiss with default parameters
- kiss
# Tweak kiss parameters
- name: kiss_tweaked
pipeline: kiss
deskew: true
# Sweep over voxel size parameter
sweep:
voxel_size: [0.1, 0.5, 1.0]
```
This can then be run via
```bash
evalio run -c config.yml
```
## Contributing
Contributions are always welcome! Feel free to open an issue, pull request, etc. The documentation has a more details on developing new datasets and pipelines.
## Citation
If you use evalio in your research, please cite the following paper,
```bibtex
@misc{potokar2025_evaluation_lidar_odometry,
title={A Comprehensive Evaluation of LiDAR Odometry Techniques},
author={Easton Potokar and Michael Kaess},
year={2025},
eprint={2507.16000},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2507.16000},
}
```
Raw data
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"description": "## evalio\n\nevalio is a tool for **Eval**uating **L**idar-**I**nertial **O**dometry.\n\nSpecifically, it provides a common interface for connecting LIO datasets and LIO pipelines. This allows for easy addition of new datasets and pipelines, as well as a common location to evaluate them making benchmarks significantly easier to run. It features,\n- No ROS dependency! (though it can still load rosbag datasets using the wonderful [rosbags](https://ternaris.gitlab.io/rosbags/) package)\n- Easy to add new datasets and pipelines, see the [example](https://github.com/contagon/evalio-example)\n- Unified representation of lidar scan, e.g. row (scan-line) major order, stamped at the start of the scan, point stamps are relative from the start of the scan.\n- Download and manage datasets via the CLI interface\n- Simple to use API for friction-free access to data\n- Run pipelines via the CLI interface and yaml config files\n- Compute statistics for resulting trajectory runs\n\n## Installation\n\nevalio is available on PyPi (with all pipelines compiled in!), so simply install via your favorite python package manager,\n```bash\nuv add evalio # uv\npip install evalio # pip\n```\n\n## Basic Usage\n\nevalio can be used both as a python library and as a CLI for both datasets and pipelines. We cover just the tip of the iceberg here, so please check out the [docs](https://contagon.github.io/evalio/) for more information.\n\n### Datasets\n\nOnce evalio is installed, datasets can be listed and downloaded via the CLI interface. For example, to list all datasets and then download a sequence from the hilti-2022 dataset,\n```bash\nevalio ls datasets\nevalio download hilti_2022/basement_2\n```\n\nOnce downloaded, a trajectory can then be easily used in python,\n```python\nfrom evalio import datasets as ds\n\n# for all data\nfor mm in ds.Hilti2022.basement_2:\n print(mm)\n\n# for lidars\nfor scan in ds.Hilti2022.basement_2.lidar():\n print(scan)\n\n# for imu\nfor imu in ds.Hilti2022.basement_2.imu():\n print(imu)\n```\n\n### Pipelines\n\nThe other half of evalio is the pipelines that can be run on various datasets. All pipelines and their parameters can be shown via,\n```bash\nevalio ls pipelines\n```\nFor example, to run KissICP on a dataset,\n```bash\nevalio run -o results -d hilti_2022/basement_2 -p kiss\n```\nThis will run the pipeline on the dataset and save the results to the `results` folder. The results can then be used to compute statistics on the trajectory,\n```bash\nevalio stats results\n```\n\nMore complex experiments can be run, including varying pipeline parameters, via specifying a config file,\n```yaml\noutput_dir: ./results/\n\ndatasets:\n # Run on all of hilti trajectories\n - hilti_2022/*\n # Run on first 1000 scans of multi campus\n - name: multi_campus/ntu_day_01\n length: 1000\n\npipelines:\n # Run vanilla kiss with default parameters\n - kiss\n # Tweak kiss parameters\n - name: kiss_tweaked\n pipeline: kiss\n deskew: true\n # Sweep over voxel size parameter\n sweep:\n voxel_size: [0.1, 0.5, 1.0]\n \n```\nThis can then be run via\n```bash\nevalio run -c config.yml\n```\n\n## Contributing\n\nContributions are always welcome! Feel free to open an issue, pull request, etc. The documentation has a more details on developing new datasets and pipelines.\n\n## Citation\n\nIf you use evalio in your research, please cite the following paper,\n```bibtex\n@misc{potokar2025_evaluation_lidar_odometry,\n title={A Comprehensive Evaluation of LiDAR Odometry Techniques}, \n author={Easton Potokar and Michael Kaess},\n year={2025},\n eprint={2507.16000},\n archivePrefix={arXiv},\n primaryClass={cs.RO},\n url={https://arxiv.org/abs/2507.16000}, \n}\n```",
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"license": "Copyright (c) 2025 Easton Potokar\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.",
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