evalio is a tool for Evaluating Lidar-Inertial Odometry.

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 package)
• Easy to add new datasets and pipelines, see the 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

evalio is available on PyPi (with all pipelines compiled in!), so simply install via your favorite python package manager,

uv add evalio      # uv
pip install evalio # pip

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 for more information.

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,

evalio ls datasets
evalio download hilti_2022/basement_2

Once downloaded, a trajectory can then be easily used in 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)

The other half of evalio is the pipelines that can be run on various datasets. All pipelines and their parameters can be shown via,

evalio ls pipelines

For example, to run KissICP on a dataset,

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,

evalio stats results

More complex experiments can be run, including varying pipeline parameters, via specifying a config file,

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

evalio run -c config.yml

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.

If you use evalio in your research, please cite the following paper,

@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}, 
}