Colmap-Odometry

Colmap-Odometry is a visual odometry framework based on pycolmap and is mainly intended for the development and testing of new VO/SLAM features (deep-learning based tie points and matching, keyframe selection, global optimization, etc).

Feel free to join the project!

Key fratures:

• completly build on pycolmap, few dependency and easy to install
• windowed bundle adjustement
• monocular camera supported
• stereo cameras supported
• multi-camera supported (requires partial FOV overlap)
• build for long sequences
• deep learning based features support

Installation

For installing colmap-odometry, we recommend using uv for fast and reliable package management:

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create and activate a virtual environment
uv venv --python 3.10
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Then, you can install colmap-odometry using uv:

uv pip install -e .

This command will install the package in editable mode, allowing you to modify the source code and see changes immediately without needing to reinstall. If you want to use colmap-slam as a non-editable library, you can also install it without the -e flag.

Check that cuda is available in pytorch, or manually install pytorch from https://pytorch.org/ to have GPU support.

Running the code

python ./main.py -c ./config/config_carla.yaml -a ./calibration/calibration_carla.yaml -r ./calibration/camera_rig_carla.yaml -i ./assets/sample_carla_dataset -w ./assets/output

The full trajectory is stored in the output folder: trajectory.txt in world reference system or images.txt following COLMAP conventions.

If you want to run on the euroc dataset you can download the data from https://projects.asl.ethz.ch/datasets/euroc-mav/ and run:

python ./main.py -c ./config/config_euroc.yaml -a ./calibration/calibration_euroc.yaml -r ./calibration/camera_rig_euroc.yaml -i path/to/image/folder -w ./path/to/output/folder

Reference

@article{morelli2025deep,
  title={Deep Learning in Visual Odometry for Autonomous Driving},
  author={Morelli, Luca and Tryba{\l}a, Pawe{\l} and Razzino, Armando Vittorio and Remondino, Fabio},
  journal={The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
  volume={48},
  pages={93--99},
  year={2025},
  publisher={Copernicus GmbH}
}

@article{morelli2023colmap,
  title={COLMAP-SLAM: A framework for visual odometry},
  author={Morelli, Luca and Ioli, Francesco and Beber, Raniero and Menna, Fabio and Remondino, Fabio and Vitti, Alfonso},
  journal={The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
  volume={48},
  pages={317--324},
  year={2023},
  publisher={Copernicus Publications G{\"o}ttingen, Germany}
}