This repository is a fork of DSO, incorporating updates to utilize most recent libraries.
This version has been modified to serve as a baseline for Visual Simultaneous Localization and Mapping (VSLAM) experiments within the VSLAM-LAB environment.
Note: The current version of this repository only supports the radtan distortion model with 5 parameters. As a result, other capabilities such as using photometric calibration might not be working properly.
If you want to run DSO on your own data that is not contained in VSLAM-LAB, you can follow these steps.
To ensure all dependencies are properly installed, we suggest using mamba. If you haven't installed mamba yet, please download and set up miniforge, which is a more streamlined installer. This installer will create a "base" environment that includes the conda and mamba package managers.
If you already have a conda installation, you can add mamba by running:
conda install mamba -c conda-forge
Clone the repository:
git clone https://github.com/alejandrofontan/dso.git && cd dso
Create the environment:
mamba env create -f environment.yml
mamba activate dso
Build DSO:
python build.py
- Direct Sparse Odometry, J. Engel, V. Koltun, D. Cremers, In arXiv:1607.02565, 2016
- A Photometrically Calibrated Benchmark For Monocular Visual Odometry, J. Engel, V. Usenko, D. Cremers, In arXiv:1607.02555, 2016
DSO was developed at the Technical University of Munich and Intel. The open-source version is licensed under the GNU General Public License Version 3 (GPLv3). See http://vision.in.tum.de/dso for details.