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ACKNOWLEDGMENTS.md

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Acknowledgments

This project includes reference implementations and draws inspiration from the following open-source projects:

Boost.Graph

GTSAM (Georgia Tech Smoothing and Mapping Library)

  • Repository: https://github.com/borglab/gtsam
  • License: BSD 3-Clause License
  • Copyright: Copyright (c) 2010, Georgia Tech Research Corporation, Atlanta, Georgia 30332-0415
  • Description: A C++ library that implements smoothing and mapping (SAM) in robotics and vision, using Factor Graphs and Bayes Networks as the underlying computing paradigm
  • Note: GTSAM is a foundational library that inspired the factor graph design and optimization implementation in this project

Easy-Factor-Graph

ensmallen

  • Repository: https://github.com/mlpack/ensmallen
  • License: BSD 3-Clause License
  • Copyright: Copyright 2008-2023, mlpack contributors
  • Description: A header-only C++ library for numerical optimization

minisam

  • License: BSD 3-Clause License
  • Copyright: Copyright (c) 2019, Jing Dong
  • Description: A minimal SLAM framework derived from GTSAM

minisam_lib

  • Repository: https://github.com/shaolinbit/minisam_lib
  • License: Free for research; derived from GTSAM (BSD simplified license)
  • Description: A minimal SLAM library based on GTSAM
  • Note: For commercial uses, please contact the authors

We gratefully acknowledge these projects and their contributors for their valuable work, which has served as reference and inspiration for the development of Graphix. Special thanks to the Boost.Graph library for providing foundational graph algorithms and data structures, and to the GTSAM team at Georgia Tech for creating such a foundational library in the robotics and computer vision community.