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Thi repository implements the Distributed Gauss-Seidel algorithm for pose graph optimization in addition to the Pairwise Consistency Maximization for outlier rejection.

If you use this work, please cite any of the following publications:

For the distributed PGO implementation cite:

@inproceedings{Choudhary16icra,
  author    = {Siddharth Choudhary and
               Luca Carlone and
	       Carlos Nieto and
	       John Rogers and
               Henrik I. Christensen and
               Frank Dellaert},
  title     = {Distributed Trajectory Estimation with Privacy and Communication Constraints: 
a Two-Stage Distributed Gauss-Seidel Approach},
  booktitle = {IEEE International Conference on Robotics and Automation 2016},
  year      = {2016}
}
@article{Choudhary17arXiv,
  author    = {Siddharth Choudhary and
               Luca Carlone and
               Carlos Nieto{-}Granda and
               John G. Rogers III and
               Henrik I. Christensen and
               Frank Dellaert},
  title     = {Distributed Mapping with Privacy and Communication Constraints: Lightweight
               Algorithms and Object-based Models},
  journal   = {CoRR},
  volume    = {abs/1702.03435},
  year      = {2017},
  url       = {http://arxiv.org/abs/1702.03435},
}

For the outlier rejection implementation cite:

@inproceedings{mangelsonPairwiseConsistentMeasurement2018,
  title = {Pairwise {{Consistent Measurement Set Maximization}} for {{Robust Multi-Robot Map Merging}}},
  booktitle = {2018 {{IEEE International Conference}} on {{Robotics}} and {{Automation}} ({{ICRA}})},
  author = {Mangelson, Joshua G. and Dominic, Derrick and Eustice, Ryan M. and Vasudevan, Ram},
  year = {2018},
  month = may,
  pages = {2916--2923},
  issn = {2577-087X},
  doi = {10.1109/ICRA.2018.8460217}
}

@article{lajoieDOORSLAMDistributedOnline2020,
  title = {{{DOOR}}-{{SLAM}}: {{Distributed}}, {{Online}}, and {{Outlier Resilient SLAM}} for {{Robotic Teams}}},
  shorttitle = {{{DOOR}}-{{SLAM}}},
  author = {Lajoie, Pierre-Yves and Ramtoula, Benjamin and Chang, Yun and Carlone, Luca and Beltrame, Giovanni},
  year = {2020},
  month = apr,
  volume = {5},
  pages = {1656--1663},
  issn = {2377-3766},
  doi = {10.1109/LRA.2020.2967681},
  number = {2}
}

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This library is an implementation of the algorithm described in Distributed Trajectory Estimation with Privacy and Communication Constraints: a Two-Stage Distributed Gauss-Seidel Approach.

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  • C++ 89.4%
  • MATLAB 8.3%
  • CMake 1.8%
  • Dockerfile 0.5%