https://github.com/borglab/gtsam

GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.
https://github.com/borglab/gtsam

Last synced: about 2 months ago
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GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.

• Host: GitHub
• URL: https://github.com/borglab/gtsam
• Owner: borglab
• License: other
• Created: 2017-03-27T17:12:06.000Z (about 7 years ago)
• Default Branch: develop
• Last Pushed: 2024-01-07T18:31:58.000Z (6 months ago)
• Last Synced: 2024-01-07T19:23:36.385Z (6 months ago)
• Language: C++
• Homepage: http://gtsam.org
• Size: 69.9 MB
• Stars: 2,271
• Watchers: 61
• Forks: 713
• Open Issues: 92
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• Awesome-SLAM - GTSAM
• awesome-robotics-libraries - [github
• awesome-weekly-robotics - GTSAM - a BSD-licensed C++ library that implements sensor fusion for robotics and computer vision applications, including SLAM (Simultaneous Localization and Mapping), VO (Visual Odometry), and SFM (Structure from Motion). It uses factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices to optimize for the most probable configuration or an optimal plan. Licence: BSD. (Libraries and Frameworks / Autopilots)
• awesome-mobile-robotics - GTSAM
• awesome-robotics-projects - borglab/gtsam - Georgia Tech Smoothing and Mapping Library (Library, Framework, SDK / ML & Vision)
• my-awesome-stars - borglab/gtsam - GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices. (C++)
• awesome-stars - borglab/gtsam - GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matr (C++)