nvidia-isaac

    nvidia-isaac/cuVSLAM

    CUDA-accelerated visual odometry and SLAM library for fast, accurate, low-latency camera tracking

    developer-tools
    C++
    NOASSERTION
    1.9K stars
    209 forks
    1.9K GitHub watchers
    Updated Nov 4, 2026
    View on GitHub

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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    1 release in the last 90 days, the latest 22 days ago, with 9 contributors on GitHub.

    Peaked at #39 in the weekly ranking the week of Jan 18, its best in the past year.

    Use Cases & Benefits

    • Provides CUDA-accelerated visual odometry and SLAM for fast, accurate, low-latency camera tracking in robotics and computer vision.
    • Delivers real-time, computationally efficient performance leveraging GPU acceleration for high accuracy and low latency in visual tracking.
    • Use for developing monocular, stereo, and multi-camera visual odometry systems with live camera feeds or benchmark datasets.
    • Use for integrating visual SLAM capabilities into ROS2 robotic applications requiring precise localization and mapping.
    • Use for research and development of advanced visual tracking algorithms with support for distorted images, masking, and inertial data fusion.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    Other

    GitHub could not identify this license; read the LICENSE file before depending on it.

    Community files

    • README: present
    • Contributing guide: present

    Watch-outs

    • License not identified by GitHub; read the LICENSE file before depending on it.

    General guidance from repository metadata, not legal advice. How we score

    From the README

    PyCuVSLAM is the official Python wrapper for the NVIDIA cuVSLAM library, providing various Visual Tracking Camera modes and Simultaneous Localization and Mapping (SLAM) capabilities. Leveraging CUDA acceleration and a rich set of features, PyCuVSLAM delivers highly accurate, computationally efficient, real-time performance.

    Excerpt from the nvidia-isaac/cuVSLAM README on GitHub (NOASSERTION license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Forks

    209+19

    Open issues

    30+10

    Compared with peers in Developer Tools

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth

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