cuVSLAM

cuVSLAM: CUDA-Accelerated Visual Odometry and Mapping

Latest release | ArXiv paper | Python API | C++ API | ROS2

Overview

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

cuVSLAM works out of the box. All internal algorithms use carefully engineered defaults or adapt automatically to the environment and motion profile — no parameter tuning is required or expected. To get started, provide two things: accurate camera calibration (intrinsics and extrinsics) and a sensor configuration (which tracking mode matches your rig). Everything else is handled internally.

Table of Contents

• Tracking modes
• Using cuVSLAM
• Performance
• Install PyCuVSLAM
• Build cuVSLAM
• FAQ
• Development
• Feedback
• License
• Citation

Tracking modes

cuVSLAM's tracker supports several odometry modes selected via Odometry::Config::odometry_mode (C++) / cuvslam.Odometry.OdometryMode (Python). Modes differ in which sensors they require, which ones they can additionally fuse, and how they handle scale. Pick the mode that matches the most informative sensor set on your rig:

Notes:

• Multisensor is the only mode that requires a cuNLS-enabled build — see cuNLS. All other modes work with the default build.
• IMU fusion is available in Inertial (always on) and Multisensor (auto-enabled when Rig::imus is non-empty).
• For a runnable Multisensor walkthrough on a multi-RGB-D + IMU rig, see examples/multisensor/. For the full per-field API reference, see the C++ or Python docs.

Multisensor mode

Multisensor tracking is experimental: tracking may be inaccurate or fail for some sensor configurations and scenes. The current implementation supports pinhole cameras only.

Using cuVSLAM

The quickest way to get started is to install PyCuVSLAM from a pre-built wheel and explore the examples.

ROS2 Support

To use cuVSLAM in a ROS2 environment:

• Isaac ROS cuVSLAM GitHub
• Isaac ROS cuVSLAM Documentation
• Isaac ROS cuVSLAM User Manual

cuVSLAM Documentation

• cuVSLAM Technical Report
• PyCuVSLAM API Documentation
• cuVSLAM C++ API Documentation
• Version-matched offline API documentation is available as cuvslam-docs-<version>.tar.gz on the latest release. Extract it and open python/index.html or cpp/index.html.

Performance

cuVSLAM is a highly optimized visual tracking library validated across numerous public datasets and popular robotic camera setups. For detailed benchmarking and validation results, please refer to our technical report.

The accuracy and robustness of cuVSLAM can be influenced by several factors. If you experience performance issues, please check your system against these common causes:

• Hardware Overload: Hardware overload can negatively impact visual tracking, resulting in dropped frames or insufficient computational resources for cuVSLAM. Disable intensive visualization or image-saving operations to improve performance. For expected performance metrics on Jetson embedded platforms, see our technical report

• Intrinsic and Extrinsic Calibration: Accurate camera calibration is crucial. Ensure your calibration parameters are precise. For more details, refer to our guide on image undistortion. If you're new to calibration, consider working with experienced vendors

• Synchronization and Timestamps: Accurate synchronization significantly impacts cuVSLAM performance. Make sure multi-camera images are captured simultaneously—ideally through hardware synchronization—and verify correct relative timestamps across cameras. Refer to our multi-camera hardware assembly guide for building a rig with synchronized RealSense cameras

• Frame Rate: Frame rate significantly affects performance. The ideal frame rate depends on translational and rotational velocities. Typically, 30 FPS is suitable for most "human-speed" motions. Adjust accordingly for faster movements

• Resolution: Image resolution matters. VGA resolution or higher is recommended. cuVSLAM efficiently handles relatively high-resolution images due to CUDA acceleration

• Image Quality: Ensure good image quality by using suitable lenses, correct exposure, and proper white balance to avoid clipping large image regions. For significant distortion or external objects within the camera's field of view, please refer to our guide on static masking

• Motion Blur: Excessive motion blur can negatively impact tracking. Ensure that exposure times are short enough to minimize motion blur. If avoiding motion blur isn't feasible, consider increasing the frame rate or try the following Mono-Depth or Stereo Inertial tracking modes

See Troubleshooting

Install PyCuVSLAM

PyCuVSLAM is the Python wrapper (bindings) for the cuVSLAM library.

Install from Wheels

Pre-built wheels are available on the cuVSLAM releases page for the following configurations:

Only the combinations listed above are provided as pre-built wheels. The cp312-abi3 wheels use Python's stable ABI and are compatible with Python 3.12 and later. Other Python, CUDA, or Jetson combinations require an installation from source.

Prerequisite: CUDA Toolkit 12 or 13 must be installed separately (not included in the wheels). Its major version must match the wheel's cu12 or cu13 tag.

Official wheels include cuNLS support for Multisensor mode; no separate cuNLS installation is required.

To install (virtual environment is recommended):

1. Go to the latest release.
2. Download the wheel matching the table above. On Jetson, also match the device family: Orin uses cu12/cp310; Thor uses cu13/cp312-abi3.
3. Install with pip:

pip install cuvslam-*.whl

If a pre-built wheel is not available for your system, see Install from Source below.

Install from Source

Note: cuVSLAM must be built before installing from source.

To install PyCuVSLAM from repository (virtual environment is recommended):

CUVSLAM_BUILD_DIR=<path-to-cuvslam-build> pip install python/

CUVSLAM_BUILD_DIR is required for build script to find libcuvslam.so. Warning: Due to scikit-build-core limitations, bindings must be reinstalled after rebuilding libcuvslam.

Build cuVSLAM

Pre-built Libraries

Pre-built C++ SDK archives are available on the latest release:

The desktop/server row represents four archives covering both Ubuntu versions with both listed CUDA versions. Select the archive matching your target, Ubuntu version, and CUDA version. Each archive contains bin/libcuvslam.so, bin/cuvslam_api_launcher, and the public headers under include/cuvslam/.

For Python usage, pre-built wheels are the recommended approach.

Building from Source

Requirements

• Ubuntu 22+ (22.04 & 24.04 tested) x86_64/aarch64 (Desktop/Laptop & Nvidia Jetson Orin/Thor)
• CUDA Toolkit 12 or 13, Jetpack 6.1/6.2/7.0/7.1
• apt update && apt install g++ cmake git git-lfs python3-dev
  • git + git-lfs to clone this repository
  • CMake 3.19+, gcc
  • Python 3.9+ (for python bindings, examples and some tools)

Build on local x86

In the repository root build C++ code using one of two ways:

1. Build manually:

cmake -S . -B build
cmake --build build --parallel $(nproc)

With CMake 4.x, add -DCMAKE_POLICY_VERSION_MINIMUM=3.5 during configure if an external dependency reports that compatibility with CMake < 3.5 has been removed:

cmake -S . -B build -DCMAKE_POLICY_VERSION_MINIMUM=3.5

1. Set source & build paths for build_release.sh and run it.

   Important: Before running build_release.sh set paths using one of the options:

   1. Set paths in ~/.bashrc (or equivalent shell login script), which will be useful when switching between cuvslam branches:
      bash复制

      export CUVSLAM_SRC_DIR=<path-to-cuvslam-src>
      export CUVSLAM_DST_DIR=<path-to-cuvslam-build>

   2. Update SRC & DST paths in build_release.sh

CMake options

All flags have defaults; override with -DFLAG=VALUE.

| Flag | Default