cuVSLAM: CUDA-Accelerated Visual Odometry and Mapping

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:

Mode	Required sensors	Optional sensors	Mode-specific settings	When to use
Mono	1 camera	—	—	Single-camera tracking; cheapest setup but scale-ambiguous.
RGBD	1 RGB-D camera (aligned RGB + depth)	—	RGBDSettings	Single depth-aligned camera (e.g. RealSense, TUM RGB-D).
Multicamera	≥2 cameras with at least one overlapping pair (a stereo pair)	up to 32 cameras total	—	Stereo or multi-stereo rigs. Most accurate purely-visual mode.
Inertial	1 stereo pair + 1 IMU	—	—	Stereo VIO. Adds robustness to brief visual failures.
Multisensor	At least one camera pair with overlapping frustums; cuNLS-enabled build	RGB-D cameras (any subset), 0 or 1 IMU	MultisensorSettings	Any-mix RGB / RGB-D rigs with optional IMU. Use when the legacy modes don't fit: ≥3 cameras of mixed RGB and RGB-D types; a multi-camera rig where only a subset provides depth; or RGB-D + IMU fusion (note that Inertial mode is stereo-only).

Notes:

• Multisensor is the only mode that requires a cuNLS-enabled build — see Optional: 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.

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

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:

Ubuntu	Python	CUDA	Architectures
22.04	3.10	12, 13	x86_64, aarch64
24.04+	3.12+	12, 13	x86_64, aarch64

Prerequisite: CUDA Toolkit 12 or 13 must be installed separately (not included in the wheels).

To install (virtual environment is recommended):

1. Go to the releases page.
2. Download the wheel matching your CUDA version (cu12 or cu13), Python version, and platform (x86_64 or aarch64).
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++ libraries are available on the releases page for Ubuntu 22.04/24.04 on x86_64 and Jetson(aarch64) with CUDA 12 and CUDA 13.

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

2. 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:

      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	Purpose
USE_CUDA	ON	CUDA acceleration
USE_CUNLS	ON	cuNLS (CUDA nonlinear least squares); requires USE_CUDA
USE_LMDB	ON	LMDB map database
USE_RERUN	OFF	Rerun SDK visualization
USE_NVTX	OFF	NVIDIA NVTX profiling
TREAT_WARNINGS_AS_ERRORS	OFF	Strict warning policy

Build types: Release (default), Debug, RelWithDebInfo, MinSizeRel. Do not mix types in the same build directory.

cuNLS

cuNLS (CUDA nonlinear least squares) is enabled by default (USE_CUNLS=ON) and is built from source via FetchContent, like the other external dependencies — no separate install or path is required. The source is pinned in cmake/ext/cunls.cmake; cuNLS downloads its own dependencies (spdlog is shared with cuVSLAM, cuDSS is fetched as a prebuilt archive) at configure time, so a network connection and the CUDA Toolkit are the only prerequisites. The resulting static archive is bundled into libcuvslam.

USE_CUDA must be ON. To build without cuNLS (and disable the multisensor odometry mode that depends on it), configure with -DUSE_CUNLS=OFF.

Build on Jetson (aarch64)

JetPack only ships CUDA runtime libraries by default. Install the dev packages on the Jetson before building:

# JetPack 6.x (CUDA 12)
sudo apt-get install libcublas-dev-12-6 libcusolver-dev-12-6
# JetPack 7.x (CUDA 13)
sudo apt-get install libcublas-dev-13-0 libcusolver-dev-13-0

For building natively, follow the same steps as Build on local x86. For building remotely via SSH:

./copy_to_remote.sh <jetson-host>
ssh <jetson-host> 'export CUVSLAM_SRC_DIR=~/cuvslam/src CUVSLAM_DST_DIR=~/cuvslam/build && ~/cuvslam/src/build_release.sh [options]'
./copy_from_remote.sh <jetson-host>

To speed up the build, target your specific GPU architecture instead of building for all, e.g.:

./build_release.sh --cuda_arch=87   # 87 for Orin Nano/NX/AGX

Omit --cuda_arch to build for all architectures (default). To detect your GPU's SM version:

nvidia-smi --query-gpu=compute_cap --format=csv,noheader   # e.g. 8.7 -> use 87

Enable rerun visualizer for C++ code

1. Create virtual environment and install rerun SDK:

   python3 -m venv .venv
   source .venv/bin/activate
   pip install rerun-sdk==0.33.1

2. Specify virtual env with CUVSLAM_TOOLS_PYENV environment variable (defaults to .venv in repository root).
3. Update build_release.sh and set USE_RERUN to ON
4. Run any tool from tools folder

C++ unit tests disable Rerun by default, even in builds configured with USE_RERUN=ON. To enable the viewer for interactive test debugging, ensure rerun is on PATH and set RERUN=1:

cd <build-dir>
RERUN=1 ctest --output-on-failure

FAQ

Q: What Python versions are supported by PyCuVSLAM?

A: Pre-built wheels are available for Python 3.10 (Ubuntu 22.04) and Python 3.12 or later (Ubuntu 24.04+). When built from source, PyCuVSLAM supports Python 3.9 and later.

Troubleshooting

See TROUBLESHOOTING.md

Feedback

Are you having problems running cuVSLAM or PyCuVSLAM? Do you have any suggestions? We'd love to hear your feedback in the issues tab.

License

This project is licensed under the NVIDIA Community License, for details refer to the LICENSE file.

Citation

If you find this work useful in your research, please consider citing:

@article{korovko2025cuvslam,
      title={cuVSLAM: CUDA accelerated visual odometry and mapping},
      author={Alexander Korovko and Dmitry Slepichev and Alexander Efitorov and Aigul Dzhumamuratova and Viktor Kuznetsov and Hesam Rabeti and Joydeep Biswas and Soha Pouya},
      year={2025},
      eprint={2506.04359},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2506.04359},
}