# nvidia-isaac/cuVSLAM

cuVSLAM: CUDA-Accelerated Visual Odometry and Mapping

Repository: https://github.com/nvidia-isaac/cuVSLAM
Canonical: https://ross.abutalabs.com/products/cuvslam
Homepage: https://nvidia-isaac.github.io/cuVSLAM/
Language: C++
License: NOASSERTION
License Family: other
Last push: 2026-08-26T18:20:55+00:00

## Health v2 (maintenance only)
Score: 82/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 82, longevity 44
- inputs: {"age_days": 622, "days_push": 7, "days_rel": 41, "gap_med": 67.0, "n_releases_24m": 3}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1773, forks 196 (observed 2026-08-28T04:05:34.293607+00:00)

## What it is
cuVSLAM is NVIDIA's CUDA-accelerated library for real-time visual odometry and simultaneous localization and mapping (SLAM). It supports multiple tracking modes including mono, RGB-D, multicamera, stereo-inertial, and multisensor configurations, with Python, C++, and ROS2 APIs.

## Use cases
- real-time visual odometry for robots
- SLAM for autonomous navigation
- stereo camera tracking
- RGB-D camera odometry
- visual-inertial odometry with IMU fusion
- multi-camera tracking rigs
- robotics localization and mapping

## When to choose
- you need GPU-accelerated real-time SLAM on NVIDIA hardware
- you want a library that works out of the box without parameter tuning
- you need support for diverse sensor configurations (mono, stereo, RGB-D, IMU)
- you're building robotics applications within the NVIDIA Isaac ecosystem

## When to avoid
- you don't have NVIDIA CUDA-capable GPU hardware
- you need CPU-only SLAM solutions
- you require a permissively licensed library (license is custom/NVIDIA)
- you need a fully open-source modifiable implementation

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, simulation, graphics
- domain: robotics, computer-vision, autonomous-vehicles, gpu-computing
- platform: cpp, python
- tags: slam, visual-odometry, cuda, mapping, vio, stereo-vision, nvidia-isaac, real-time, linux, gpu, ros

## Member repositories
- nvidia-isaac/cuVSLAM (main) score 82

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:34.293607+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:25:56.132271+00:00, confidence not recorded.
  - readme: https://github.com/nvidia-isaac/cuVSLAM (fetched 2026-08-28T04:05:34.293607+00:00, sha 108111716d58)
  - homepage: https://nvidia-isaac.github.io/cuVSLAM/ (fetched 2026-08-29T11:04:02.542095+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.