NVIDIA-ISAAC-ROS/isaac_ros_visual_slam

Health v2 · maintenance only

Adoption not part of the score

1473 stars · 206 forks observed · 2026-09-23

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Isaac ROS Visual SLAM is a ROS 2 package providing GPU-accelerated visual simultaneous localization and mapping (VSLAM) using stereo visual inertial odometry (SVIO), built on NVIDIA's cuVSLAM. It estimates real-time, low-latency robot odometry from stereo cameras and optionally an IMU, serving as an input to navigation on mobile robots and drones.

Use cases

• estimate robot odometry from stereo cameras in GPS-denied indoor environments
• provide localization for autonomous mobile robot navigation
• run real-time visual SLAM on NVIDIA Jetson embedded hardware
• fuse IMU data with stereo vision for robust visual-inertial odometry
• serve as the primary odometry source for drones
• integrate GPU-accelerated SLAM into an existing ROS 2 robotics stack

When to choose

• you need real-time, low-latency visual odometry with GPU acceleration on NVIDIA hardware
• your robot operates indoors or where GPS is unavailable or unreliable
• you are building a ROS 2-based robotics application with stereo cameras and optionally an IMU
• you deploy on NVIDIA Jetson embedded platforms and want optimized performance

When to avoid

• you do not have NVIDIA GPU or Jetson hardware, since acceleration depends on CUDA
• you need LiDAR-based SLAM rather than camera-based visual SLAM
• your environment lacks visual features and you have no IMU
• you are not using ROS 2 and cannot integrate with its middleware

Facets

library · maturity active

computer-vision machine-learning sdk robotics computer-vision autonomous-vehicles cpp visual-slam visual-odometry stereo-camera imu localization ros2 nvidia gpu-accelerated navigation perception linux jetson gpu

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markdown · JSON · MCP: product_card(name="NVIDIA-ISAAC-ROS/isaac_ros_visual_slam")