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Top-Level Features

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  • Integration paths
  • Typical use cases
  • Guides and examples

VIO and SLAM

VIO (visual odometry) and SLAM (simultaneous localization and mapping) estimate camera trajectory while building and updating a map from synchronized stereo, IMU and other sensor data. OAK devices provide synchronized sensing and flexible software paths, so you can start with native DepthAI and scale into external SLAM ecosystems.

Integration paths

  1. Native in DepthAI v3: Use the RTab Map VIO SLAM example, built on the open-source RTAB-Map project and running natively in the DepthAI v3 library.
  2. ROS 2 stack: Integrate SLAM through the RTAB-Map package in ROS 2. We provide easy-to-run rtabmap_ros launch files for OAK, either as a standalone setup or as a drop-in replacement for RealSense cameras. Examples:
  3. Spectacular AI: Use the Spectacular AI OAK wrapper for real-time VIO/SLAM pipelines.
  4. NVIDIA stack: Build with PyCuVSLAM when targeting NVIDIA and Isaac workflows.

Typical use cases

  1. Robot localization: keep low-drift pose estimates where GNSS is unavailable or unreliable.
  2. Mapping and relocalization: map environments once, then relocalize reliably in repeat runs.
  3. Spatial autonomy: combine VIO pose with depth and AI for navigation and obstacle-aware behavior.

Guides and examples

DepthAI: RTab Map VIO SLAM

Native DepthAI v3 SLAM pipeline using stereo + IMU

DepthAI: Basalt VIO + RTAB-Map

Combine Basalt VIO with RTAB-Map SLAM in one pipeline

ROS2 RTAB-Map Integration

Visual SLAM with wheel odometry and OAK-D Pro

Spectacular AI OAK Wrapper

Use OAK cameras directly in Spectacular AI SDK workflows

NVIDIA PyCuVSLAM

Integrate OAK pipelines with cuVSLAM / Isaac workflows

ROS VIO and SLAM Docs

General ROS guidance for VIO and SLAM with OAK cameras

Need assistance?

Head over to Discussion Forum for technical support or any other questions you might have.