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qrb_ros_samples is a catalog of reference examples that wire QNN inference and — where applicable — zero‑copy transport into ready‑to‑run ROS 2 launch files. The repo is licensed under BSD‑3‑Clause. The main branch is active development; for stable releases track the jazzy-rel branch.
These are starting points and references — not the only supported way to use Qualcomm robotics platforms. The same primitives (the QNN delegate, qrb_ros_transport, and models from Qualcomm AI Hub) compose directly with stock ROS 2 / community packages. NPU Workflows is an example of that from‑scratch approach, targeting the same NPU.

AI samples

Vision and audio pipelines — depth, pose, segmentation, detection, speech recognition. Inference runs on the NPU via QNN.

Robotics samples

AMR motion, 2D lidar SLAM, Nav2, AprilTag, follow-me, pick-and-place. A mix of on-device and Gazebo simulation.

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Reading the support matrix

The upstream README does not define a legend for its support columns, and per‑sample READMEs sometimes disagree with the top‑level table. Here is how this page interprets the data:Every sample listed in this catalog is buildable from source on the Ubuntu on Qualcomm® IoT platforms image. Samples that are only available through the Qualcomm Linux / QIRP SDK toolchain are excluded.
An empty checkbox does not mean “unsupported” — only “unvalidated by upstream.” Before committing time to a sample on a given kit, check the sample’s own README (linked from the Sample column) for the authoritative per‑kit support list. Where the per‑sample README and this catalog disagree, trust the per‑sample README.

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AI samples

Most vision samples ingest from qrb_ros_camera (Qualcomm CSI/GMSL). To run these with a USB webcam instead, swap the camera node for v4l2_camera — see NPU Workflows for an end-to-end example.
Rows are ordered by ease of getting started — least additional hardware first.
Want to build an inference pipeline from scratch on the same primitives these samples wrap? See NPU Workflows for a hand‑rolled depth estimation example using the QNN TFLite delegate directly.

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Robotics samples

Rows are ordered by ease of getting started — simulation first, then camera-based, then vendor-specific peripherals.

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System requirements

Per upstream: Canonical Ubuntu for Qualcomm IoT Platforms. See Install Ubuntu on Qualcomm IoT Platforms and Install ROS Jazzy. For Qualcomm Linux targets, use the QIRP SDK instead.