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End-to-end robotics development on Qualcomm Dragonwing™ SBCs using ROS 2 Jazzy. This section has three tracks — pick the one that fits your goal.

Foundations

One-time ROS 2 Jazzy install on Ubuntu. Start here if this is a fresh SBC.

QRB ROS ecosystem

Zero-copy transport, NPU inference, reference samples, Gazebo sim, and benchmarking.

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Which track should I pick?

  • Just got a new board? Start with Software Setup.
  • Evaluating Qualcomm silicon for your robot? Read QRB ROS Overview for a tour of the packages — reference samples, zero-copy transport, generic NPU inference, Gazebo sim, benchmarking.
  • Want to learn how NPU inference wires together from scratch? The Depth Estimation on the NPU page is the hand-rolled example (same result as the packaged sample_depth_estimation, but built against the underlying primitives: QNN TFLite delegate, stock ROS 2, and an AI Hub model) so you can see every seam.
Why Qualcomm-specific packages? Stock community ROS 2 can’t reach the Hexagon HTP NPU — access to HTP requires the Qualcomm QNN delegate — and stock image_transport always serializes camera frames into a memcpy. QRB ROS Overview and qrb_ros_transport cover both in depth.

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QRB ROS ecosystem

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Hand-rolled example: Depth Estimation on the NPU

A from-scratch counterpart to the packaged sample_depth_estimation pipeline — same result, no QRB wrappers. Useful when you want to see every seam: camera ingest, TFLite preprocessing, the QNN delegate loading onto the HTP NPU, and the ROS 2 node glue. Uses a quantized TFLite model from Qualcomm AI Hub, the Qualcomm QNN TFLite delegate, and a camera publishing /image_raw.